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Author SHA1 Message Date
Sydney RunkleandGitHub a529b9bede chore(langgraph): bump version 1.1.8 -> 1.1.9 (#7563)
## Summary

- Bumps `langgraph` patch version from `1.1.8` to `1.1.9` in
`libs/langgraph/pyproject.toml`

## Test plan

- [ ] Verify version string is correct in `pyproject.toml`
- [ ] Confirm release workflow triggers on merge
2026-04-21 09:38:58 -04:00
0a26b471d3 fix(langgraph): don't propagate ReplayState to subgraphs on plain resume (#7561)
**Description:**
When clients resume an interrupted subgraph with `Command(resume=...)`
plus an explicit `checkpoint_id` in the config (the pattern LangGraph
Studio and the API server emit on every resume), the subgraph restarts
from its first node instead of continuing at the interrupted node.

Fix: gate `ReplayState` propagation on `is_time_traveling` rather than
`is_replaying`, so a resume that happens to carry a head checkpoint_id
behaves the same as one with just a thread_id.

**Verification:** added regression test
`test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id` (fails
on main, passes with fix, across memory/sqlite/sqlite_aes).
Full `test_time_travel.py`, `test_time_travel_async.py`,
`test_interruption.py`, and all pregel subgraph/interrupt/resume/replay
tests still pass.

Co-authored-by: Jessie Ibarra <jessie.ibarra@langgraph.dev>
2026-04-20 21:29:18 -04:00
Eugene YurtsevandGitHub b674dd4622 feat(prebuilt): expose available tools on ToolRuntime (#7512) 2026-04-17 16:54:16 -04:00
Eugene YurtsevandGitHub 8df0a377d0 chore(langgraph): undo unnecessary changes in stream handler (#7536)
This change is no longer necessary as langchain-core will continue
copying checkpoint_ns for backwards compatibility
2026-04-17 16:22:34 -04:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
216cf33a54 chore(deps): bump the pip group across 3 directories with 1 update (#7537)
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs/deps/zuper_deps directory:
[langchain-openai](https://github.com/langchain-ai/langchain).
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs_fail directory:
[langchain-openai](https://github.com/langchain-ai/langchain).
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs directory:
[langchain-openai](https://github.com/langchain-ai/langchain).

Updates `langchain-openai` from 1.0.1 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
</details>
<br />

Updates `langchain-openai` from 1.0.0a2 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
</details>
<br />

Updates `langchain-openai` from 1.0.0a2 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
</details>
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Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-04-17 12:42:55 -07:00
Eugene YurtsevandGitHub 4956134a37 release(langgraph): 1.1.8 (#7545)
releasing 1.1.8
2026-04-17 19:41:13 +00:00
aa94790f36 fix(langgraph): remove strict add_handler type check that breaks OTel instrumentation (#7544)
## Summary

Removes the `add_handler()` overrides on `_GraphCallbackManager` and
`_AsyncGraphCallbackManager` that reject handlers not inheriting from
`GraphCallbackHandler`. This fixes a regression in 1.1.7 where
`opentelemetry-instrumentation-langchain` (and likely other libraries
that patch `BaseCallbackManager.__init__`) crash with `TypeError:
handlers must inherit GraphCallbackHandler` at invocation time.

## Why this is safe

The strict type check is redundant — `_configure_graph_callbacks` and
`_filter_graph_handlers` already filter handlers to
`GraphCallbackHandler` instances at construction time. Non-graph
handlers that enter via external patches (like OTel's monkey-patch) are
harmless because `handle_event("on_interrupt", ...)` /
`handle_event("on_resume", ...)` will simply no-op on handlers that
don't implement those methods.

## What changed

- Deleted `add_handler()` override from `_GraphCallbackManager` (was
lines 248-255)
- Deleted `add_handler()` override from `_AsyncGraphCallbackManager`
(was lines 324-331)
- No other changes — 18 lines removed, 0 added

## Test plan

- [x] All 8 existing `test_graph_callbacks.py` tests pass (`make test
TEST=tests/test_graph_callbacks.py`)
- [x] `make lint` passes
- [x] `make format` passes (no changes needed)
- [x] Verified fix locally: `LangchainInstrumentor().instrument()` +
`create_react_agent()` + `graph.ainvoke()` no longer raises `TypeError`
- [x] Verified the graph lifecycle callbacks (`on_interrupt`,
`on_resume`) still work correctly

Closes #7543

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2026-04-17 15:33:10 -04:00
ccurmeandGitHub e002711ede release(prebuilt): 1.0.10 (#7541) 2026-04-17 13:52:20 -04:00
f44b49b33d chore: dedup warnings (#7257)
Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
2026-04-17 10:13:03 -07:00
a0a95df2ac release(cli): 0.4.23 (#7542)
Release Note: Increase the max bound for langgraph-api

Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
2026-04-17 16:38:14 +00:00
Eugene YurtsevandGitHub d194c18c06 release(langgraph): 1.1.7 (#7540)
release langgraph 1.1.7
2026-04-17 15:19:18 +00:00
Sydney Runkle eae916719f CLI bump 2026-04-16 13:48:02 -04:00
f093702e4e fix(prebuilt): handle injected NotRequired keys (#7392)
Resolves https://github.com/langchain-ai/langchain/issues/35585

This would previously raise KeyError:
```python
from typing import Annotated

from langchain_core.tools import tool
from langchain.agents import create_agent
from typing_extensions import NotRequired
from langgraph.prebuilt import InjectedState
from langchain.agents import AgentState


class CustomAgentState(AgentState):
    city: NotRequired[str]


@tool
def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
    """Get weather for a given city."""
    if city is None:
        city = "Boston"
    return f"It's always sunny in {city}!"


agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
    state_schema=CustomAgentState,
)

input_message = {
    "role": "user",
    "content": "What's the weather?",
}

result = agent.invoke({"messages": [input_message]})
for m in result["messages"]:
    m.pretty_print()
```

---------

Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
2026-04-16 13:06:33 +00:00
Sydney RunkleandGitHub 51cbdbd5cd fix: time travel when going back to interrupt node (#7498)
# Fix: Create fork checkpoint on subgraph time travel

## Problem

When time-traveling to a subgraph checkpoint that has an interrupt, and
then resuming, the resume would load the **wrong state** — it would pick
up the original execution's latest checkpoint instead of the
time-traveled one.

This happened because replaying from a subgraph checkpoint never created
a new parent checkpoint. If the replay hit an interrupt before
`after_tick()` ran, no checkpoint was written at all, so the parent's
"latest" checkpoint was still the old one from the original execution.

## Fix

When the loop detects a time-travel replay (not an `update_state` fork),
it now **eagerly writes a fork checkpoint** at the start of the tick.
This ensures:

1. The parent thread's latest checkpoint points to the replayed state
2. Subsequent `Command(resume=...)` calls find the correct checkpoint
3. Stale `INTERRUPT` pending writes from the old checkpoint are cleared
(they reference old task IDs)

Additionally, the subgraph replay logic now uses the **parent checkpoint
ID** (from `prev_checkpoint_config`) when resolving subgraph checkpoints
during time-travel, matching the existing behavior for `update_state`
forks.

## Checkpoint flow diagrams

### Before fix: time travel leaves no fork

```
Original execution:

  C0 (start) --> C1 (step_a) --> C2 (ask_1 interrupt) --> C3 (resume) --> C4 (ask_2 interrupt) --> C5 (done)

Time travel to C2 (subgraph config):

  Replay runs... hits interrupt... no new checkpoint written.
  Parent "latest" is still C5.

  Command(resume="new_answer"):
    Loads C5 (wrong!) instead of the replayed C2 state.
```

### After fix: time travel creates a fork

```
Original execution:

  C0 --> C1 --> C2 --> C3 --> C4 --> C5 (done)

Time travel to C2 (subgraph config):

  C0 --> C1 --> C2 --> C3 --> C4 --> C5
                  \
                   F1 (fork, source="fork")  <-- new latest

  Command(resume="new_answer"):
    Loads F1 (correct!) --> resumes from the right state.

  After full resume:

  C0 --> C1 --> C2 --> C3 --> C4 --> C5
                  \
                   F1 --> F2 (ask_1 result) --> F3 (ask_2 interrupt) --> F4 (done)
```

### Manual fork via `update_state` (unchanged)

```
  C0 --> C1 --> C2 --> C3
                  \
                   U1 (source="update")  <-- created by update_state()

  This path already worked. The fix skips update/fork sources
  so existing behavior is preserved.
```

## Changes

- **`libs/langgraph/langgraph/pregel/_loop.py`**:
- Extract `is_time_traveling` flag from the existing replay detection
logic for reuse
- Write a fork checkpoint (`source="fork"`) eagerly at the start of a
time-travel tick, before execution begins
- Clear stale `INTERRUPT` pending writes when creating the fork (they
reference old task IDs that won't match the new checkpoint)
- Unify subgraph replay ID resolution: check `source in ("update",
"fork")` instead of a separate `is_time_traveling` condition, since the
new fork checkpoint now has `source="fork"`
- **`libs/langgraph/tests/test_time_travel.py`** and
**`test_time_travel_async.py`**: Added 4 new test cases (sync + async):
- `test_replay_from_before_interrupt_then_resume` — replays from a
checkpoint before an interrupt, resumes with a new answer, and verifies
the full checkpoint history (source, next, values) at each stage
- `test_subgraph_time_travel_resume_from_first_interrupt` — time-travels
to a subgraph's first interrupt, resumes both interrupts with new
answers, and verifies the fork creates a new branch while preserving the
original
- `test_subgraph_time_travel_resume_from_second_interrupt` —
time-travels to a subgraph's second interrupt, resumes with a new
answer, and verifies the first interrupt's original answer is preserved
- `test_subgraph_time_travel_checkpoint_pattern` — verifies the fork
checkpoint branches from the correct replay point and that the full
checkpoint tree is correct after resume
- **`libs/langgraph/tests/test_pregel.py`** /
**`test_pregel_async.py`**: Updated existing
`test_weather_subgraph_state` to account for the new fork checkpoint
appearing in history (history length increases by 1)
2026-04-16 08:29:48 -04:00
hari-dhanushkodiandGitHub 4d64227c13 chore: start tracking cli deploy source (#7520)
Fixes #

<!-- Replace everything above this line with a 1-2 sentence description
of your change. Keep the "Fixes #xx" keyword and update the issue
number. -->

Read the full contributing guidelines:
https://docs.langchain.com/oss/python/contributing/overview

> **All contributions must be in English.** See the [language
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If you paste a large clearly AI generated description here your PR may
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Thank you for contributing to LangGraph! Follow these steps to have your
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1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION

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- If this PR depends on another PR being merged first, please include
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2026-04-16 08:24:47 -04:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
6bcac5d72e chore(deps): bump langsmith from 0.6.4 to 0.7.31 in /libs/prebuilt (#7530)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.6.4 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.6.4...v0.7.31">compare
view</a></li>
</ul>
</details>
<br />


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6177c4311b chore(deps): bump langsmith from 0.6.4 to 0.7.31 in /libs/checkpoint (#7525)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.6.4 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
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chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
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<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
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303769904b chore(deps): bump langsmith from 0.7.20 to 0.7.31 in /libs/sdk-py (#7528)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.7.20 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
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<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.20...v0.7.31">compare
view</a></li>
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cee7dcd523 chore(deps): bump langsmith from 0.7.26 to 0.7.31 in /libs/cli (#7529)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
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<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.26...v0.7.31">compare
view</a></li>
</ul>
</details>
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3413723e5a chore(deps): bump langsmith from 0.6.4 to 0.7.31 in /libs/checkpoint-postgres (#7527)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.6.4 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
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view</a></li>
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07252d2cda chore(deps): bump langsmith from 0.6.4 to 0.7.31 in /libs/langgraph (#7526)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.6.4 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
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</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.6.4...v0.7.31">compare
view</a></li>
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47fd42abb2 chore(deps): bump langsmith from 0.6.4 to 0.7.31 in /libs/checkpoint-sqlite (#7524)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
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href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
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<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.6.4...v0.7.31">compare
view</a></li>
</ul>
</details>
<br />


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554b2db1f8 chore(deps): bump langsmith from 0.7.3 to 0.7.31 in /libs/checkpoint-conformance (#7523)
Bumps [langsmith](https://github.com/langchain-ai/langsmith-sdk) from
0.7.3 to 0.7.31.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.3...v0.7.31">compare
view</a></li>
</ul>
</details>
<br />


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93a144b404 chore(deps): bump the uv group across 2 directories with 1 update (#7531)
Bumps the uv group with 1 update in the /libs/cli/uv-examples/monorepo
directory: [langsmith](https://github.com/langchain-ai/langsmith-sdk).
Bumps the uv group with 1 update in the /libs/cli/uv-examples/simple
directory: [langsmith](https://github.com/langchain-ai/langsmith-sdk).

Updates `langsmith` from 0.7.26 to 0.7.31
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.26...v0.7.31">compare
view</a></li>
</ul>
</details>
<br />

Updates `langsmith` from 0.7.26 to 0.7.31
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langsmith-sdk/releases">langsmith's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.31</h2>
<h2>What's Changed</h2>
<ul>
<li>chore(deps-dev): bump langchain-core from 1.2.23 to 1.2.28 in
/python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2692">langchain-ai/langsmith-sdk#2692</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.82.0 to
0.84.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2684">langchain-ai/langsmith-sdk#2684</a></li>
<li>chore(deps): bump cryptography from 46.0.6 to 46.0.7 in /python by
<a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2693">langchain-ai/langsmith-sdk#2693</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.84.0 to
0.85.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2700">langchain-ai/langsmith-sdk#2700</a></li>
<li>feat(py): Tag OpenAI Agent Python SDK runs with ls_agent_type by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2699">langchain-ai/langsmith-sdk#2699</a></li>
<li>feat(js): Adds ls_agent_type metadata to AI SDK runs by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2701">langchain-ai/langsmith-sdk#2701</a></li>
<li>chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to
4.67.3.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2710">langchain-ai/langsmith-sdk#2710</a></li>
<li>chore(deps): bump pnpm/action-setup from 5 to 6 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2705">langchain-ai/langsmith-sdk#2705</a></li>
<li>chore(deps): bump the py-minor-and-patch group across 1 directory
with 10 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2711">langchain-ai/langsmith-sdk#2711</a></li>
<li>chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2702">langchain-ai/langsmith-sdk#2702</a></li>
<li>chore(deps): bump actions/github-script from 8 to 9 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2706">langchain-ai/langsmith-sdk#2706</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 7 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2712">langchain-ai/langsmith-sdk#2712</a></li>
<li>chore(deps-dev): bump types-psutil from 7.2.2.20260130 to
7.2.2.20260408 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2709">langchain-ai/langsmith-sdk#2709</a></li>
<li>chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2708">langchain-ai/langsmith-sdk#2708</a></li>
<li>feat: Filter kwargs from new token events by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2714">langchain-ai/langsmith-sdk#2714</a></li>
<li>release(py): 0.7.31 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2716">langchain-ai/langsmith-sdk#2716</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.30...v0.7.31</a></p>
<h2>v0.7.30</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(python): add service feature to sandbox by <a
href="https://github.com/DanielKneipp"><code>@​DanielKneipp</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2665">langchain-ai/langsmith-sdk#2665</a></li>
<li>fix(js): Fix prototype pollution bug in anonymizers by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2690">langchain-ai/langsmith-sdk#2690</a></li>
<li>release(js): 0.5.18 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2691">langchain-ai/langsmith-sdk#2691</a></li>
<li>chore(js/sandbox): suppress warning log by <a
href="https://github.com/hntrl"><code>@​hntrl</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2694">langchain-ai/langsmith-sdk#2694</a></li>
<li>feat(js): Add metadata to Claude Agent SDK JS tracing by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2695">langchain-ai/langsmith-sdk#2695</a></li>
<li>fix(py): Fix run tree memory leak by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2696">langchain-ai/langsmith-sdk#2696</a></li>
<li>release(py): 0.7.30 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2698">langchain-ai/langsmith-sdk#2698</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.29...v0.7.30</a></p>
<h2>v0.7.29</h2>
<h2>What's Changed</h2>
<ul>
<li>release(js): 0.5.17 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2681">langchain-ai/langsmith-sdk#2681</a></li>
<li>feat(py): Fix race condition around Claude Agent SDK instrumentation
by <a href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a>
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2685">langchain-ai/langsmith-sdk#2685</a></li>
<li>release(py): 0.7.29 by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2686">langchain-ai/langsmith-sdk#2686</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29">https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.28...v0.7.29</a></p>
<h2>v0.7.28</h2>
<h2>What's Changed</h2>
<ul>
<li>feat(py): Support subagent tracing in Claude Agents SDK, fix usage
and duplicate messages by <a
href="https://github.com/jacoblee93"><code>@​jacoblee93</code></a> in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2670">langchain-ai/langsmith-sdk#2670</a></li>
<li>chore(deps-dev): bump the py-minor-and-patch group across 1
directory with 11 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2677">langchain-ai/langsmith-sdk#2677</a></li>
<li>chore(deps-dev): bump the js-minor-and-patch group across 1
directory with 8 updates by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2667">langchain-ai/langsmith-sdk#2667</a></li>
<li>chore(deps): bump pnpm/action-setup from 4 to 5 by <a
href="https://github.com/dependabot"><code>@​dependabot</code></a>[bot]
in <a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/pull/2658">langchain-ai/langsmith-sdk#2658</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/c434999d05c00334efeba88b8bbd2de9f3afbef6"><code>c434999</code></a>
release(py): 0.7.31 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2716">#2716</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/47d7c4a783333e716395d802e7632f1f1b4744d3"><code>47d7c4a</code></a>
feat: Filter kwargs from new token events (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2714">#2714</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/3c57445b543c9a2f86db52024ea2c998bfc2ffab"><code>3c57445</code></a>
chore(deps-dev): bump rich from 14.3.3 to 15.0.0 in /python (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2708">#2708</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/2be6cd01a2b6e35e811488d3561e7b0b57b06f63"><code>2be6cd0</code></a>
chore(deps-dev): bump types-psutil from 7.2.2.20260130 to 7.2.2.20260408
in /...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/b8b6ca32d43c919c07a4e13c99a83bcaab8accb0"><code>b8b6ca3</code></a>
chore(deps-dev): bump the js-minor-and-patch group across 1 directory
with 7 ...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9897cb33da7698291637f268edd833ca3e1adde6"><code>9897cb3</code></a>
chore(deps): bump actions/github-script from 8 to 9 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2706">#2706</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/572c0184285747e027a796e03ea6c9ba171e09a6"><code>572c018</code></a>
chore(deps-dev): bump <code>@​anthropic-ai/sdk</code> from 0.85.0 to
0.86.0 in /js (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2702">#2702</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/57447524c88b6bba2775161aa449da32fb8e5c42"><code>5744752</code></a>
chore(deps): bump the py-minor-and-patch group across 1 directory with
10 upd...</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/960cae7f490e9ccbe428e6b56c8047bdb7b942a5"><code>960cae7</code></a>
chore(deps): bump pnpm/action-setup from 5 to 6 (<a
href="https://redirect.github.com/langchain-ai/langsmith-sdk/issues/2705">#2705</a>)</li>
<li><a
href="https://github.com/langchain-ai/langsmith-sdk/commit/9370e7670abf7f8f9a36fbb72250bcfd2f91e7c6"><code>9370e76</code></a>
chore(deps-dev): bump types-tqdm from 4.67.3.20260303 to 4.67.3.20260408
in /...</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langsmith-sdk/compare/v0.7.26...v0.7.31">compare
view</a></li>
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2f4611db8f chore(deps): bump langsmith from 0.5.18 to 0.5.20 in /libs/cli/js-examples (#7522)
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20c5d36efe chore(deps): bump langsmith from 0.5.18 to 0.5.20 in /libs/cli/js-monorepo-example (#7521)
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25470ea435 release(checkpoint): 4.0.2 (#7518)
Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
2026-04-15 20:52:57 +00:00
60 changed files with 2628 additions and 8430 deletions
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]
[[package]]
name = "zstandard"
version = "0.25.0"
@@ -46,6 +46,23 @@ LC_REVIVER = Reviver()
EMPTY_BYTES = b""
logger = logging.getLogger(__name__)
# Dedup log warnings across process lifetime; cap bounds state if types are
# dynamically generated (also acts as a circuit breaker on warning volume).
# Dedup is best-effort: racing threads may each emit once for the same key,
# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
_MAX_WARNED_TYPES = 1000
_warned_unregistered_types: set[tuple[str, str]] = set()
_warned_blocked_types: set[tuple[str, str]] = set()
def _warn_once(
seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
) -> None:
if key in seen or len(seen) >= _MAX_WARNED_TYPES:
return
seen.add(key)
logger.warning(msg, *args)
class JsonPlusSerializer(SerializerProtocol):
"""Serializer that uses ormsgpack, with optional fallbacks.
@@ -534,7 +551,9 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
logger.warning(
_warn_once(
_warned_unregistered_types,
key,
"Deserializing unregistered type %s.%s from checkpoint. "
"This will be blocked in a future version. "
"Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
@@ -556,7 +575,9 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
logger.warning(
_warn_once(
_warned_blocked_types,
key,
"Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
"Add to allowed_msgpack_modules to allow: [(%r, %r)]",
module,
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-checkpoint"
version = "4.0.1"
version = "4.0.2"
description = "Library with base interfaces for LangGraph checkpoint savers."
authors = []
requires-python = ">=3.10"
+9
View File
@@ -29,6 +29,8 @@ from langgraph.checkpoint.serde.jsonplus import (
EXT_METHOD_SINGLE_ARG,
JsonPlusSerializer,
_msgpack_enc,
_warned_blocked_types,
_warned_unregistered_types,
)
@@ -102,6 +104,13 @@ def test_msgpack_method_pathlib_blocked_encrypted_strict(
class TestEncryptedSerializerMsgpackAllowlist:
"""Test msgpack allowlist behavior through EncryptedSerializer."""
@pytest.fixture(autouse=True)
def _reset_warned_types(self) -> None:
# Warning dedup state is process-global; reset per-test so each case
# sees a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
def test_safe_types_no_warning(self, caplog: pytest.LogCaptureFixture) -> None:
"""Test safe types deserialize without warnings through encryption."""
serde = _make_encrypted_serde()
+16 -2
View File
@@ -35,6 +35,8 @@ from langgraph.checkpoint.serde.jsonplus import (
JsonPlusSerializer,
_msgpack_enc,
_msgpack_ext_hook_to_json,
_warned_blocked_types,
_warned_unregistered_types,
)
from langgraph.store.base import Item
@@ -580,6 +582,14 @@ def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None
assert result is not None
@pytest.fixture(autouse=True)
def _reset_warned_types() -> None:
# Warning dedup state is process-global; reset per-test so each case sees
# a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
"""Pydantic models not in allowlist should log warning but still deserialize."""
current = _lg_msgpack.STRICT_MSGPACK_ENABLED
@@ -595,6 +605,12 @@ def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) ->
assert "unregistered type" in caplog.text.lower()
assert "allowed_msgpack_modules" in caplog.text
assert result == obj
# Second deserialization of the same type should NOT produce another warning
caplog.clear()
result2 = serde.loads_typed(dumped)
assert "unregistered type" not in caplog.text.lower()
assert result2 == obj
_lg_msgpack.STRICT_MSGPACK_ENABLED = current
@@ -639,7 +655,6 @@ def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) ->
def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
"""allowed_msgpack_modules=None should block unregistered types."""
serde = JsonPlusSerializer(allowed_msgpack_modules=None)
obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
@@ -657,7 +672,6 @@ def test_msgpack_allowlist_blocks_non_listed(
caplog: pytest.LogCaptureFixture,
) -> None:
"""Allowlists should block unregistered types even if msgpack is enabled."""
serde = JsonPlusSerializer(
allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
)
+13 -1
View File
@@ -12,13 +12,25 @@ from langgraph.checkpoint.base import (
empty_checkpoint,
)
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
from langgraph.checkpoint.serde.jsonplus import (
JsonPlusSerializer,
_warned_blocked_types,
_warned_unregistered_types,
)
class MemoryPydantic(BaseModel):
foo: str
@pytest.fixture(autouse=True)
def _reset_warned_types() -> None:
# Warning dedup state is process-global; reset per-test so each case sees
# a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
class TestMemorySaver:
@pytest.fixture(autouse=True)
def setup(self) -> None:
+123 -4
View File
@@ -286,7 +286,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.1"
version = "4.0.2"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -369,7 +369,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.6.4"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
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name = "zstandard"
version = "0.25.0"
@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.1"
"langchain-openai==1.1.14"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.0a2",
"langchain-openai==1.1.14",
"langchain-anthropic==1.0.0a5",
"langgraph==1.1.5"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.0a2",
"langchain-openai==1.1.14",
"langgraph==1.1.2",
"langchain_community>=0.3.0",
]
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View File
@@ -3676,9 +3676,9 @@ keyv@^4.5.4:
json-buffer "3.0.1"
"langsmith@>=0.5.0 <1.0.0":
version "0.5.18"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
version "0.5.20"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
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p-queue "6.6.2"
uuid "10.0.0"
+3 -3
View File
@@ -1328,9 +1328,9 @@ keyv@^4.5.4:
json-buffer "3.0.1"
"langsmith@>=0.5.0 <1.0.0":
version "0.5.18"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
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version "0.5.20"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
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uuid "10.0.0"
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@@ -1 +1 @@
__version__ = "0.4.21"
__version__ = "0.4.23"
+10 -3
View File
@@ -26,8 +26,15 @@ class LogData(TypedDict):
params: dict[str, Any]
def get_anonymized_params(kwargs: dict[str, Any]) -> dict[str, bool]:
params = {}
def get_anonymized_params(
kwargs: dict[str, Any], *, cli_command: str
) -> dict[str, bool | str]:
params: dict[str, bool | str] = {}
if cli_command == "deploy" and (
analytics_source := os.getenv("LANGGRAPH_CLI_ANALYTICS_SOURCE")
):
params["source"] = analytics_source
# anonymize params with values
if config := kwargs.get("config"):
@@ -88,7 +95,7 @@ def log_command(func):
"python_version": platform.python_version(),
"cli_version": __version__,
"cli_command": func.__name__,
"params": get_anonymized_params(kwargs),
"params": get_anonymized_params(kwargs, cli_command=func.__name__),
}
background_thread = threading.Thread(target=log_data, args=(data,))
+1 -1
View File
@@ -23,7 +23,7 @@ dependencies = [
path = "langgraph_cli/__init__.py"
[project.optional-dependencies]
inmem = [
"langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
"langgraph-api>=0.5.35,<0.9.0 ; python_version >= '3.11'",
"langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
]
+3 -3
View File
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name = "langsmith"
version = "0.7.26"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
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{ name = "xxhash" },
{ name = "zstandard" },
]
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name = "langsmith"
version = "0.7.26"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -279,9 +279,9 @@ dependencies = [
{ name = "xxhash" },
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[[package]]
+465 -383
View File
File diff suppressed because it is too large Load Diff
@@ -66,9 +66,6 @@ CONFIG_KEY_RUNTIME = sys.intern("__pregel_runtime")
# holds a `Runtime` instance with context, store, stream writer, etc.
CONFIG_KEY_RESUME_MAP = sys.intern("__pregel_resume_map")
# holds a mapping of task ns -> resume value for resuming tasks
CONFIG_KEY_STREAM_MESSAGES_V2 = sys.intern("__pregel_stream_messages_v2")
# when True, attach StreamMessagesHandlerV2 so content-block (v2) events
# flow through stream_mode="messages"; set by GraphStreamer only.
# --- Other constants ---
PUSH = sys.intern("__pregel_push")
@@ -110,7 +107,6 @@ RESERVED = {
CONFIG_KEY_CHECKPOINT_ID,
CONFIG_KEY_CHECKPOINT_NS,
CONFIG_KEY_RESUME_MAP,
CONFIG_KEY_STREAM_MESSAGES_V2,
# other constants
PUSH,
PULL,
-18
View File
@@ -245,15 +245,6 @@ class _GraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
@@ -321,15 +312,6 @@ class _AsyncGraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
-41
View File
@@ -1,7 +1,5 @@
import asyncio
import sys
from collections.abc import Callable
from contextvars import ContextVar
from typing import Any
from langchain_core.runnables import RunnableConfig
@@ -11,18 +9,6 @@ from langgraph.store.base import BaseStore
from langgraph._internal._constants import CONF, CONFIG_KEY_RUNTIME
from langgraph.types import StreamWriter
_tool_call_writer: ContextVar[Callable[[Any], None] | None] = ContextVar(
"langgraph_tool_call_writer", default=None
)
"""ContextVar holding the writer for the currently-executing tool call.
Set by `StreamToolCallHandler.on_tool_start` and reset on end/error.
Defined here (rather than alongside the handler in `pregel/_tools.py`)
so `emit_tool_output_delta` can import it without triggering the
pregel import chain user tool code does
`from langgraph.config import emit_tool_output_delta` at import time.
"""
def _no_op_stream_writer(c: Any) -> None:
pass
@@ -208,30 +194,3 @@ def get_stream_writer() -> StreamWriter:
"""
runtime = get_config()[CONF][CONFIG_KEY_RUNTIME]
return runtime.stream_writer
def emit_tool_output_delta(delta: Any) -> None:
"""Emit a `tool-output-delta` event onto the `tools` stream mode.
Must be called from inside a tool's execution scope (sync or async).
While a tool is running, `StreamToolCallHandler.on_tool_start` sets a
writer closure on a ContextVar keyed to that call's `tool_call_id`
and namespace; this helper reads the ContextVar and forwards `delta`
through it.
When called outside any tool call, or when the graph was not
streamed with `"tools"` in `stream_mode`, this is a silent no-op
tool authors can leave `emit_tool_output_delta` calls in place
without gating them on stream mode.
Args:
delta: The partial output chunk to stream. Shape is up to the
caller strings are the common case, but any JSON-
serializable value is accepted and surfaced as-is on the
`tools` channel's `tool-output-delta` payload under
`"delta"`.
"""
writer = _tool_call_writer.get()
if writer is None:
return
writer(delta)
-7
View File
@@ -1045,7 +1045,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
interrupt_after: All | list[str] | None = None,
debug: bool = False,
name: str | None = None,
transformers: Sequence[Callable[..., Any]] | None = None,
) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
"""Compiles the `StateGraph` into a `CompiledStateGraph` object.
@@ -1078,11 +1077,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
interrupt_after: An optional list of node names to interrupt after.
debug: A flag indicating whether to enable debug mode.
name: The name to use for the compiled graph.
transformers: Optional sequence of zero-arg factories returning
`StreamTransformer` instances. Registered on the compiled
graph and instantiated per-run whenever `stream_v2` /
`astream_v2` is called. Appended after the built-in
`ValuesTransformer` and `MessagesTransformer`.
Returns:
CompiledStateGraph: The compiled `StateGraph`.
@@ -1165,7 +1159,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
store=store,
cache=cache,
name=name or "LangGraph",
stream_transformers=transformers,
)
compiled._serde_allowlist = serde_allowlist
@@ -1,274 +0,0 @@
from __future__ import annotations
from collections.abc import AsyncIterator, Callable, Iterator
from typing import Any, TypeVar, cast
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langgraph._internal._constants import NS_SEP
from langgraph.errors import GraphInterrupt
from langgraph.pregel.protocol import StreamChunk
try:
from langchain_core.tracers._streaming import _StreamingCallbackHandler
except ImportError:
_StreamingCallbackHandler = object # type: ignore[assignment,misc]
T = TypeVar("T")
_LANGGRAPH_SENTINEL_NODES = frozenset({"__start__", "__end__"})
def _is_nested_pregel_start(
name: str | None,
metadata: dict[str, Any] | None,
parent_run_id: UUID | None,
task_run_ids: set[UUID],
) -> bool:
"""Recognize a nested `Pregel` invocation from its `on_chain_start` metadata.
When a compiled graph is added as a node, pregel fires two
`on_chain_start` callbacks at that task: first for the node chain
(whose `name` matches `metadata["langgraph_node"]`) and second for
the inner `Pregel` chain (whose `name` is the graph's `name`, not
the node name). Both share the same `langgraph_checkpoint_ns`.
Primary signal: a `langgraph_checkpoint_ns` is set AND `name`
differs from the owning task's `langgraph_node`. This covers the
common case where the compiled subgraph's name differs from the
node name it was registered under.
Fallback for name collisions (subgraph compiled with
`name == node_name`): the inner `Pregel` start's `parent_run_id`
is the run_id of the node chain's start event, which the handler
records in `task_run_ids` on the first start. Matching
`parent_run_id` to that set identifies the second start as the
nested `Pregel` even when names coincide.
Regular node chains are skipped; the root `Pregel` (which has no
`langgraph_node` metadata) isn't observed by this handler because
the root's start fires before the handler is attached.
Metadata-based detection is used because `on_chain_start`'s
`serialized` argument is `None` for compiled graphs in this
version of langchain-core, so class-based detection via
`serialized["id"]` isn't available.
Sentinel nodes (`__start__` / `__end__`) are excluded: conditional
edges from `START` fire an `on_chain_start` with `lg_node=__start__`
and the router function's name as `name`, which would otherwise
match the discriminator without representing an actual nested
`Pregel`.
Args:
name: The `name` kwarg from `on_chain_start`.
metadata: The `metadata` kwarg from `on_chain_start`.
parent_run_id: The `parent_run_id` kwarg from `on_chain_start`.
task_run_ids: The set of run_ids the handler has already seen
as node-chain starts (i.e. `name == langgraph_node`).
"""
if not metadata:
return False
if not metadata.get("langgraph_checkpoint_ns"):
return False
lg_node = metadata.get("langgraph_node")
if lg_node is None or lg_node in _LANGGRAPH_SENTINEL_NODES:
return False
if name != lg_node:
return True
# Name collision fallback: the inner Pregel's parent_run_id is
# the node chain's run_id, which we recorded when that node
# chain's start fired.
return parent_run_id is not None and parent_run_id in task_run_ids
class StreamLifecycleHandler(BaseCallbackHandler, _StreamingCallbackHandler):
"""Callback handler that emits subgraph lifecycle events on the stream.
Pushes `LifecycleData`-shaped payloads onto the pregel stream under
the `"lifecycle"` mode, keyed by the subgraph's namespace tuple.
Drives the `started` `running` `completed` / `failed` /
`interrupted` state machine.
The handler is attached to `run_manager.inheritable_handlers` inside
a `Pregel.stream` / `astream` call, so it sees callbacks for every
descendant chain (nodes, nested `Pregel` subgraphs) but *not* for
the root `Pregel` whose start event has already fired. The root's
`started` event is emitted eagerly at construction; its terminal
state is emitted by `SubgraphTransformer.finalize` / `fail`.
`run_inline = True` keeps event ordering deterministic.
"""
run_inline = True
def __init__(
self,
stream: Callable[[StreamChunk], None],
*,
root_graph_name: str | None = None,
) -> None:
"""Initialize the handler and emit the root graph's `started` event.
Args:
stream: Callable that accepts a `StreamChunk` tuple
`(namespace, mode, payload)` and enqueues it.
root_graph_name: The root `Pregel` instance's `name`, emitted
with the root's `started` lifecycle payload.
"""
self.stream = stream
# Namespaces awaiting the started→running transition.
self._pending_running: set[tuple[str, ...]] = set()
# run_id → subgraph namespace; populated only for Pregel chains.
self._run_to_ns: dict[UUID, tuple[str, ...]] = {}
# run_ids of node-chain starts (name == langgraph_node); used
# as the parent_run_id fallback when a subgraph's name equals
# its node name. Cleared as each chain ends.
self._task_run_ids: set[UUID] = set()
root_payload: dict[str, Any] = {"event": "started"}
if root_graph_name is not None:
root_payload["graph_name"] = root_graph_name
self.stream(((), "lifecycle", root_payload))
self._pending_running.add(())
@staticmethod
def _subgraph_ns_from_metadata(metadata: dict[str, Any] | None) -> tuple[str, ...]:
"""Return the running subgraph's own namespace from task metadata.
For a nested `Pregel` invoked as a node, `langgraph_checkpoint_ns`
ends at the node segment (no inner task appended yet), so
splitting on `NS_SEP` gives the subgraph's own namespace.
"""
if not metadata:
return ()
nskey = metadata.get("langgraph_checkpoint_ns")
if not nskey:
return ()
return tuple(cast(str, nskey).split(NS_SEP))
@staticmethod
def _containing_ns_from_metadata(
metadata: dict[str, Any] | None,
) -> tuple[str, ...]:
"""Return the namespace of the subgraph that contains this task.
For an inner task with `langgraph_checkpoint_ns`
`"seg_a|seg_b"`, the containing subgraph is `("seg_a",)`.
"""
if not metadata:
return ()
nskey = metadata.get("langgraph_checkpoint_ns")
if not nskey:
return ()
return tuple(cast(str, nskey).split(NS_SEP))[:-1]
def _emit(self, ns: tuple[str, ...], payload: dict[str, Any]) -> None:
self.stream((ns, "lifecycle", payload))
def tap_output_aiter(
self, run_id: UUID, output: AsyncIterator[T]
) -> AsyncIterator[T]:
"""Pass-through — required by the `_StreamingCallbackHandler` protocol.
Returns the iterator unchanged. A missing implementation lets
langchain's default `Protocol` body return `None`, which breaks
the `_consume_aiter` code path in `_runnable.py:900`.
"""
return output
def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
"""Pass-through — sync counterpart to `tap_output_aiter`."""
return output
def _fire_running_if_pending(self, ns: tuple[str, ...]) -> None:
if ns in self._pending_running:
self._pending_running.discard(ns)
self._emit(ns, {"event": "running"})
def on_chain_start(
self,
serialized: dict[str, Any],
inputs: dict[str, Any],
*,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
**kwargs: Any,
) -> Any:
# Any descendant activity transitions the containing subgraph to running.
containing = self._containing_ns_from_metadata(metadata)
self._fire_running_if_pending(containing)
name = cast(str | None, kwargs.get("name"))
lg_node = (metadata or {}).get("langgraph_node")
# Record node-chain starts so the name-collision fallback in
# `_is_nested_pregel_start` can match the inner Pregel's
# parent_run_id to them.
if (
lg_node is not None
and lg_node not in _LANGGRAPH_SENTINEL_NODES
and name == lg_node
):
self._task_run_ids.add(run_id)
if not _is_nested_pregel_start(
name, metadata, parent_run_id, self._task_run_ids
):
return
ns = self._subgraph_ns_from_metadata(metadata)
if not ns:
return
self._run_to_ns[run_id] = ns
payload: dict[str, Any] = {"event": "started"}
if name:
payload["graph_name"] = name
# `cause` is intentionally not populated here: pregel does not know
# what on the parent namespace triggered this subgraph. Product-
# specific stream transformers populate `cause` before events
# reach the wire. See LifecycleCause in the protocol definition.
self._emit(ns, payload)
self._pending_running.add(ns)
def on_chain_end(
self,
response: Any,
*,
run_id: UUID,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
self._task_run_ids.discard(run_id)
ns = self._run_to_ns.pop(run_id, None)
if ns is None:
return
# Ensure started→running fired even for empty subgraphs.
if ns in self._pending_running:
self._pending_running.discard(ns)
self._emit(ns, {"event": "running"})
self._emit(ns, {"event": "completed"})
def on_chain_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
self._task_run_ids.discard(run_id)
ns = self._run_to_ns.pop(run_id, None)
if ns is None:
return
self._pending_running.discard(ns)
if isinstance(error, GraphInterrupt):
self._emit(ns, {"event": "interrupted"})
else:
self._emit(ns, {"event": "failed", "error": str(error)})
+40 -5
View File
@@ -692,7 +692,7 @@ class PregelLoop:
# writes so that interrupt() calls re-fire instead of returning
# stale values. But if we're actively resuming, keep them —
# multi-interrupt scenarios need previously resolved values preserved.
if self.is_replaying and (
is_time_traveling = self.is_replaying and (
# Time-travel to a subgraph checkpoint: the parent sets
# RESUMING=True (it can't distinguish time-travel from resume),
# so we check if this subgraph's own ns is in checkpoint_map.
@@ -710,7 +710,8 @@ class PregelLoop:
# (subgraph input is a Send arg, not a Command)
or configurable.get(CONFIG_KEY_RESUMING, False)
)
):
)
if is_time_traveling:
self.checkpoint_pending_writes = [
w for w in self.checkpoint_pending_writes if w[1] != RESUME
]
@@ -765,6 +766,26 @@ class PregelLoop:
if k in self.checkpoint["channel_versions"]:
version = self.checkpoint["channel_versions"][k]
self.checkpoint["versions_seen"][INTERRUPT][k] = version
# When time-traveling (replaying from a specific checkpoint),
# save a fork checkpoint so the replayed execution creates a
# new branch. Without this, if the execution hits an interrupt
# before after_tick() runs, no new checkpoint is created —
# the parent's latest checkpoint remains the old one and
# subsequent resumes load the wrong state.
# Skip for update_state forks (source=update/fork) since they
# already have their own fork checkpoint.
if is_time_traveling and self.checkpoint_metadata.get("source") not in (
"update",
"fork",
):
# Clear old INTERRUPT writes from the loaded checkpoint.
# The fork will have a new checkpoint_id which changes
# task IDs — stale interrupt writes would accumulate and
# confuse the multiple-interrupt check in future resumes.
self.checkpoint_pending_writes = [
w for w in self.checkpoint_pending_writes if w[1] != INTERRUPT
]
self._put_checkpoint({"source": "fork"})
# produce values output
self._emit(
"values", map_output_values, self.output_keys, True, self.channels
@@ -807,14 +828,28 @@ class PregelLoop:
if not self.is_nested:
# Pass the resolved before-bound checkpoint ID so subgraphs can
# find their corresponding checkpoint without re-fetching the
# parent. For forks (source=update), use the fork's parent
# parent. For forks (source=update/fork), use the fork's parent
# checkpoint ID since the fork was created after the subgraph's
# checkpoints from the original execution.
#
# Only gate on is_time_traveling (not is_replaying). When the
# client resumes with an explicit checkpoint_id that happens to
# point at the current head (e.g. LangGraph Studio sending
# `checkpoint: {checkpoint_id}` alongside Command(resume=...)),
# is_replaying is True but is_time_traveling is False. In that
# case subgraphs should load their latest checkpoint normally,
# not go through ReplayState's before-bound lookup which would
# miss subgraph checkpoints created during processing of the
# current parent step.
replay_state: ReplayState | None = None
if self.is_replaying:
if is_time_traveling:
replay_checkpoint_id = self.checkpoint["id"]
if (
self.checkpoint_metadata.get("source") == "update"
self.checkpoint_metadata.get("source")
in (
"update",
"fork",
)
and self.prev_checkpoint_config
):
replay_checkpoint_id = self.prev_checkpoint_config[CONF].get(
+6 -201
View File
@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
from pydantic import BaseModel
from langgraph._internal._constants import NS_END, NS_SEP
from langgraph._internal._constants import NS_SEP
from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
from langgraph.pregel.protocol import StreamChunk
from langgraph.types import Command
@@ -24,11 +24,6 @@ try:
except ImportError:
_StreamingCallbackHandler = object # type: ignore
try:
from langchain_core.tracers._streaming import _V2StreamingCallbackHandler
except ImportError:
_V2StreamingCallbackHandler = object # type: ignore
T = TypeVar("T")
Meta = tuple[tuple[str, ...], dict[str, Any]]
@@ -137,23 +132,15 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
**kwargs: Any,
) -> Any:
if metadata and (not tags or (TAG_NOSTREAM not in tags)):
task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
checkpoint_ns = (
f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
if NS_END in task_checkpoint_ns
else task_checkpoint_ns
)
ns = tuple(task_checkpoint_ns.split(NS_SEP))[:-1]
ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
:-1
]
if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
return
stream_metadata = dict(metadata)
stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
# Preserve backwards-compatible streamed checkpoint metadata shape.
stream_metadata["checkpoint_ns"] = checkpoint_ns
if tags:
if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
stream_metadata["tags"] = filtered_tags
self.metadata[run_id] = (ns, stream_metadata)
metadata["tags"] = filtered_tags
self.metadata[run_id] = (ns, metadata)
def on_llm_new_token(
self,
@@ -261,185 +248,3 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
**kwargs: Any,
) -> Any:
self.metadata.pop(run_id, None)
class StreamMessagesHandlerV2(StreamMessagesHandler, _V2StreamingCallbackHandler):
"""v2 variant of `StreamMessagesHandler`.
Declaring `_V2StreamingCallbackHandler` as a base flips
`BaseChatModel.invoke` to route through `_stream_chat_model_events`
(firing `on_stream_event`) instead of `_stream` (firing
`on_llm_new_token`). Inherits `on_stream_event` from the parent,
which forwards protocol events onto the messages stream channel.
Pregel attaches this class instead of the v1 handler only when
`GraphStreamer` opts in via the internal
`CONFIG_KEY_STREAM_MESSAGES_V2` config key; direct
`graph.stream(stream_mode="messages")` callers keep the v1
AIMessageChunk shape.
"""
def on_chat_model_start(
self,
serialized: dict[str, Any],
messages: list[list[BaseMessage]],
*,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
**kwargs: Any,
) -> Any:
"""Record metadata with the FULL checkpoint namespace for v2.
v1's ``on_chat_model_start`` (inherited) slices the ns tuple
with ``[:-1]`` to re-position chat model tokens onto the
*containing pregel's* namespace — historically convenient for
consumers of ``stream_mode="messages"`` who want "where did
this node produce its output" rather than the chat-model's
own task ns.
For the protocol-v2 wire shape that is wrong: the client
subscribes the root feed at ``namespaces=[[]]`` with
``depth=1``, and any message emitted at depth ``>=1`` that
still carries the containing node's ns must appear at the
*full* path from root so that depth filtering cleanly isolates
subgraph chatter from the root conversation. JS's
``StreamMessagesHandlerV2`` already does
``metadata.langgraph_checkpoint_ns.split("|")`` (no slice); this
override brings the Python v2 handler to the same shape.
Without this, a chat model invoked inside a nested subgraph
(e.g. ``research -> researcher``, ``research`` being a root
node that ``.ainvoke()``s a ``researcher`` subgraph) emits at
``["research:<task>"]`` a single level deep which slips
through the root-feed depth-1 filter and pollutes the main
conversation with subgraph tokens. With this override we emit
at ``["research:<task>", "researcher:<task>"]`` so the client
routes those tokens to the subgraph card instead.
"""
if metadata and (not tags or (TAG_NOSTREAM not in tags)):
task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
# Keep the trailing ``:<task_id>`` segment (unlike the v1
# handler which strips it via ``[:-1]``). The client's
# lifecycle events land on the same ns, so message deltas
# now correlate 1:1 with a ``lifecycle: started`` event —
# ``useMessages(stream, subgraph)`` picks them up without
# needing to collapse sibling namespaces.
ns = tuple(task_checkpoint_ns.split(NS_SEP))
if not self.subgraphs and len(ns) > 1 and ns != self.parent_ns:
return
stream_metadata = dict(metadata)
# Preserve the v1-shaped ``langgraph_checkpoint_ns`` (task
# id stripped, trailing ``NS_END`` retained) so downstream
# consumers reading checkpoint metadata off a streamed
# message see the same shape they did pre-v2. Only the ns
# tuple emitted on the wire changes.
checkpoint_ns = (
f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
if NS_END in task_checkpoint_ns
else task_checkpoint_ns
)
stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
stream_metadata["checkpoint_ns"] = checkpoint_ns
if tags:
if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
stream_metadata["tags"] = filtered_tags
self.metadata[run_id] = (ns, stream_metadata)
def on_chain_start(
self,
serialized: dict[str, Any],
inputs: dict[str, Any],
*,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
**kwargs: Any,
) -> Any:
"""Record chain (node) metadata with the FULL checkpoint ns.
Mirror of :meth:`on_chat_model_start` for the node-start path,
so messages returned by ``on_chain_end`` (``Command`` updates
and plain state dict outputs) land at the same full-path ns as
any chat-model deltas from within that node. See the
:meth:`on_chat_model_start` docstring for why the v1 ``[:-1]``
slice is dropped here.
"""
if (
metadata
and kwargs.get("name") == metadata.get("langgraph_node")
and (not tags or TAG_HIDDEN not in tags)
):
ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))
if not self.subgraphs and len(ns) > 1:
return
self.metadata[run_id] = (ns, metadata)
for value in _state_values(inputs):
if isinstance(value, BaseMessage):
if value.id is not None:
self.seen.add(value.id)
elif isinstance(value, Sequence) and not isinstance(value, str):
for item in value:
if isinstance(item, BaseMessage):
if item.id is not None:
self.seen.add(item.id)
def on_llm_new_token(
self,
token: str,
*,
chunk: ChatGenerationChunk | None = None,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
**kwargs: Any,
) -> Any:
"""Intentional no-op — v1 chunks are not used on v2-flagged runs.
The v2 marker already steers `invoke` to the event generator, so
`on_llm_new_token` should not fire under normal routing. This
override stays a pass-through (no call to `super()`) to make
the intent explicit and to guard against any caller (e.g. a
node that calls `model.stream()` directly, which still fires
the v1 callback) leaking AIMessageChunks onto a v2-flagged
messages stream.
"""
# Intentionally empty: v2 handler does not forward v1 chunks.
def on_stream_event(
self,
event: dict[str, Any],
*,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
**kwargs: Any,
) -> Any:
"""Forward a protocol event from `stream_v2` as a messages stream part.
Fires once per `MessagesData` event (`message-start`, per-block
`content-block-*`, `message-finish`). The transformer layer
correlates events back to a single `ChatModelStream` via
`metadata["run_id"]` attached here so the v1
`stream_mode="messages"` output (which emits
`(AIMessageChunk, metadata)` via `on_llm_new_token`) keeps its
original metadata shape.
Lives on the v2 handler rather than the v1 base: content-block
events are a v2-only concept, and forwarding them only when the
v2 handler is attached keeps the message channel's shape
predictable for v1 callers.
"""
if meta := self.metadata.get(run_id):
# Record message_id on message-start so on_chain_end's
# dedupe skips the finalized AIMessage the node returns
# (otherwise the messages projection double-counts: once
# from streaming, once from the chain output).
if event.get("event") == "message-start":
msg_id = event.get("message_id")
if msg_id:
self.seen.add(msg_id)
v2_meta = {**meta[1], "run_id": str(run_id)}
self.stream((meta[0], "messages", (event, v2_meta)))
-223
View File
@@ -1,223 +0,0 @@
from __future__ import annotations
from collections.abc import AsyncIterator, Callable, Iterator
from contextvars import Token
from typing import Any, TypeVar, cast
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langgraph._internal._constants import NS_SEP
from langgraph.config import _tool_call_writer
from langgraph.pregel.protocol import StreamChunk
try:
from langchain_core.tracers._streaming import _StreamingCallbackHandler
except ImportError:
_StreamingCallbackHandler = object # type: ignore[assignment,misc]
T = TypeVar("T")
ToolCallWriter = Callable[[Any], None]
"""A closure bound to a single tool call that emits `tool-output-delta` events."""
class StreamToolCallHandler(BaseCallbackHandler, _StreamingCallbackHandler):
"""Callback handler that emits tool-call lifecycle events on the stream.
Fires on LangChain's `on_tool_*` callbacks and pushes to the `tools`
stream mode. Emits `tool-started` / `tool-output-delta` /
`tool-finished` / `tool-error` payloads keyed by `tool_call_id`.
While a tool is executing, this handler sets `_tool_call_writer` to a
closure bound to that call's namespace and `tool_call_id`. The
`emit_tool_output_delta` helper in `langgraph.config` reads that
ContextVar so tool bodies can stream partial output without threading
the writer through their own signature.
Attached by `Pregel.stream` / `astream` when `"tools"` is in
`stream_modes`. `run_inline = True` keeps event ordering
deterministic.
"""
run_inline = True
def __init__(self, stream: Callable[[StreamChunk], None]) -> None:
"""Initialize the handler.
Args:
stream: Callable that accepts a `StreamChunk` tuple
`(namespace, mode, payload)` and enqueues it.
"""
self.stream = stream
# run_id → (namespace, tool_call_id, ContextVar token)
# `on_tool_end` does not receive `tool_call_id` in kwargs, so
# we correlate by `run_id` which is present on every callback.
self._run_to_call: dict[
UUID, tuple[tuple[str, ...], str, Token[ToolCallWriter | None]]
] = {}
@staticmethod
def _containing_ns_from_metadata(
metadata: dict[str, Any] | None,
) -> tuple[str, ...]:
"""Return the namespace of the subgraph that contains this tool call.
`langgraph_checkpoint_ns` on a tool's callback metadata ends with
the `node_name:task_id` segment of the node that invoked the
tool. Dropping that segment gives the subgraph's own namespace,
which matches what other `tools` / `lifecycle` / `messages`
emitters use.
"""
if not metadata:
return ()
nskey = metadata.get("langgraph_checkpoint_ns")
if not nskey:
return ()
return tuple(cast(str, nskey).split(NS_SEP))[:-1]
def _start(
self,
serialized: dict[str, Any] | None,
input_str: str,
*,
run_id: UUID,
metadata: dict[str, Any] | None,
inputs: dict[str, Any] | None,
kwargs: dict[str, Any],
) -> None:
tool_call_id = cast("str | None", kwargs.get("tool_call_id")) or str(run_id)
tool_name = (
(serialized or {}).get("name")
or cast("str | None", kwargs.get("name"))
or ""
)
ns = self._containing_ns_from_metadata(metadata)
def writer(delta: Any) -> None:
self.stream(
(
ns,
"tools",
{
"event": "tool-output-delta",
"tool_call_id": tool_call_id,
"delta": delta,
},
)
)
token = _tool_call_writer.set(writer)
self._run_to_call[run_id] = (ns, tool_call_id, token)
payload: dict[str, Any] = {
"event": "tool-started",
"tool_call_id": tool_call_id,
"tool_name": tool_name,
}
if inputs is not None:
payload["input"] = inputs
self.stream((ns, "tools", payload))
def _end(self, output: Any, *, run_id: UUID) -> None:
info = self._run_to_call.pop(run_id, None)
if info is None:
return
ns, tool_call_id, token = info
self._reset_writer(token)
self.stream(
(
ns,
"tools",
{
"event": "tool-finished",
"tool_call_id": tool_call_id,
"output": output,
},
)
)
def _error(self, error: BaseException, *, run_id: UUID) -> None:
info = self._run_to_call.pop(run_id, None)
if info is None:
return
ns, tool_call_id, token = info
self._reset_writer(token)
self.stream(
(
ns,
"tools",
{
"event": "tool-error",
"tool_call_id": tool_call_id,
"message": str(error),
},
)
)
def tap_output_aiter(
self, run_id: UUID, output: AsyncIterator[T]
) -> AsyncIterator[T]:
"""Pass-through — required by the `_StreamingCallbackHandler` protocol."""
return output
def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
"""Pass-through — sync counterpart to `tap_output_aiter`."""
return output
@staticmethod
def _reset_writer(token: Token[ToolCallWriter | None]) -> None:
# Token is invalid if `on_tool_end` runs in a different context
# than `on_tool_start` (e.g. langchain may hand off to a thread
# worker without copying the context). Swallow that case; the
# ContextVar lifetime is bounded by the enclosing task anyway.
try:
_tool_call_writer.reset(token)
except ValueError:
pass
# ------------------------------------------------------------------
# Sync callbacks
# ------------------------------------------------------------------
def on_tool_start(
self,
serialized: dict[str, Any],
input_str: str,
*,
run_id: UUID,
parent_run_id: UUID | None = None,
tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
inputs: dict[str, Any] | None = None,
**kwargs: Any,
) -> Any:
self._start(
serialized,
input_str,
run_id=run_id,
metadata=metadata,
inputs=inputs,
kwargs=kwargs,
)
def on_tool_end(
self,
output: Any,
*,
run_id: UUID,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
self._end(output, run_id=run_id)
def on_tool_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: UUID | None = None,
**kwargs: Any,
) -> Any:
self._error(error, run_id=run_id)
+3 -221
View File
@@ -73,7 +73,6 @@ from langgraph._internal._constants import (
CONFIG_KEY_RUNTIME,
CONFIG_KEY_SEND,
CONFIG_KEY_STREAM,
CONFIG_KEY_STREAM_MESSAGES_V2,
CONFIG_KEY_TASK_ID,
CONFIG_KEY_THREAD_ID,
ERROR,
@@ -130,19 +129,14 @@ from langgraph.pregel._checkpoint import (
)
from langgraph.pregel._draw import draw_graph
from langgraph.pregel._io import map_input, read_channels
from langgraph.pregel._lifecycle import StreamLifecycleHandler
from langgraph.pregel._loop import (
AsyncPregelLoop,
SyncPregelLoop,
)
from langgraph.pregel._messages import (
StreamMessagesHandler,
StreamMessagesHandlerV2,
)
from langgraph.pregel._messages import StreamMessagesHandler
from langgraph.pregel._read import DEFAULT_BOUND, PregelNode
from langgraph.pregel._retry import RetryPolicy
from langgraph.pregel._runner import PregelRunner
from langgraph.pregel._tools import StreamToolCallHandler
from langgraph.pregel._utils import get_new_channel_versions
from langgraph.pregel._validate import validate_graph, validate_keys
from langgraph.pregel._write import ChannelWrite, ChannelWriteEntry
@@ -346,65 +340,6 @@ class NodeBuilder:
)
def _collect_stream_modes(mux: Any) -> list[StreamMode]:
"""Return the union of `required_stream_modes` across registered transformers.
Transformers declare the stream modes they need to function, and
`stream_v2` asks the graph for exactly that union no hardcoded
default set. If zero transformers are registered (or none declares
a given mode), the graph does not stream events for that mode.
"""
modes: set[str] = set()
for transformer in mux._transformers:
modes.update(transformer.required_stream_modes)
return cast("list[StreamMode]", list(modes))
def _build_stream_factories(
compile_time: Sequence[Callable[..., Any]],
call_site: Sequence[Any] | None,
) -> list[Callable[..., Any]]:
"""Assemble the factory list handed to `StreamMux(factories=...)`.
Prepends the built-in `ValuesTransformer`, `MessagesTransformer`,
and `SubgraphTransformer` factories, then appends the graph's
compile-time `stream_transformers` followed by any call-site
additions. Factories flow down into subgraph mini-muxes, so
per-scope instances propagate automatically.
"""
from langgraph.stream.transformers import (
MessagesTransformer,
SubgraphTransformer,
ToolLifecycleTransformer,
ValuesTransformer,
)
builtins: list[Callable[..., Any]] = [
ValuesTransformer,
ToolLifecycleTransformer,
MessagesTransformer,
SubgraphTransformer,
]
return [*builtins, *compile_time, *(call_site or ())]
def _merge_v2_messages_flag(
config: RunnableConfig | None,
) -> RunnableConfig:
"""Return a config with the v2 messages flag set in `configurable`.
Signals to pregel that `stream_mode="messages"` should attach
`StreamMessagesHandlerV2` for this call so invoke-time model runs
route through the v2 event generator and their protocol events
reach the messages channel.
"""
merged: RunnableConfig = dict(config or {}) # type: ignore[assignment]
configurable = dict(merged.get(CONF) or {})
configurable[CONFIG_KEY_STREAM_MESSAGES_V2] = True
merged[CONF] = configurable
return merged
class Pregel(
PregelProtocol[StateT, ContextT, InputT, OutputT],
Generic[StateT, ContextT, InputT, OutputT],
@@ -736,7 +671,6 @@ class Pregel(
config: RunnableConfig | None = None,
trigger_to_nodes: Mapping[str, Sequence[str]] | None = None,
name: str = "LangGraph",
stream_transformers: Sequence[Callable[..., Any]] | None = None,
**deprecated_kwargs: Unpack[DeprecatedKwargs],
) -> None:
if (
@@ -783,9 +717,6 @@ class Pregel(
self.config = config
self.trigger_to_nodes = trigger_to_nodes or {}
self.name = name
self._stream_transformers: tuple[Callable[..., Any], ...] = tuple(
stream_transformers or ()
)
self._serde_allowlist: set[tuple[str, ...]] | None = None
if auto_validate:
self.validate()
@@ -876,15 +807,6 @@ class Pregel(
def copy(self, update: dict[str, Any] | None = None) -> Self:
attrs = {k: v for k, v in self.__dict__.items() if k != "__orig_class__"}
# ``__init__`` accepts ``stream_transformers`` (public parameter) but
# the attribute is stored as ``_stream_transformers`` (private). Map
# the private key back onto the public kwarg so compile-time
# transformers survive ``copy()`` / ``with_config()``. Without this,
# ``_stream_transformers`` gets captured by ``**deprecated_kwargs``
# and the resulting instance silently has an empty transformer
# pipeline.
if "_stream_transformers" in attrs:
attrs["stream_transformers"] = attrs.pop("_stream_transformers")
attrs.update(update or {})
return self.__class__(**attrs)
@@ -2704,34 +2626,14 @@ class Pregel(
# set up messages stream mode
if "messages" in stream_modes:
ns_ = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
messages_handler_cls = (
StreamMessagesHandlerV2
if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
else StreamMessagesHandler
)
run_manager.inheritable_handlers.append(
messages_handler_cls(
StreamMessagesHandler(
stream.put,
subgraphs,
parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
)
)
# set up lifecycle stream mode
if "lifecycle" in stream_modes:
run_manager.inheritable_handlers.append(
StreamLifecycleHandler(
stream.put,
root_graph_name=self.name,
)
)
# set up tools stream mode
if "tools" in stream_modes:
run_manager.inheritable_handlers.append(
StreamToolCallHandler(stream.put)
)
# set up custom stream mode
if "custom" in stream_modes:
@@ -3101,34 +3003,14 @@ class Pregel(
if "messages" in stream_modes:
# namespace can be None in a root level graph?
ns_ = cast(str | None, config[CONF].get(CONFIG_KEY_CHECKPOINT_NS))
messages_handler_cls = (
StreamMessagesHandlerV2
if config[CONF].get(CONFIG_KEY_STREAM_MESSAGES_V2)
else StreamMessagesHandler
)
run_manager.inheritable_handlers.append(
messages_handler_cls(
StreamMessagesHandler(
stream_put,
subgraphs,
parent_ns=tuple(ns_.split(NS_SEP)) if ns_ else None,
)
)
# set up lifecycle stream mode
if "lifecycle" in stream_modes:
run_manager.inheritable_handlers.append(
StreamLifecycleHandler(
stream_put,
root_graph_name=self.name,
)
)
# set up tools stream mode
if "tools" in stream_modes:
run_manager.inheritable_handlers.append(
StreamToolCallHandler(stream_put)
)
# set up custom stream mode
def stream_writer(c: Any) -> None:
aioloop.call_soon_threadsafe(
@@ -3355,106 +3237,6 @@ class Pregel(
await asyncio.shield(run_manager.on_chain_error(e))
raise
def stream_v2(
self,
input: InputT | Command | None,
config: RunnableConfig | None = None,
*,
interrupt_before: All | Sequence[str] | None = None,
interrupt_after: All | Sequence[str] | None = None,
transformers: Sequence[Any] | None = None,
stream_modes: Sequence[StreamMode] | None = None,
output_keys: str | Sequence[str] | None = None,
**kwargs: Any,
) -> Any:
"""Start a sync v2 streaming run driven by transformer projections.
Builds a `StreamMux` from the built-in `ValuesTransformer` /
`MessagesTransformer`, this graph's compile-time
`stream_transformers`, and any additional `transformers=`
supplied at the call site. Returns a `GraphRunStream` that the
caller drives by iterating any projection no background
thread.
Args:
input: Graph input.
config: Optional runnable config forwarded to the graph.
interrupt_before: Nodes to interrupt before, if any.
interrupt_after: Nodes to interrupt after, if any.
transformers: Extra transformer instances appended after
compile-time `stream_transformers`.
Returns:
A `GraphRunStream` the caller iterates to drive the run.
"""
from langgraph.stream._mux import StreamMux
from langgraph.stream.run_stream import GraphRunStream
factories = _build_stream_factories(self._stream_transformers, transformers)
mux = StreamMux(factories=factories, is_async=False)
requested_stream_modes = set(_collect_stream_modes(mux))
requested_stream_modes.update(stream_modes or ())
graph_iter = iter(
self.stream(
input,
_merge_v2_messages_flag(config),
stream_mode=list(requested_stream_modes),
subgraphs=True,
version="v2",
output_keys=output_keys,
interrupt_before=interrupt_before,
interrupt_after=interrupt_after,
**kwargs,
)
)
return GraphRunStream(graph_iter, mux)
async def astream_v2(
self,
input: InputT | Command | None,
config: RunnableConfig | None = None,
*,
interrupt_before: All | Sequence[str] | None = None,
interrupt_after: All | Sequence[str] | None = None,
transformers: Sequence[Any] | None = None,
stream_modes: Sequence[StreamMode] | None = None,
output_keys: str | Sequence[str] | None = None,
**kwargs: Any,
) -> Any:
"""Async counterpart to `stream_v2`.
Returns an `AsyncGraphRunStream` whose projections can be awaited
concurrently; each subscribed cursor drives the pump when its
buffer is empty.
Args:
input: Graph input.
config: Optional runnable config forwarded to the graph.
interrupt_before: Nodes to interrupt before, if any.
interrupt_after: Nodes to interrupt after, if any.
transformers: Extra transformer instances appended after
compile-time `stream_transformers`.
"""
from langgraph.stream._mux import StreamMux
from langgraph.stream.run_stream import AsyncGraphRunStream
factories = _build_stream_factories(self._stream_transformers, transformers)
mux = StreamMux(factories=factories, is_async=True)
requested_stream_modes = set(_collect_stream_modes(mux))
requested_stream_modes.update(stream_modes or ())
graph_aiter = self.astream(
input,
_merge_v2_messages_flag(config),
stream_mode=list(requested_stream_modes),
subgraphs=True,
version="v2",
output_keys=output_keys,
interrupt_before=interrupt_before,
interrupt_after=interrupt_after,
**kwargs,
).__aiter__()
return AsyncGraphRunStream(graph_aiter, mux)
@overload
def invoke(
self,
+4 -28
View File
@@ -650,46 +650,22 @@ class RemoteGraph(PregelProtocol):
"""
updated_stream_modes: list[StreamModeSDK] = []
req_single = True
# `"lifecycle"` is emitted locally by the `StreamLifecycleHandler`
# attached inside `Pregel.stream` / `astream`. The remote graph
# API has no corresponding mode, so requests for it against a
# `RemoteGraph` are silently stripped here and a warning is
# logged so the caller isn't left wondering why no lifecycle
# events arrive.
dropped_lifecycle = False
# coerce to list, or add default stream mode
if stream_mode:
if isinstance(stream_mode, str):
if stream_mode != "lifecycle":
updated_stream_modes.append(cast(StreamModeSDK, stream_mode))
else:
dropped_lifecycle = True
updated_stream_modes.append(stream_mode)
else:
req_single = False
for m in stream_mode:
if m == "lifecycle":
dropped_lifecycle = True
else:
updated_stream_modes.append(cast(StreamModeSDK, m))
updated_stream_modes.extend(stream_mode)
else:
updated_stream_modes.append(default) # type: ignore[arg-type]
updated_stream_modes.append(default)
requested_stream_modes = updated_stream_modes.copy()
# add any from parent graph
stream: StreamProtocol | None = (
(config or {}).get(CONF, {}).get(CONFIG_KEY_STREAM)
)
if stream:
for m in stream.modes:
if m == "lifecycle":
dropped_lifecycle = True
else:
updated_stream_modes.append(cast(StreamModeSDK, m))
if dropped_lifecycle:
logger.warning(
"Stream mode 'lifecycle' is not supported by RemoteGraph "
"and was stripped from the request; no lifecycle events "
"will be emitted for this remote run."
)
updated_stream_modes.extend(stream.modes)
# map "messages" to "messages-tuple"
if "messages" in updated_stream_modes:
updated_stream_modes.remove("messages")
@@ -1,20 +0,0 @@
"""Streaming infrastructure for LangGraph.
Compile a graph with `transformers=[...]` and call `graph.stream_v2()` /
`graph.astream_v2()` to drive a transformer pipeline that projects the
graph's raw events into ergonomic per-channel streams.
"""
from langgraph.stream._event_log import EventLog
from langgraph.stream._types import ProtocolEvent, StreamTransformer
from langgraph.stream.run_stream import AsyncGraphRunStream, GraphRunStream
from langgraph.stream.stream_channel import StreamChannel
__all__ = [
"AsyncGraphRunStream",
"EventLog",
"GraphRunStream",
"ProtocolEvent",
"StreamChannel",
"StreamTransformer",
]
@@ -1,71 +0,0 @@
from __future__ import annotations
import time
from typing import Any, cast
from langgraph.stream._types import ProtocolEvent, _ProtocolEventParams
from langgraph.types import StreamPart
def _is_v2_messages_payload(data: Any) -> bool:
return isinstance(data, dict) and isinstance(data.get("event"), str)
def _normalize_messages_data(data: dict[str, Any]) -> dict[str, Any]:
"""Normalize Python Core message fields to the protocol wire shape."""
normalized = {**data}
if (
normalized["event"] == "message-start"
and "id" not in normalized
and isinstance(normalized.get("message_id"), str)
):
normalized["id"] = normalized["message_id"]
if (
normalized["event"]
in ("content-block-start", "content-block-delta", "content-block-finish")
and "content" not in normalized
and isinstance(normalized.get("content_block"), dict)
):
normalized["content"] = normalized["content_block"]
normalized.pop("message_id", None)
normalized.pop("content_block", None)
return normalized
def convert_to_protocol_event(part: StreamPart) -> ProtocolEvent:
"""Convert a v2 StreamPart to a ProtocolEvent.
Args:
part: A stream part with keys `type`, `ns`, `data`, and
optionally `interrupts` (present on values events).
Returns:
The equivalent ProtocolEvent.
"""
part_dict = cast(dict[str, Any], part)
data = part_dict["data"]
params: _ProtocolEventParams = {
"namespace": list(part_dict["ns"]),
"timestamp": int(time.time() * 1000),
"data": data,
}
if (
part_dict["type"] == "messages"
and isinstance(data, tuple)
and len(data) == 2
and _is_v2_messages_payload(data[0])
and isinstance(data[1], dict)
):
payload, metadata = data
params["data"] = _normalize_messages_data(payload)
if isinstance(metadata.get("langgraph_node"), str):
params["node"] = metadata["langgraph_node"]
if isinstance(metadata.get("run_id"), str):
params["run_id"] = metadata["run_id"]
if "interrupts" in part_dict:
params["interrupts"] = part_dict["interrupts"]
return {
"type": "event",
"method": part_dict["type"],
"params": params,
}
@@ -1,306 +0,0 @@
from __future__ import annotations
import asyncio
from collections import deque
from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
from typing import Generic, TypeVar
T = TypeVar("T")
class EventLog(Generic[T]):
"""Single-consumer drainable queue for streaming events.
Items are popped off the front as the consumer advances there is
no retention beyond what's currently queued. A log accepts exactly
one subscriber; a second `__iter__` / `__aiter__` call raises. Use
`tee(n)` / `atee(n)` for fan-out.
Starts unbound neither `__iter__` nor `__aiter__` is available
until the StreamMux calls `_bind(is_async)`. After binding, only
the matching iteration protocol works; the other raises `TypeError`.
Pump wiring (set by the run stream, not by `_bind`):
- `_request_more`: sync pump callable, returns True if a new
event was produced.
- `_arequest_more`: async pump coroutine factory, same contract.
Memory is bounded by caller pace: both sync and async use caller-
driven pumps, so each cursor advance produces at most one event.
The only shape where a log can accumulate meaningfully is
concurrent async consumers at unequal rates a slow consumer's
log grows while fast consumers drive the shared pump. That's the
documented tradeoff for concurrent consumption; consume at similar
rates or use a single consumer if memory matters.
Lazy-subscribe: `push` is a no-op when no subscriber has registered.
Transformers still execute `process()` (so scalar state like
`ValuesTransformer._latest` stays current); only the log append is
skipped.
"""
def __init__(self, maxlen: int | None = None) -> None:
"""Initialize an empty, unbound log.
Args:
maxlen: Accepted for forward compatibility; currently unused.
The caller-driven pump bounds memory naturally for
single-consumer use.
Raises:
ValueError: If `maxlen` is not a positive integer or `None`.
"""
if maxlen is not None and maxlen <= 0:
raise ValueError("EventLog maxlen must be a positive int or None")
self._items: deque[T] = deque()
self._maxlen: int | None = maxlen
self._closed = False
self._error: BaseException | None = None
# Binding state — None means unbound.
self._is_async: bool | None = None
# Flipped on first __iter__ / __aiter__. Pre-subscription
# pushes are silent no-ops.
self._subscribed = False
# Pump wiring set by the run stream after bind.
self._request_more: Callable[[], bool] | None = None
self._arequest_more: Callable[[], Awaitable[bool]] | None = None
# ------------------------------------------------------------------
# Binding
# ------------------------------------------------------------------
def _bind(self, *, is_async: bool) -> None:
"""Bind this log to sync or async mode.
Called by the StreamMux after transformer registration. Must be
called exactly once before any iteration.
Args:
is_async: True to enable async iteration, False for sync.
Raises:
RuntimeError: If the log has already been bound.
"""
if self._is_async is not None:
raise RuntimeError("EventLog is already bound")
self._is_async = is_async
# ------------------------------------------------------------------
# Producer API
# ------------------------------------------------------------------
def push(self, item: T) -> None:
"""Append an item. No-op when no subscriber is registered.
Non-blocking in both sync and async matches v1's
`put_nowait` producer shape. Memory is bounded by caller pace
via the caller-driven pump.
Raises:
RuntimeError: If the log is closed (and subscribed).
"""
if not self._subscribed:
return
if self._closed:
raise RuntimeError("Cannot push to a closed EventLog")
self._items.append(item)
def close(self) -> None:
"""Mark the log as complete."""
self._closed = True
def fail(self, err: BaseException) -> None:
"""Mark the log as errored.
Args:
err: The exception to surface to the subscriber.
"""
self._error = err
self._closed = True
# ------------------------------------------------------------------
# Sync iteration (caller-driven pump)
# ------------------------------------------------------------------
def __iter__(self) -> Iterator[T]:
"""Subscribe and return a sync cursor. Can be called only once.
Raises:
TypeError: If the log is unbound or bound to async mode.
RuntimeError: If the log already has a subscriber.
"""
if self._is_async is None:
raise TypeError(
"EventLog has not been bound yet. "
"Register the transformer with a StreamMux first."
)
if self._is_async:
raise TypeError(
"This EventLog is bound to async mode — use 'async for' instead."
)
if self._subscribed:
raise RuntimeError(
"EventLog already has a subscriber; use .tee(n) for fan-out."
)
self._subscribed = True
return self._sync_cursor()
def _sync_cursor(self) -> Iterator[T]:
while True:
if self._items:
yield self._items.popleft()
elif self._closed:
if self._error is not None:
raise self._error
return
elif self._request_more is not None:
if not self._request_more():
if not self._items and not self._closed:
return
else:
return
# ------------------------------------------------------------------
# Async iteration (caller-driven pump)
# ------------------------------------------------------------------
def __aiter__(self) -> AsyncIterator[T]:
"""Subscribe and return an async cursor. Can be called only once.
Raises:
TypeError: If the log is unbound or bound to sync mode.
RuntimeError: If the log already has a subscriber.
"""
if self._is_async is None:
raise TypeError(
"EventLog has not been bound yet. "
"Register the transformer with a StreamMux first."
)
if not self._is_async:
raise TypeError("This EventLog is bound to sync mode — use 'for' instead.")
if self._subscribed:
raise RuntimeError(
"EventLog already has a subscriber; use .atee(n) for fan-out."
)
self._subscribed = True
return self._async_cursor()
async def _async_cursor(self) -> AsyncIterator[T]:
while True:
if self._items:
yield self._items.popleft()
elif self._closed:
if self._error is not None:
raise self._error
return
elif self._arequest_more is not None:
if not await self._arequest_more():
if not self._items and not self._closed:
return
else:
return
# ------------------------------------------------------------------
# Fan-out via tee
# ------------------------------------------------------------------
def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
"""Subscribe and return `n` independent sync iterators.
Each branch has its own buffer; items pulled from the
underlying cursor are copied into every branch. Branches are
naturally bounded by caller pace since the sync pump is
caller-driven.
Args:
n: Number of branches to create. Must be >= 1.
Returns:
A tuple of `n` iterators over the same underlying stream.
Raises:
TypeError: If the log is unbound or bound to async mode.
RuntimeError: If the log already has a subscriber.
ValueError: If `n` < 1.
"""
if n < 1:
raise ValueError("tee() requires n >= 1")
source = self.__iter__()
buffers: list[deque[T]] = [deque() for _ in range(n)]
exhausted = [False]
def branch(i: int) -> Iterator[T]:
buf = buffers[i]
while True:
if buf:
yield buf.popleft()
elif exhausted[0]:
return
else:
try:
item = next(source)
except StopIteration:
exhausted[0] = True
return
for b in buffers:
b.append(item)
return tuple(branch(i) for i in range(n))
def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
"""Subscribe and return `n` independent async iterators.
Caller-driven fan-out: each branch's `__anext__` either pops
from its own buffer or, under a shared `asyncio.Lock`, pulls
one item from the underlying cursor and distributes it to
every branch's buffer.
Args:
n: Number of branches to create. Must be >= 1.
Returns:
A tuple of `n` async iterators over the same underlying
stream.
Raises:
TypeError: If the log is unbound or bound to sync mode.
RuntimeError: If the log already has a subscriber.
ValueError: If `n` < 1.
"""
if n < 1:
raise ValueError("atee() requires n >= 1")
source = self.__aiter__()
buffers: list[deque[T]] = [deque() for _ in range(n)]
exhausted = [False]
error: list[BaseException | None] = [None]
lock = asyncio.Lock()
async def branch(i: int) -> AsyncIterator[T]:
buf = buffers[i]
while True:
if buf:
yield buf.popleft()
continue
if exhausted[0]:
if error[0] is not None:
raise error[0]
return
async with lock:
if buf or exhausted[0]:
continue
try:
item = await source.__anext__()
except StopAsyncIteration:
exhausted[0] = True
continue
except Exception as e:
error[0] = e
exhausted[0] = True
continue
for b in buffers:
b.append(item)
return tuple(branch(i) for i in range(n))
-497
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@@ -1,497 +0,0 @@
from __future__ import annotations
import asyncio
import time
from collections.abc import Awaitable, Callable
from typing import Any
from langgraph.stream._event_log import EventLog
from langgraph.stream._types import (
ProtocolEvent,
StreamTransformer,
transformer_requires_async,
)
from langgraph.stream.stream_channel import StreamChannel
TransformerFactory = Callable[["tuple[str, ...]"], StreamTransformer]
"""Factory that builds a scoped transformer for a mux.
Called once per `StreamMux` (root or mini-mux) with the mux's scope
typically a subgraph's namespace or `()` for the root. Standard
transformer classes (`ValuesTransformer`, `MessagesTransformer`,
`SubgraphTransformer`) accept a single positional scope argument, so
the class itself is a valid factory. User transformers can close over
their config: `lambda scope: MyTransformer(scope, foo=...)`.
"""
class StreamMux:
"""Central event dispatcher for the streaming infrastructure.
Owns the main event log and routes events through a transformer
pipeline. StreamChannels discovered in transformer projections are
auto-wired so that every `push()` also injects a `ProtocolEvent`
into the main log.
Pass `is_async=True` when the mux will be consumed via async
iteration (`handler.astream()`). All EventLog and StreamChannel
instances discovered during registration are automatically bound
to the matching mode.
Attributes:
extensions: Merged projection dict across all registered
transformers. Treat as read-only mutations won't be
reflected back in individual transformers' state.
native_keys: Projection keys contributed by transformers with
`_native = True`.
"""
def __init__(
self,
transformers: list[StreamTransformer] | None = None,
*,
is_async: bool = False,
factories: list[TransformerFactory] | None = None,
scope: tuple[str, ...] = (),
) -> None:
"""Initialize the mux and register transformers in order.
Callers pass either `transformers` (pre-built instances) or
`factories` (callables producing fresh instances per mux). A
factory list is preferred mini-muxes built by `make_child()`
inherit the factory list, so transformers propagate naturally
into every subgraph's scope. `transformers` is kept for
back-compat tests that exercise the mux directly.
Each transformer's `init()` is called once during registration,
projections are merged into `extensions`, `_native` keys are
recorded in `native_keys`, and any EventLog / StreamChannel
instances are bound and wired.
Args:
transformers: Already-built transformer instances. Mutually
exclusive with `factories`.
is_async: True for async dispatch (`apush` / `aclose` /
`afail`), False for the sync path.
factories: Zero-or-one-argument callables producing
transformers. Called with this mux's `scope`.
scope: The namespace the mux operates within. The root mux
is `()`; mini-muxes for subgraphs use the subgraph's
namespace tuple.
Raises:
RuntimeError: If any transformer requires an async run but
the mux is in sync mode.
TypeError: If a transformer's `init()` doesn't return a dict.
ValueError: If transformers' projection keys collide, or if
both `transformers` and `factories` are supplied.
"""
if transformers is not None and factories is not None:
raise ValueError("Pass either `transformers` or `factories`, not both.")
self._is_async = is_async
self._factories: list[TransformerFactory] = list(factories or ())
self.scope: tuple[str, ...] = scope
self._pump_fn: Callable[[], bool] | None = None
self._apump_fn: Callable[[], Awaitable[bool]] | None = None
self._events: EventLog[ProtocolEvent] = EventLog()
self._events._bind(is_async=is_async)
self._transformers: list[StreamTransformer] = []
self._channels: list[StreamChannel[Any]] = []
self._logs: list[EventLog[Any]] = []
self._seq = 0
self.extensions: dict[str, Any] = {}
self.native_keys: set[str] = set()
self._projection_owners: dict[str, str] = {}
self._transformer_by_key: dict[str, StreamTransformer] = {}
if factories is not None:
for factory in factories:
self._register(factory(scope))
else:
for transformer in transformers or ():
self._register(transformer)
def make_child(self, scope: tuple[str, ...]) -> StreamMux:
"""Build a mini-mux with the same factories scoped to `scope`.
Used by `SubgraphTransformer` to attach a fresh transformer
pipeline to each discovered subgraph handle. The child mux
inherits the current pump binding (so cursors on its projection
logs drive the root pump) and carries the same factory list
forward to any grandchild subgraphs.
Raises:
RuntimeError: If the mux was not built from a factory list
(i.e., constructed with `transformers=`). Mini-muxes
require factories so each scope gets its own fresh
transformer instances.
"""
if not self._factories:
raise RuntimeError(
"StreamMux.make_child requires the mux to be constructed "
"with factories; pre-built transformers can't be cloned "
"to a new scope."
)
child = StreamMux(
factories=self._factories,
is_async=self._is_async,
scope=scope,
)
if self._pump_fn is not None:
child.bind_pump(self._pump_fn)
if self._apump_fn is not None:
child.bind_apump(self._apump_fn)
return child
def bind_pump(self, fn: Callable[[], bool]) -> None:
"""Wire the sync pull callback onto every EventLog in the mux.
Also propagates to transformers that expose `_bind_pump` so
nested handles (e.g., `ChatModelStream` instances produced by
`MessagesTransformer`) can drive the graph pump from their
projection cursors.
"""
self._pump_fn = fn
self._events._request_more = fn
for value in self.extensions.values():
if isinstance(value, EventLog):
value._request_more = fn
elif isinstance(value, StreamChannel):
value._log._request_more = fn
for transformer in self._transformers:
bind = getattr(transformer, "_bind_pump", None)
if bind is not None:
bind(fn)
def bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
"""Async counterpart to `bind_pump`."""
self._apump_fn = fn
self._events._arequest_more = fn
for value in self.extensions.values():
if isinstance(value, EventLog):
value._arequest_more = fn
elif isinstance(value, StreamChannel):
value._log._arequest_more = fn
for transformer in self._transformers:
abind = getattr(transformer, "_bind_apump", None)
if abind is not None:
abind(fn)
def _register(self, transformer: StreamTransformer) -> None:
"""Register a single transformer.
Calls `transformer.init()`, stores the transformer for event
processing, binds any EventLog or StreamChannel instances in
the projection, and merges the projection into `extensions`.
"""
if transformer_requires_async(transformer) and not self._is_async:
raise RuntimeError(
f"{type(transformer).__name__} requires an async run — "
"it overrides aprocess/afinalize/afail or sets "
"requires_async=True. Use astream(), not stream()."
)
projection = transformer.init()
if not isinstance(projection, dict):
raise TypeError(
f"StreamTransformer.init() must return a dict, "
f"got {type(projection).__name__}"
)
conflicts = set(projection) & set(self.extensions)
if conflicts:
attributions = ", ".join(
f"{key!r} (owned by {self._projection_owners[key]})"
for key in sorted(conflicts)
)
raise ValueError(
f"Transformer {type(transformer).__name__} returned "
f"projection keys that conflict with already-registered "
f"keys: {attributions}"
)
self._transformers.append(transformer)
self._bind_and_wire(projection)
self.extensions.update(projection)
owner_name = type(transformer).__name__
for key in projection:
self._projection_owners[key] = owner_name
self._transformer_by_key[key] = transformer
if getattr(transformer, "_native", False):
self.native_keys.update(projection.keys())
on_register = getattr(transformer, "_on_register", None)
if on_register is not None:
on_register(self)
def transformer_by_key(self, key: str) -> StreamTransformer | None:
"""Return the transformer that owns the projection at `key`, if any."""
return self._transformer_by_key.get(key)
def emit(self, event: ProtocolEvent) -> None:
"""Append a protocol event directly to the main log.
Built-in transformers use this for protocol repair events that
must appear before the source event they are processing. Direct
emission intentionally bypasses the transformer pipeline, but
still lets this mux remain the only local sequencing authority.
"""
self._seq += 1
event["seq"] = self._seq
self._events.push(event)
def push(self, event: ProtocolEvent) -> None:
"""Route an event through all transformers, then append to the main log.
Each transformer's `process()` is called in registration order
except when the transformer has `scope_exact = True` (the
default) and the event's namespace differs from the mux's
`scope`, in which case the transformer is skipped. Transformers
that need to see cross-scope events opt out by setting
`scope_exact = False` (e.g. `SubgraphTransformer`).
If any transformer returns False, the event is suppressed from
the main log, but transformers that already saw it keep their
side effects.
Seq is assigned right before an event enters the main log, not
before the transformer pipeline runs. This ensures that events
auto-forwarded from StreamChannels during `process()` get
earlier seq numbers than the original event, preserving
monotonic ordering in the log.
Args:
event: The protocol event to dispatch.
"""
ns = tuple(event["params"]["namespace"])
in_scope = ns == self.scope
keep = True
for transformer in self._transformers:
if transformer.scope_exact and not in_scope:
continue
if not transformer.process(event):
keep = False
if keep:
self._seq += 1
event["seq"] = self._seq
self._events.push(event)
def close(self) -> None:
"""Finalize all transformers, close all projections and the main log.
EventLogs and StreamChannels discovered in transformer
projections are auto-closed after `finalize()` runs
transformers don't need to close them manually. If any
transformer's `finalize()` raises, the remaining transformers,
projections, and the main log are still closed; the first error
is re-raised after cleanup completes.
Raises:
BaseException: The first error raised by a transformer's
`finalize()`, re-raised after cleanup finishes.
"""
first_error: BaseException | None = None
for transformer in self._transformers:
try:
transformer.finalize()
except BaseException as e:
if first_error is None:
first_error = e
for log in self._logs:
if not log._closed:
log.close()
for ch in self._channels:
if not ch._log._closed:
ch._close()
self._events.close()
if first_error is not None:
raise first_error
def fail(self, err: BaseException) -> None:
"""Fail all transformers, projections, and the main log.
EventLogs and StreamChannels discovered in transformer
projections are auto-failed transformers don't need to fail
them manually. If any transformer's `fail()` raises, the
remaining transformers, projections, and the main log are still
failed.
Args:
err: The exception that ended the run.
"""
for transformer in self._transformers:
try:
transformer.fail(err)
except BaseException:
pass
for log in self._logs:
if not log._closed:
log.fail(err)
for ch in self._channels:
if not ch._log._closed:
ch._fail(err)
self._events.fail(err)
# ------------------------------------------------------------------
# Async dispatch
# ------------------------------------------------------------------
async def apush(self, event: ProtocolEvent) -> None:
"""Dispatch an event on the async lane.
Awaits each transformer's `aprocess` in registration order
before appending to the main log except when the transformer
has `scope_exact = True` and the event's namespace differs from
`self.scope`, in which case it is skipped. A slow `aprocess`
serializes the pipeline by design that's the guarantee that
lets a later transformer (or a synchronous consumer) see the
result of the async work. For decoupled work, use `schedule()`
from inside `process` / `aprocess` instead.
The main log append is a non-blocking `push` matching v1's
`put_nowait` shape. Memory is bounded by caller pace via the
caller-driven pump; see `EventLog` for the full tradeoff story.
Args:
event: The protocol event to dispatch.
"""
ns = tuple(event["params"]["namespace"])
in_scope = ns == self.scope
keep = True
for transformer in self._transformers:
if transformer.scope_exact and not in_scope:
continue
if not await transformer.aprocess(event):
keep = False
if keep:
self._seq += 1
event["seq"] = self._seq
self._events.push(event)
async def aclose(self) -> None:
"""Finalize on the async lane.
Awaits every task started via `StreamTransformer.schedule()`
across all transformers, then calls `afinalize()` on each,
then auto-closes logs, channels, and the main event log.
If any scheduled task raised under `on_error="raise"`, or any
transformer's `afinalize` raises, the exception propagates.
The caller (the pump) handles it by routing into `afail`.
Raises:
BaseException: The first scheduled-task or `afinalize`
error, re-raised after cleanup.
"""
pending = self._collect_scheduled_tasks()
if pending:
results = await asyncio.gather(*pending, return_exceptions=True)
first_err = next(
(
r
for r in results
if isinstance(r, BaseException)
and not isinstance(r, asyncio.CancelledError)
),
None,
)
if first_err is not None:
raise first_err
first_error: BaseException | None = None
for transformer in self._transformers:
try:
await transformer.afinalize()
except BaseException as e:
if first_error is None:
first_error = e
for log in self._logs:
if not log._closed:
log.close()
for ch in self._channels:
if not ch._log._closed:
ch._close()
self._events.close()
if first_error is not None:
raise first_error
async def afail(self, err: BaseException) -> None:
"""Fail on the async lane.
Cancels every scheduled task across all transformers, awaits
them to completion, then runs each transformer's `afail` hook
and auto-fails logs, channels, and the main event log.
Args:
err: The exception that ended the run.
"""
pending = self._collect_scheduled_tasks()
for task in pending:
task.cancel()
if pending:
await asyncio.gather(*pending, return_exceptions=True)
for transformer in self._transformers:
try:
await transformer.afail(err)
except BaseException:
pass
for log in self._logs:
if not log._closed:
log.fail(err)
for ch in self._channels:
if not ch._log._closed:
ch._fail(err)
if not self._events._closed:
self._events.fail(err)
def _collect_scheduled_tasks(self) -> list[asyncio.Task[Any]]:
"""Return a snapshot of in-flight tasks scheduled via transformers."""
return [
task
for transformer in self._transformers
for task in getattr(transformer, "_stream_scheduled_tasks", ())
if not task.done()
]
# ------------------------------------------------------------------
# Binding and StreamChannel auto-wiring
# ------------------------------------------------------------------
def _bind_and_wire(self, projection: dict[str, Any]) -> None:
"""Bind and wire EventLog / StreamChannel instances in a projection."""
for value in projection.values():
if isinstance(value, StreamChannel):
value._bind(is_async=self._is_async)
self._channels.append(value)
channel_name = value.name
def _make_forward(name: str) -> Callable[[Any], None]:
def _forward(item: Any) -> None:
self._forward(name, item)
return _forward
value._wire(_make_forward(channel_name))
elif isinstance(value, EventLog):
value._bind(is_async=self._is_async)
self._logs.append(value)
def _forward(self, channel_name: str, item: Any) -> None:
"""Inject a ProtocolEvent for a StreamChannel push.
Forwarded events bypass the transformer pipeline to avoid
infinite recursion (a transformer that pushes to a channel
during `process()` would re-trigger itself). These events are
visible in the main event log but are not passed through
transformers' `process()` methods.
"""
event: ProtocolEvent = {
"type": "event",
"method": f"custom:{channel_name}",
"params": {
"namespace": [],
"timestamp": int(time.time() * 1000),
"data": item,
},
}
self.emit(event)
-312
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@@ -1,312 +0,0 @@
from __future__ import annotations
import asyncio
import logging
from abc import ABC, abstractmethod
from collections.abc import Coroutine
from typing import Any, ClassVar, Literal
from typing_extensions import NotRequired, TypedDict
_logger = logging.getLogger(__name__)
class _ProtocolEventParams(TypedDict):
"""Parameters for a protocol event.
`timestamp` is wall-clock milliseconds since the epoch and can go
backwards across NTP adjustments use `ProtocolEvent.seq` for
ordering.
"""
namespace: list[str]
timestamp: int
data: Any
node: NotRequired[str]
run_id: NotRequired[str]
interrupts: NotRequired[tuple[Any, ...]]
class ProtocolEvent(TypedDict):
"""A protocol event emitted by the streaming infrastructure.
Wraps a raw stream part (values, messages, custom, etc.) in a uniform
envelope with a monotonic sequence number assigned by the StreamMux.
Consumers that need a total order across events should use `seq`, not
`params.timestamp` (which is wall-clock and not monotonic).
"""
type: Literal["event"]
event_id: NotRequired[str]
seq: NotRequired[int]
method: str # StreamMode value: "values", "messages", "custom", etc.
params: _ProtocolEventParams
class StreamTransformer(ABC):
"""Extension point for custom stream projections.
Transformers observe protocol events flowing through the StreamMux and
build typed derived projections (EventLogs, StreamChannels, promises,
etc.).
Set `_native = True` on a transformer to have its projection keys
exposed as direct attributes on the run stream (in addition to
appearing in `run.extensions`).
Subclasses must implement `init` and override at least one of
`process` / `aprocess`. The `finalize` / `afinalize` and `fail` /
`afail` hooks are optional the default implementations are no-ops.
EventLog and StreamChannel instances in the projection dict are
auto-closed / auto-failed by the mux, so most transformers don't
need `finalize` or `fail` at all.
Transformers that need async work pick the async lane by:
1. Overriding `aprocess` (and optionally `afinalize` / `afail`), or
2. Calling `self.schedule(coro)` from inside a sync `process`, or
3. Setting `requires_async = True` explicitly.
The mux detects these cases at registration and raises if they're
used under sync `stream()` they only work under `astream()`.
Use `aprocess` when the pump must wait for async work before the
next transformer sees the event (e.g. PII redaction that mutates
`event` in place). Use `schedule()` for decoupled async work whose
result lands on an independent projection (e.g. async moderation
scoring, cost lookup, external tracing).
Attributes:
scope: Namespace the transformer operates within `()` for the
root mux, a subgraph's namespace tuple inside a mini-mux.
Set at construction from the mux's scope (each factory is
called as `factory(scope)`). Transformers that only care
about events at their own namespace compare against
`self.scope`; subgraph-aware transformers can treat it as
a parent path.
scope_exact: If True (the default), the mux only calls
`process` / `aprocess` for events whose namespace equals
`self.scope` user transformers get scope-scoped events
for free with no boilerplate. Set False for transformers
that need to see events across scopes (e.g.
`SubgraphTransformer` forwards deeper events into child
mini-muxes).
requires_async: Explicit opt-in for transformers that need a
running event loop but don't override any async method (for
example, transformers that call `schedule()` from a sync
`process`). The mux also auto-detects the async lane when
`aprocess`, `afinalize`, or `afail` is overridden.
required_stream_modes: Stream modes the graph must emit for
this transformer to have anything to process. Computed as
the union across all registered transformers to determine
which modes a `GraphStreamer` run requests from the
graph. Empty tuple means the transformer consumes only
synthetic events (or is purely passive).
"""
requires_async: ClassVar[bool] = False
scope_exact: ClassVar[bool] = True
required_stream_modes: ClassVar[tuple[str, ...]] = ()
def __init__(self, scope: tuple[str, ...] = ()) -> None:
"""Initialize the transformer with its mux's scope.
Args:
scope: The namespace tuple the owning mux is scoped to.
`()` for the root, the subgraph's namespace inside a
mini-mux. Factories receive this at construction time
(`factory(scope)` in `StreamMux`).
"""
self.scope: tuple[str, ...] = scope
@abstractmethod
def init(self) -> dict[str, Any]:
"""Return the projection dict.
Keys become entries in `run.extensions`. If the transformer has
`_native = True`, keys are also set as direct attributes on the
run stream.
StreamChannel instances in the return value are automatically
wired by the StreamMux for protocol event auto-forwarding.
"""
...
def process(self, event: ProtocolEvent) -> bool:
"""Handle an event on the sync lane.
Called for every event before it is appended to the main event
log. Subclasses must override either `process` or `aprocess`.
The default raises so a missing override fails loudly rather
than silently passing every event through.
Args:
event: The protocol event to observe.
Returns:
True to keep the event in the main log, False to suppress it.
"""
raise NotImplementedError(
f"{type(self).__name__} must override process() or aprocess()"
)
async def aprocess(self, event: ProtocolEvent) -> bool:
"""Handle an event on the async lane.
The mux awaits this before dispatching to the next transformer,
so a slow `aprocess` serializes the pipeline. Use it only when
a later transformer or a consumer reading the event
synchronously must see the result of the async work (e.g.
PII redaction that mutates `event` in place).
The default delegates to `process`, so purely-sync transformers
run unchanged under `astream()`.
Args:
event: The protocol event to observe.
Returns:
True to keep the event in the main log, False to suppress it.
"""
return self.process(event)
def finalize(self) -> None:
"""Called when the run ends normally (sync lane).
Override to close EventLogs, resolve promises, or perform other
teardown. StreamChannel instances are auto-closed by the mux.
"""
async def afinalize(self) -> None:
"""Called when the run ends normally (async lane).
By the time this runs, the mux has already awaited every task
started via `schedule()`, so EventLogs can be closed here
without a last-task-wins race.
The default delegates to `finalize`.
"""
self.finalize()
def fail(self, err: BaseException) -> None:
"""Called when the run ends with an error (sync lane).
Override to fail EventLogs, reject promises, or perform other
teardown. StreamChannel instances are auto-failed by the mux.
Args:
err: The exception that ended the run.
"""
async def afail(self, err: BaseException) -> None:
"""Called when the run ends with an error (async lane).
The mux cancels and awaits every task started via `schedule()`
before calling this, so cleanup doesn't race with in-flight work.
The default delegates to `fail`.
Args:
err: The exception that ended the run.
"""
self.fail(err)
# ------------------------------------------------------------------
# Scheduled async work
# ------------------------------------------------------------------
def schedule(
self,
coro: Coroutine[Any, Any, Any],
*,
on_error: Literal["log", "raise"] = "log",
) -> asyncio.Task[Any]:
"""Schedule a coroutine tied to this transformer's lifecycle.
The mux holds the task reference, awaits all scheduled tasks
during `aclose()` before calling `afinalize()`, and cancels
them on `afail()`. Authors don't need to track tasks or
implement the last-task-closes-the-log dance.
Requires a running event loop call only under `astream()`.
Set `requires_async = True` on the class so registration under
sync `stream()` fails fast with a clear message.
Args:
coro: The coroutine to run. Its lifecycle is owned by the
mux from this point on.
on_error: `"log"` (default) catches and logs any exception
the coroutine raises, so a single failure doesn't tear
down the run. `"raise"` lets the exception propagate
when the mux joins pendings, converting the close path
into the fail path.
Returns:
The asyncio Task. Authors rarely need to await it directly
consumers read results from whatever projection the
coroutine pushes into.
Raises:
RuntimeError: If called without a running event loop (i.e.
under sync `stream()` rather than `astream()`).
"""
try:
asyncio.get_running_loop()
except RuntimeError:
raise RuntimeError(
f"{type(self).__name__}.schedule() requires a running "
"event loop; this transformer must run under astream(), "
"not stream(). Set requires_async=True on the class so "
"this fails at registration rather than at first event."
) from None
wrapped = self._wrap_scheduled(coro) if on_error == "log" else coro
task = asyncio.create_task(wrapped)
tasks = self._scheduled_task_set()
tasks.add(task)
task.add_done_callback(tasks.discard)
return task
@staticmethod
async def _wrap_scheduled(coro: Coroutine[Any, Any, Any]) -> Any:
try:
return await coro
except asyncio.CancelledError:
raise
except BaseException:
_logger.exception("Scheduled StreamTransformer task failed")
def _scheduled_task_set(self) -> set[asyncio.Task[Any]]:
"""Return the lazily-allocated task set.
Avoids requiring subclasses to call `super().__init__()`.
"""
tasks: set[asyncio.Task[Any]] | None = getattr(
self, "_stream_scheduled_tasks", None
)
if tasks is None:
tasks = set()
self._stream_scheduled_tasks = tasks
return tasks
def transformer_requires_async(transformer: StreamTransformer) -> bool:
"""Return True if the transformer needs a running event loop.
A transformer requires async if it explicitly opts in
(`requires_async = True`) or overrides any of the async-lane methods
(`aprocess`, `afinalize`, `afail`).
Args:
transformer: The transformer to inspect.
Returns:
True if the transformer cannot run under sync `stream()`.
"""
if transformer.requires_async:
return True
cls = type(transformer)
for name in ("aprocess", "afinalize", "afail"):
if getattr(cls, name) is not getattr(StreamTransformer, name):
return True
return False
@@ -1,412 +0,0 @@
from __future__ import annotations
import asyncio
from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping
from types import MappingProxyType, TracebackType
from typing import TYPE_CHECKING, Any
from langgraph.stream._convert import convert_to_protocol_event
from langgraph.stream._mux import StreamMux
from langgraph.stream._types import ProtocolEvent
if TYPE_CHECKING:
from langgraph.stream.transformers import ValuesTransformer
def _drive_until_done(pump: Callable[[], bool]) -> None:
"""Call the sync pump until it returns False."""
while pump():
pass
async def _adrive_until_done(pump: Callable[[], Awaitable[bool]]) -> None:
"""Call the async pump until it returns False."""
while await pump():
pass
class BaseRunStream:
"""Shared shape for any object that wraps a `StreamMux`.
Root (`GraphRunStream` / `AsyncGraphRunStream`) and scoped
(`SubgraphRunStream`) streams both compose a `StreamMux`. The mux
owns the projections `values`, `messages`, `subgraphs`, and any
user-registered keys all exposed via `extensions`. Native
projections (`_native = True`) are also bound as direct attributes
(`run.values`, `run.messages`, ) for ergonomics.
Raw iteration (`for event in run` / `async for event in run`) and
the `interleave(...)` helper both live here so every subclass
behaves consistently. Subclasses only add pump ownership, scope
metadata, or sync/async flavor.
"""
def __init__(self, mux: StreamMux) -> None:
self._mux = mux
self.extensions: Mapping[str, Any] = MappingProxyType(mux.extensions)
for key in mux.native_keys:
setattr(self, key, mux.extensions[key])
@property
def _values_transformer(self) -> ValuesTransformer:
"""Look up the `ValuesTransformer` backing `output` / `interrupted`.
Resolved lazily off the mux so subclasses don't have to thread
it through their constructors. Raises if no `ValuesTransformer`
is registered `output` / `interrupted` / `interrupts` have
nothing to return in that case, so failing loudly is better
than returning `None` silently.
"""
from langgraph.stream.transformers import ValuesTransformer
vt = self._mux.transformer_by_key("values")
if not isinstance(vt, ValuesTransformer):
raise RuntimeError(
"No ValuesTransformer is registered on this mux — "
"`output`, `interrupted`, and `interrupts` require one. "
"Add it to your GraphStreamer subclass's "
"`builtin_factories` or pass it via `transformers=`."
)
return vt
def __iter__(self) -> Iterator[ProtocolEvent]:
"""Sync iteration of protocol events on this mux's main log.
Raises at the EventLog level if the mux is async-bound.
"""
return iter(self._mux._events)
def __aiter__(self) -> AsyncIterator[ProtocolEvent]:
"""Async iteration of protocol events on this mux's main log.
Raises at the EventLog level if the mux is sync-bound.
"""
return self._mux._events.__aiter__()
def interleave(self, *names: str) -> Iterator[tuple[str, Any]]:
"""Iterate multiple projections round-robin, yielding ``(name, item)``.
Each turn advances one projection's cursor; when a cursor's
buffer is empty, pulling from it drives the pump once, which
fans out to every subscribed projection log. Projections whose
items aren't consumed on this turn sit in their own buffers
only until the next turn reaches them, bounding memory by the
skew between projection rates rather than letting any single
log grow to the full run length.
Projections are exhausted independently; a projection that
finishes early drops out of the rotation while others
continue. The overall iterator ends once all named projections
are done.
Args:
*names: Projection keys to interleave. Must match keys in
`extensions`.
Yields:
`(name, item)` tuples in round-robin order across the named
projections.
Raises:
KeyError: If a name doesn't match a registered projection.
Example:
```python
for name, item in run.interleave("messages", "values"):
if name == "messages":
print("msg:", item)
else:
print("val:", item)
```
"""
cursors: dict[str, Iterator[Any]] = {
name: iter(self.extensions[name]) for name in names
}
done: set[str] = set()
while len(done) < len(cursors):
for name, cursor in cursors.items():
if name in done:
continue
try:
item = next(cursor)
except StopIteration:
done.add(name)
continue
yield (name, item)
class GraphRunStream(BaseRunStream):
"""Sync run stream with caller-driven pumping.
The caller's iteration on any projection (`values`, `messages`,
raw events, or `output`) drives the graph forward. No background
thread is used the caller's `for` loop is the pump.
Projections are single-consumer iterating `run.values` twice
raises. Use `projection.tee(n)` if you genuinely need fan-out.
"""
def __init__(
self,
graph_iter: Iterator[Any],
mux: StreamMux,
) -> None:
"""Initialize the run stream.
Args:
graph_iter: Pull-based iterator over the graph's stream.
mux: The StreamMux owning projections and the main log.
Must have a `ValuesTransformer` registered under the
`"values"` key `output` / `interrupted` / `interrupts`
read from it lazily.
"""
super().__init__(mux)
self._graph_iter = graph_iter
self._exhausted = False
mux.bind_pump(self._pump_next)
def _pump_next(self) -> bool:
"""Pull one event from the graph and push it through the mux.
Returns:
True if an event was pulled, False if the graph is
exhausted or has raised.
"""
if self._exhausted:
return False
try:
part = next(self._graph_iter)
except StopIteration:
self._mux.close()
self._exhausted = True
return False
except Exception as e:
self._mux.fail(e)
self._exhausted = True
return False
self._mux.push(convert_to_protocol_event(part))
return True
def abort(self) -> None:
"""Stop the run early.
Closes the mux and marks the stream exhausted. The graph
iterator is dropped; any in-flight nodes see the closure on
their next yield point. Idempotent.
"""
if self._exhausted:
return
self._exhausted = True
try:
self._mux.close()
except Exception:
pass
def __enter__(self) -> GraphRunStream:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc: BaseException | None,
tb: TracebackType | None,
) -> None:
self.abort()
@property
def output(self) -> dict[str, Any] | None:
"""Drive the run to completion and return the final state."""
_drive_until_done(self._pump_next)
vt = self._values_transformer
if vt.error is not None:
raise vt.error
return vt._latest
@property
def interrupted(self) -> bool:
"""Drive the run to completion, then return whether it was interrupted.
Raises:
BaseException: If the run ended with an error.
"""
_drive_until_done(self._pump_next)
vt = self._values_transformer
if vt.error is not None:
raise vt.error
return vt._interrupted
@property
def interrupts(self) -> list[Any]:
"""Drive the run to completion, then return interrupt payloads.
Raises:
BaseException: If the run ended with an error.
"""
_drive_until_done(self._pump_next)
vt = self._values_transformer
if vt.error is not None:
raise vt.error
return vt._interrupts
class AsyncGraphRunStream(BaseRunStream):
"""Async run stream with caller-driven pumping.
Async iteration on any projection drives the graph forward there
is no background task. Concurrent consumers share a single-flight
pump via an `asyncio.Lock`, so each awaiting cursor contributes
one event per acquisition. Backpressure comes from the logs: when
a subscribed log's buffer reaches `maxlen`, `apush` awaits the
subscriber to drain, which holds back the pump and paces the
graph.
Projections are single-consumer a second `aiter(run.values)`
raises. Use `projection.tee(n)` for fan-out.
Use as an async context manager to guarantee clean shutdown on
early exit:
```python
async with await handler.astream(input) as run:
async for msg in run.messages:
...
```
"""
def __init__(
self,
graph_aiter: AsyncIterator[Any],
mux: StreamMux,
) -> None:
"""Initialize the async run stream.
Args:
graph_aiter: Async iterator over the graph's stream.
mux: The StreamMux owning projections and the main log.
Must have a `ValuesTransformer` registered under the
`"values"` key `output` / `interrupted` / `interrupts`
read from it lazily.
"""
super().__init__(mux)
self._graph_aiter = graph_aiter
self._exhausted = False
self._pump_cond = asyncio.Condition()
self._pumping = False
mux.bind_apump(self._apump_next)
async def _apump_next(self) -> bool:
"""Drive one pump step, or wait for the active pumper to drive one.
"Take-a-number" semantics: at most one task at a time calls
`graph_aiter.__anext__()` (asyncio iterators can't be advanced
concurrently). Other callers wait on a Condition that the
active pumper notifies after each step. This lets a "passive"
consumer one whose projection's buffer is being filled by the
active pumper's push — wake up as soon as its data lands,
instead of queueing on the pump and only observing its data one
graph event late.
`except Exception` is intentional `CancelledError` and other
`BaseException` subclasses propagate, matching asyncio's
cancellation contract.
Returns:
True if a pump step completed (by this task or another),
False if the graph is exhausted.
"""
async with self._pump_cond:
if self._exhausted:
return False
if self._pumping:
# Another task is pumping; wait for its progress signal.
await self._pump_cond.wait()
return not self._exhausted
self._pumping = True
try:
try:
part = await self._graph_aiter.__anext__()
except StopAsyncIteration:
self._exhausted = True
await self._mux.aclose()
return False
except Exception as e:
self._exhausted = True
await self._mux.afail(e)
return False
await self._mux.apush(convert_to_protocol_event(part))
return True
finally:
async with self._pump_cond:
self._pumping = False
self._pump_cond.notify_all()
async def abort(self) -> None:
"""Stop the run early.
Marks the stream exhausted, wakes any pump-waiters, and closes
the mux. Any `apush` blocked on backpressure wakes and returns
without appending. Idempotent.
"""
async with self._pump_cond:
if self._exhausted:
return
self._exhausted = True
self._pump_cond.notify_all()
try:
await self._mux.aclose()
except Exception:
pass
async def __aenter__(self) -> AsyncGraphRunStream:
return self
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc: BaseException | None,
tb: TracebackType | None,
) -> None:
await self.abort()
async def output(self) -> dict[str, Any] | None:
"""Drive the run to completion and return the final state.
Methods (not properties) on the async lane so `run.output`
without `await` raises at type-check time instead of silently
yielding a coroutine object.
Example:
```python
output = await run.output()
```
Raises:
BaseException: If the run ended with an error.
"""
await _adrive_until_done(self._apump_next)
if (err := self._values_transformer.error) is not None:
raise err
return self._values_transformer._latest
async def interrupted(self) -> bool:
"""Drive the run to completion and return whether it was interrupted.
Raises:
BaseException: If the run ended with an error.
"""
await _adrive_until_done(self._apump_next)
if (err := self._values_transformer.error) is not None:
raise err
return self._values_transformer._interrupted
async def interrupts(self) -> list[Any]:
"""Drive the run to completion and return interrupt payloads.
Raises:
BaseException: If the run ended with an error.
"""
await _adrive_until_done(self._apump_next)
if (err := self._values_transformer.error) is not None:
raise err
return self._values_transformer._interrupts
@@ -1,109 +0,0 @@
from __future__ import annotations
from collections.abc import AsyncIterator, Callable, Iterator
from typing import Generic, TypeVar
from langgraph.stream._event_log import EventLog
T = TypeVar("T")
class StreamChannel(Generic[T]):
"""A named projection channel with optional protocol auto-forwarding.
Wraps an event log and declares a protocol channel name. When the
StreamMux detects a StreamChannel in a transformer's `init()`
return value, it automatically wires every `push()` to inject a
`ProtocolEvent` into the main event stream using the channel's
name as the method.
Auto-forwarded events bypass the transformer pipeline other
transformers' `process()` / `aprocess()` methods do not see
`custom:<name>` events produced by a channel push. This prevents a
transformer that pushes to its own channel during `process()` from
re-triggering itself, but it also means filter- or tap-style
transformers cannot observe channel output from peer transformers.
Consumers that need that should iterate the main event stream.
In-process consumers iterate the channel directly (`for item in ch`
or `async for item in ch`). Remote SDK clients subscribe via
`session.subscribe("custom:<channelName>")`.
Like EventLog, a StreamChannel starts unbound. The mux calls
`_bind(is_async)` during registration so the correct iteration
protocol is available by the time user code sees it.
Lifecycle (`_close` / `_fail`) is managed by the mux transformers
using only StreamChannels don't need `finalize` or `fail` hooks.
"""
def __init__(self, name: str, *, maxlen: int | None = None) -> None:
"""Initialize the channel with an empty inner log.
Args:
name: The protocol channel name used for auto-forwarded
events (`custom:<name>` on the wire).
maxlen: Optional retention cap on the inner EventLog. See
`EventLog.__init__` for semantics.
"""
self.name = name
self._log: EventLog[T] = EventLog(maxlen=maxlen)
self._wire_fn: Callable[[T], None] | None = None
def _bind(self, *, is_async: bool) -> None:
"""Bind the underlying event log to sync or async mode.
Args:
is_async: True for async iteration, False for sync.
"""
self._log._bind(is_async=is_async)
def push(self, item: T) -> None:
"""Append an item to the log and auto-forward if wired.
Args:
item: The item to push.
"""
self._log.push(item)
if self._wire_fn is not None:
self._wire_fn(item)
# ------------------------------------------------------------------
# Mux lifecycle hooks (not called by transformers directly)
# ------------------------------------------------------------------
def _wire(self, fn: Callable[[T], None]) -> None:
"""Install the auto-forward callback (called by StreamMux)."""
self._wire_fn = fn
def _close(self) -> None:
"""Close the underlying log (called by StreamMux on run end)."""
self._log.close()
def _fail(self, err: BaseException) -> None:
"""Fail the underlying log (called by StreamMux on run error)."""
self._log.fail(err)
# ------------------------------------------------------------------
# Iteration — delegates to the inner event log (multi-cursor)
# ------------------------------------------------------------------
def __iter__(self) -> Iterator[T]:
return iter(self._log)
def __aiter__(self) -> AsyncIterator[T]:
return self._log.__aiter__()
def tee(self, n: int = 2) -> tuple[Iterator[T], ...]:
"""Fan out the channel into `n` independent sync iterators.
Delegates to the underlying EventLog's `tee()`.
"""
return self._log.tee(n)
def atee(self, n: int = 2) -> tuple[AsyncIterator[T], ...]:
"""Fan out the channel into `n` independent async iterators.
Delegates to the underlying EventLog's `atee()`.
"""
return self._log.atee(n)
@@ -1,750 +0,0 @@
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal, cast
from langchain_core.language_models._compat_bridge import message_to_events
from langchain_core.language_models.chat_model_stream import (
AsyncChatModelStream,
ChatModelStream,
)
from langchain_core.messages import AIMessageChunk, BaseMessage
from langchain_protocol.protocol import (
CheckpointRef,
LifecycleCause,
LifecycleData,
MessagesData,
)
from langgraph.errors import GraphInterrupt
from langgraph.stream._event_log import EventLog
from langgraph.stream._types import ProtocolEvent, StreamTransformer
from langgraph.stream.run_stream import BaseRunStream
if TYPE_CHECKING:
from collections.abc import Awaitable, Callable
from langgraph.stream._mux import StreamMux
logger = logging.getLogger(__name__)
SubgraphStatus = Literal["started", "running", "completed", "failed", "interrupted"]
_TERMINAL_STATUSES: frozenset[SubgraphStatus] = frozenset(
{"completed", "failed", "interrupted"}
)
def _is_record(value: Any) -> bool:
return isinstance(value, dict)
def _to_chat_model_stream_event(event: MessagesData) -> MessagesData:
"""Convert wire-shaped message fields to ChatModelStream's internal shape."""
event_type = event.get("event")
converted: dict[str, Any] = dict(event)
if (
event_type == "message-start"
and "message_id" not in converted
and isinstance(converted.get("id"), str)
):
converted["message_id"] = converted["id"]
if (
event_type in ("content-block-start", "content-block-delta", "content-block-finish")
and "content_block" not in converted
and isinstance(converted.get("content"), dict)
):
converted["content_block"] = converted["content"]
return cast("MessagesData", converted)
def _message_event_id(event: MessagesData) -> str | None:
raw_id = event.get("id") or event.get("message_id")
return str(raw_id) if raw_id is not None else None
def _content_block_start_skeleton(content: Any) -> dict[str, Any] | None:
"""Return a minimal content-block-start payload for a delta/finish block."""
if not _is_record(content) or not isinstance(content.get("type"), str):
return None
block_type = content["type"]
skeleton: dict[str, Any] = {"type": block_type}
if block_type == "text":
skeleton["text"] = ""
elif block_type == "reasoning":
skeleton["reasoning"] = ""
elif block_type in ("tool_call", "tool_call_chunk"):
skeleton["type"] = "tool_call_chunk"
if isinstance(content.get("id"), str):
skeleton["id"] = content["id"]
if isinstance(content.get("name"), str):
skeleton["name"] = content["name"]
skeleton["args"] = ""
elif block_type in ("server_tool_call", "server_tool_call_chunk"):
skeleton["type"] = "server_tool_call_chunk"
if isinstance(content.get("id"), str):
skeleton["id"] = content["id"]
if isinstance(content.get("name"), str):
skeleton["name"] = content["name"]
skeleton["args"] = ""
return skeleton
def _copy_event(
source: ProtocolEvent,
*,
method: str,
namespace: list[str],
data: Any,
) -> ProtocolEvent:
params = {**source["params"], "namespace": namespace, "data": data}
return {"type": "event", "method": method, "params": params}
def _message_repair_key(event: ProtocolEvent, run_id: str) -> str:
namespace_key = "\x1f".join(event["params"]["namespace"])
return f"{namespace_key}\x1e{run_id}"
def _extract_tool_calls_from_values(data: Any) -> dict[str, dict[str, Any]]:
if not _is_record(data):
return {}
messages = data.get("messages")
if not isinstance(messages, list):
return {}
known: dict[str, dict[str, Any]] = {}
for message in messages:
if not _is_record(message):
continue
tool_calls = message.get("tool_calls")
if not isinstance(tool_calls, list):
continue
for tool_call in tool_calls:
if not _is_record(tool_call):
continue
tool_call_id = tool_call.get("id")
if not isinstance(tool_call_id, str):
continue
name = tool_call.get("name")
args = tool_call.get("args")
known[tool_call_id] = {
"tool_name": name if isinstance(name, str) else "",
"input": args if _is_record(args) else {},
}
return known
class ValuesTransformer(StreamTransformer):
"""Capture values events as a drainable stream of state snapshots.
Keeps `_latest` / `_interrupted` / `_interrupts` as scalar state
regardless of whether the log has a subscriber so `run.output()`
and `run.interrupted` work without forcing the caller to iterate
`run.values`. Log pushes are silent no-ops when unsubscribed.
Native transformer projection keys are exposed as direct
attributes on the run stream (e.g. `run.values`).
`scope` (inherited from `StreamTransformer`) is the namespace the
transformer captures values for. `()` matches the root graph;
subgraph mini-muxes pass their subgraph's namespace, so each
instance sees only its own level.
"""
_native = True
required_stream_modes = ("values",)
def __init__(self, scope: tuple[str, ...] = ()) -> None:
super().__init__(scope)
self._log: EventLog[dict[str, Any]] = EventLog()
self._latest: dict[str, Any] | None = None
self._interrupted = False
self._interrupts: list[Any] = []
def init(self) -> dict[str, Any]:
return {"values": self._log}
@property
def error(self) -> BaseException | None:
"""The error that ended the run, or `None` if it succeeded.
Set by the mux when it auto-fails the projection log.
"""
return self._log._error
def process(self, event: ProtocolEvent) -> bool:
# Namespace filtering is handled by the mux via `scope_exact`.
if event["method"] != "values":
return True
params = event["params"]
self._latest = params["data"]
interrupts = params.get("interrupts", ())
if interrupts:
self._interrupted = True
self._interrupts.extend(interrupts)
self._log.push(params["data"])
return True
class ToolLifecycleTransformer(StreamTransformer):
"""Repair tool-start events needed for deterministic subagent discovery.
Some subagent frameworks expose a tool-caused subgraph lifecycle before
a LangChain tool callback has emitted the matching `tool-started`
frame. Core can infer the missing start from the latest values snapshot
(`messages[*].tool_calls`) and emit it before the lifecycle event leaves
the mux, keeping remote clients from guessing from values snapshots.
"""
scope_exact = False
required_stream_modes = ("values", "tools", "lifecycle")
def __init__(self, scope: tuple[str, ...] = ()) -> None:
super().__init__(scope)
self._known_tool_calls: dict[str, dict[str, Any]] = {}
self._emitted_tool_starts: set[str] = set()
self._mux: StreamMux | None = None
def init(self) -> dict[str, Any]:
return {}
def _on_register(self, mux: StreamMux) -> None:
self._mux = mux
def process(self, event: ProtocolEvent) -> bool:
method = event["method"]
data = event["params"]["data"]
if method == "values":
self._known_tool_calls.update(_extract_tool_calls_from_values(data))
return True
if method == "tools" and _is_record(data):
if (
data.get("event") == "tool-started"
and isinstance(data.get("tool_call_id"), str)
):
tool_call_id = cast("str", data["tool_call_id"])
if tool_call_id in self._emitted_tool_starts:
return False
self._emitted_tool_starts.add(tool_call_id)
return True
if method == "lifecycle":
self._emit_missing_tool_started(event)
return True
def _emit_missing_tool_started(self, event: ProtocolEvent) -> None:
if self._mux is None:
return
data = event["params"]["data"]
if not _is_record(data) or data.get("event") != "started":
return
cause = data.get("cause")
if not _is_record(cause) or cause.get("type") != "toolCall":
return
tool_call_id = cause.get("tool_call_id")
if not isinstance(tool_call_id, str):
return
if tool_call_id in self._emitted_tool_starts:
return
known = self._known_tool_calls.get(tool_call_id)
if known is None:
return
self._emitted_tool_starts.add(tool_call_id)
namespace = event["params"]["namespace"]
self._mux.emit(
_copy_event(
event,
method="tools",
namespace=namespace[:-1],
data={
"event": "tool-started",
"tool_call_id": tool_call_id,
"tool_name": known["tool_name"],
"input": known["input"],
},
)
)
class MessagesTransformer(StreamTransformer):
"""Capture messages events as ChatModelStream objects.
The messages projection yields one `ChatModelStream` (or
`AsyncChatModelStream`) per LLM call. Consumers iterate
`run.messages` to get stream handles, then use each handle's typed
projections (`.text`, `.reasoning`, `.tool_calls`, `.usage`,
`.output`) for per-message content.
Two input shapes are handled (via `params["data"] = (payload,
metadata)` from `StreamMessagesHandler`):
1. Protocol event (dict with `"event"` key) emitted by
`stream_v2()` / `astream_v2()` via the `on_stream_event`
callback. Routed to an existing `ChatModelStream` by
`metadata["run_id"]`. A `message-start` event creates a new
stream; `message-finish` closes it.
2. Whole `AIMessage` emitted from `on_chain_end` when a node
returns a finalized message. Replayed as a synthetic protocol
event lifecycle via `message_to_events`, then the
already-complete stream is pushed to the log.
V1 `AIMessageChunk` tuples (from `on_llm_new_token`) are not
streamed into this projection: chat models that want to populate
`run.messages` with content-block streaming must use
`stream_v2()` / `astream_v2()`. Models called via the legacy
`stream()` method still surface their final `AIMessage` via
`on_chain_end` when a node returns it as state.
`scope` (inherited from `StreamTransformer`) is the namespace the
transformer captures messages for. `()` matches the root graph;
subgraph mini-muxes pass their subgraph's namespace, so each
instance sees only its own level.
Native transformer the `messages` projection is exposed as a
direct attribute on the run stream.
`scope_exact = False`: matches events at the transformer's own
namespace **or** exactly one segment deeper (the chat-model /
node's own task ns). Mirrors JS's root-feed filter
(`namespaces=[[]], depth=1`) root accepts depth-0 events plus
its own nodes' depth-1 tokens; subgraph mini-muxes accept their
own scope plus their internal nodes' tokens. Events deeper than
scope + 1 are dropped (the enclosing `SubgraphTransformer` has
already forwarded them to the matching child mini-mux).
"""
_native = True
scope_exact = False
required_stream_modes = ("messages",)
def __init__(self, scope: tuple[str, ...] = ()) -> None:
super().__init__(scope)
self._log: EventLog[ChatModelStream] = EventLog()
# Correlate protocol events back to a ChatModelStream by run_id
# (attached to the event's metadata by StreamMessagesHandler).
self._by_run: dict[str, ChatModelStream] = {}
self._started_blocks: dict[str, set[int]] = {}
self._mux: StreamMux | None = None
self._pump_fn: Callable[[], bool] | None = None
self._apump_fn: Callable[[], Awaitable[bool]] | None = None
def init(self) -> dict[str, Any]:
return {"messages": self._log}
def _on_register(self, mux: StreamMux) -> None:
self._mux = mux
def _bind_pump(self, fn: Callable[[], bool]) -> None:
"""Wire the sync pull callback. Called by GraphRunStream._wire_request_more."""
self._pump_fn = fn
def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
"""Wire the async pull callback.
Called by `AsyncGraphRunStream._wire_arequest_more` so each
`AsyncChatModelStream` this transformer creates can drive the
shared graph pump from its projection cursors.
"""
self._apump_fn = fn
def _make_stream(
self,
*,
namespace: list[str],
node: str | None,
message_id: str | None,
) -> ChatModelStream:
"""Create a ChatModelStream (sync) or AsyncChatModelStream (async).
Wires whichever pump is bound. Prefers the async pump so nested
iteration under `AsyncGraphRunStream` drives the graph forward
without a background task. The unwired fallback (no pump bound)
is used by unit tests that dispatch events manually.
"""
if self._apump_fn is not None:
astream = AsyncChatModelStream(
namespace=namespace,
node=node,
message_id=message_id,
)
astream.set_arequest_more(self._apump_fn)
return astream
if self._pump_fn is not None:
stream: ChatModelStream = ChatModelStream(
namespace=namespace,
node=node,
message_id=message_id,
)
stream.set_request_more(self._pump_fn)
return stream
return AsyncChatModelStream(
namespace=namespace,
node=node,
message_id=message_id,
)
def process(self, event: ProtocolEvent) -> bool:
if event["method"] != "messages":
return True
params = event["params"]
# Accept events at our scope or exactly one segment deeper
# (the chat-model / node's own task ns). Deeper events belong
# to a subgraph and are routed by `SubgraphTransformer`.
ns = tuple(params["namespace"])
depth = len(self.scope)
if ns[:depth] != self.scope:
return True
raw_data = params["data"]
metadata: dict[str, Any] = {}
if isinstance(raw_data, tuple) and len(raw_data) == 2:
payload, raw_metadata = raw_data
metadata = raw_metadata if isinstance(raw_metadata, dict) else {}
else:
payload = raw_data
node = params.get("node")
if not isinstance(node, str):
node = metadata.get("langgraph_node")
if not isinstance(node, str):
node = None
raw_run_id = params.get("run_id", metadata.get("run_id"))
run_id = str(raw_run_id) if raw_run_id is not None else ""
if isinstance(payload, dict) and "event" in payload:
self._repair_content_block_lifecycle(
event, cast("MessagesData", payload), run_id=run_id
)
if len(ns) > depth + 1:
return True
self._route_protocol_event(
cast("MessagesData", payload), run_id=run_id, node=node
)
elif isinstance(payload, BaseMessage) and not isinstance(
payload, AIMessageChunk
):
self._route_whole_message(payload, node=node)
# Legacy AIMessageChunk tuples (from on_llm_new_token) are ignored;
# v1 streaming callers must switch to stream_v2() to populate this
# projection.
return True
def _route_protocol_event(
self,
event: MessagesData,
*,
run_id: str,
node: str | None,
) -> None:
stream_event = _to_chat_model_stream_event(event)
event_type = event.get("event")
if event_type == "message-start":
message_id = _message_event_id(event)
stream = self._make_stream(
namespace=list(self.scope),
node=node,
message_id=message_id,
)
self._by_run[run_id or message_id or ""] = stream
self._log.push(stream)
stream.dispatch(stream_event)
elif run_id in self._by_run:
stream = self._by_run[run_id]
stream.dispatch(stream_event)
if event_type == "message-finish":
del self._by_run[run_id]
def _repair_content_block_lifecycle(
self,
source: ProtocolEvent,
event: MessagesData,
*,
run_id: str,
) -> None:
if self._mux is None:
return
event_type = event.get("event")
key = _message_repair_key(source, run_id)
if event_type == "message-start":
self._started_blocks[key] = set()
return
if event_type == "content-block-start":
index = event.get("index")
if isinstance(index, int):
self._started_blocks.setdefault(key, set()).add(index)
return
if event_type in ("content-block-delta", "content-block-finish"):
index = event.get("index")
if not isinstance(index, int):
return
started = self._started_blocks.setdefault(key, set())
if index in started:
return
skeleton = _content_block_start_skeleton(event.get("content"))
if skeleton is None:
return
started.add(index)
self._mux.emit(
_copy_event(
source,
method="messages",
namespace=list(source["params"]["namespace"]),
data={
"event": "content-block-start",
"index": index,
"content": skeleton,
},
)
)
elif event_type == "message-finish":
self._started_blocks.pop(key, None)
def _route_whole_message(self, message: BaseMessage, *, node: str | None) -> None:
stream = self._make_stream(
namespace=list(self.scope),
node=node,
message_id=message.id,
)
for evt in message_to_events(message, message_id=message.id):
stream.dispatch(evt)
self._log.push(stream)
def finalize(self) -> None:
"""Clear any routing state — streams close themselves via `message-finish`."""
self._by_run.clear()
self._started_blocks.clear()
def fail(self, err: BaseException) -> None:
"""Propagate run error to any streams still open when the graph fails."""
for stream in list(self._by_run.values()):
stream.fail(err)
self._by_run.clear()
self._started_blocks.clear()
class SubgraphRunStream(BaseRunStream):
"""Scoped view of a single nested subgraph execution.
Yielded on `run.subgraphs` (or `parent.subgraphs` for grandchildren)
when a nested `Pregel` spawns. Wraps a mini-`StreamMux` built with
the same transformer factories as the root mux, so `.values`,
`.messages`, `.subgraphs` are populated by the standard
transformers scoped to this handle's namespace — no duplicated
routing logic. The mini-mux borrows the root's pump via
`make_child`'s pump inheritance, so any cursor on a subagent
projection drives the whole run forward.
Lifecycle fields update in place as events arrive:
- `path`: the namespace tuple stable for the life of the handle.
- `graph_name` / `cause`: set once from the `started` payload.
`cause` is populated by product-specific stream transformers
(see `LifecycleCause` in the protocol definition); pregel itself
emits no `cause`, so it may be `None` for subgraphs not covered
by a product transformer.
- `status`: advances `started` `running` `completed` /
`failed` / `interrupted`.
- `error` / `checkpoint`: set on the terminal event when present.
`.output` is a snapshot of the latest values seen at this
namespace it doesn't drive the pump (unlike root's
`GraphRunStream.output`), because advancing a subgraph to
completion is only meaningful as part of advancing the whole run.
"""
def __init__(
self,
path: tuple[str, ...],
mux: StreamMux,
*,
graph_name: str | None = None,
cause: LifecycleCause | None = None,
) -> None:
super().__init__(mux)
self.path: tuple[str, ...] = path
self.graph_name: str | None = graph_name
self.cause: LifecycleCause | None = cause
self.status: SubgraphStatus = "started"
self.error: str | None = None
self.checkpoint: CheckpointRef | None = None
@property
def output(self) -> dict[str, Any] | None:
"""Latest values snapshot at this namespace, or `None`.
Snapshot-only iterating other projections or the root's
`.output` is what drives the pump.
"""
values_t = self._mux.transformer_by_key("values")
if isinstance(values_t, ValuesTransformer):
return values_t._latest
return None
class SubgraphTransformer(StreamTransformer):
"""Discover subgraphs and route events into per-subgraph mini-muxes.
Thin state-machine + dispatcher. At its own `scope` (inherited
from `StreamTransformer`, determined by the enclosing mux), it
watches for `lifecycle` events at exactly one level deeper to
discover direct children. Each discovered child gets its own
`SubgraphRunStream` backed by a mini-`StreamMux` built via
`parent_mux.make_child(path)`, so the same factory list produces
fresh transformer instances at the child's scope.
Every incoming event that falls under one of the direct children
(ns starts with a child's `path`) is forwarded into that child's
mini-mux via `push`. The standard transformers in that mini-mux
(`ValuesTransformer`, `MessagesTransformer`, and another
`SubgraphTransformer` for grandchildren) handle the rest. No
duplicated routing or assembly logic.
Lifecycle state for each handle (running / completed / failed /
interrupted) is updated in place as events fire. On terminal
events, the handle's mini-mux is closed so any subscribed cursors
unblock. `finalize` / `fail` handle dangling handles left mid-run.
Native transformer `subgraphs` exposes the direct-children log.
`scope_exact = False`: this transformer sees events at any
namespace, because it forwards out-of-scope events to the matching
direct-child mini-mux.
"""
_native = True
scope_exact = False
required_stream_modes = ("lifecycle",)
def __init__(self, scope: tuple[str, ...] = ()) -> None:
super().__init__(scope)
self._root_log: EventLog[SubgraphRunStream] = EventLog()
# Direct children only (namespace = scope + one segment).
self._by_ns: dict[tuple[str, ...], SubgraphRunStream] = {}
self._mux: StreamMux | None = None
def init(self) -> dict[str, Any]:
return {"subgraphs": self._root_log}
def _on_register(self, mux: StreamMux) -> None:
"""Capture the enclosing mux so we can build child mini-muxes."""
self._mux = mux
def process(self, event: ProtocolEvent) -> bool:
ns = tuple(event["params"]["namespace"])
method = event["method"]
depth = len(self.scope)
# 1. On `started` for a direct child (ns depth = mine + 1 and
# ns prefix matches mine), register the handle.
if method == "lifecycle" and len(ns) == depth + 1 and ns[:-1] == self.scope:
data = cast(LifecycleData, event["params"]["data"])
if data.get("event") == "started":
self._on_started(ns, data)
# 2. Forward the event to the matching direct-child mini-mux
# before the status-change step below so that terminal events
# reach the child's log and grandchild transformers *before*
# the child's mini-mux is closed. Prefix-match: ns must start
# with some child's path.
direct_child_ns = ns[: depth + 1] if len(ns) > depth else None
if direct_child_ns is not None and direct_child_ns in self._by_ns:
self._by_ns[direct_child_ns]._mux.push(event)
# 3. Status change for a direct child (ns = child's path, method
# = lifecycle). Update handle fields, close mini-mux on
# terminal.
if (
method == "lifecycle"
and ns in self._by_ns
and len(ns) == depth + 1
and ns[:-1] == self.scope
):
data = cast(LifecycleData, event["params"]["data"])
event_type = data.get("event")
if event_type in ("running", "completed", "failed", "interrupted"):
self._on_status_change(ns, event_type, data)
return True
def _on_started(self, ns: tuple[str, ...], data: LifecycleData) -> None:
if ns in self._by_ns:
# Duplicate started — ignore.
return
# `_on_register` is called by the mux during registration, which
# happens before any event can be dispatched — so this should
# always be set by the time we process an event.
assert self._mux is not None, (
"SubgraphTransformer processed an event before _on_register; "
"transformer registration ordering is broken."
)
child_mux = self._mux.make_child(ns)
handle = SubgraphRunStream(
path=ns,
mux=child_mux,
graph_name=data.get("graph_name"),
cause=data.get("cause"),
)
self._by_ns[ns] = handle
self._root_log.push(handle)
def _on_status_change(
self,
ns: tuple[str, ...],
event_type: SubgraphStatus,
data: LifecycleData,
) -> None:
handle = self._by_ns[ns]
handle.status = event_type
err = data.get("error")
if err is not None:
handle.error = err
checkpoint = data.get("checkpoint")
if checkpoint is not None:
handle.checkpoint = checkpoint
if event_type in _TERMINAL_STATUSES:
self._close_handle_mux(handle)
@staticmethod
def _close_handle_mux(handle: SubgraphRunStream) -> None:
# Idempotent close — mux.close() runs finalize on its transformers
# (which cascades through grandchildren) and closes projection logs.
if not handle._mux._events._closed:
try:
handle._mux.close()
except Exception:
logger.warning(
"Error closing subgraph mini-mux at %s; subscribers "
"may not see a clean close.",
handle.path,
exc_info=True,
)
def finalize(self) -> None:
"""Transition any still-open direct children to `completed`."""
for handle in self._by_ns.values():
if handle.status not in _TERMINAL_STATUSES:
handle.status = "completed"
self._close_handle_mux(handle)
def fail(self, err: BaseException) -> None:
"""Transition any still-open direct children to `failed` / `interrupted`."""
is_interrupt = isinstance(err, GraphInterrupt)
terminal: SubgraphStatus = "interrupted" if is_interrupt else "failed"
error_str = None if is_interrupt else str(err)
for handle in self._by_ns.values():
if handle.status not in _TERMINAL_STATUSES:
handle.status = terminal
if error_str is not None and handle.error is None:
handle.error = error_str
if not handle._mux._events._closed:
try:
handle._mux.fail(err)
except Exception:
logger.warning(
"Error failing subgraph mini-mux at %s; subscribers "
"may not see the terminal error.",
handle.path,
exc_info=True,
)
+1 -11
View File
@@ -116,15 +116,7 @@ def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
StreamMode = Literal[
"values",
"updates",
"checkpoints",
"tasks",
"debug",
"messages",
"custom",
"lifecycle",
"tools",
"values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
]
"""How the stream method should emit outputs.
@@ -137,8 +129,6 @@ StreamMode = Literal[
- `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
- `"tasks"`: Emit events when tasks start and finish, including their results and errors.
- `"debug"`: Emit `"checkpoints"` and `"tasks"` events for debugging purposes.
- `"lifecycle"`: Emit subgraph lifecycle events (`started`, `running`, `completed`, `failed`, `interrupted`) with payloads matching `LifecycleData`.
- `"tools"`: Emit tool-call lifecycle events (`tool-started`, `tool-output-delta`, `tool-finished`, `tool-error`) keyed by `tool_call_id`.
"""
StreamWriter = Callable[[Any], None]
+2 -2
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
description = "Building stateful, multi-actor applications with LLMs"
authors = []
requires-python = ">=3.10"
@@ -24,7 +24,7 @@ classifiers = [
'Programming Language :: Python :: 3.13',
]
dependencies = [
"langchain-core>=1.3.2",
"langchain-core>=1.3.0,<2",
"langgraph-checkpoint>=2.1.0,<5.0.0",
"langgraph-sdk>=0.3.0,<0.4.0",
"langgraph-prebuilt>=1.0.9,<1.1.0",
@@ -275,3 +275,70 @@ def test_graph_callbacks_accept_base_callback_manager() -> None:
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
"""Non-GraphCallbackHandler added via add_handler should not raise.
Libraries like opentelemetry-instrumentation-langchain monkey-patch
BaseCallbackManager.__init__ and inject handlers via add_handler().
These handlers inherit from BaseCallbackHandler, not
GraphCallbackHandler. They must be silently accepted graph lifecycle
events will simply not be dispatched to them.
"""
from langgraph.callbacks import _GraphCallbackManager
manager = _GraphCallbackManager()
plain_handler = _LangChainCustomEventHandler()
manager.add_handler(plain_handler, inherit=True)
assert plain_handler in manager.handlers
def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
"""Non-GraphCallbackHandler added alongside a GraphCallbackHandler
should not interfere with lifecycle event dispatch."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers"},
"callbacks": [plain_handler, graph_handler],
}
first = graph.invoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = graph.invoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
"""Async variant: non-GraphCallbackHandler should not interfere."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
"callbacks": [plain_handler, graph_handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = await graph.ainvoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
+6 -3
View File
@@ -615,8 +615,11 @@ def test_run_from_checkpoint_id_retains_previous_writes(
)
]
assert len(new_history) == len(history) + 1
for original, new in zip(history, new_history[1:]):
# +2: one fork checkpoint from time travel, one from the new execution
assert len(new_history) == len(history) + 2
# new_history[0] is the new execution result, new_history[1] is the fork
assert new_history[1].metadata["source"] == "fork"
for original, new in zip(history, new_history[2:]):
assert original.values == new.values
assert original.next == new.next
assert original.metadata["step"] == new.metadata["step"]
@@ -624,7 +627,7 @@ def test_run_from_checkpoint_id_retains_previous_writes(
def _get_tasks(hist: list, start: int):
return [h.tasks for h in hist[start:]]
assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
def test_batch_two_processes_in_out() -> None:
+6 -3
View File
@@ -2086,8 +2086,11 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
)
]
assert len(new_history) == len(history) + 1
for original, new in zip(history, new_history[1:]):
# +2: one fork checkpoint from time travel, one from the new execution
assert len(new_history) == len(history) + 2
# new_history[0] is the new execution result, new_history[1] is the fork
assert new_history[1].metadata["source"] == "fork"
for original, new in zip(history, new_history[2:]):
assert original.values == new.values
assert original.next == new.next
assert original.metadata["step"] == new.metadata["step"]
@@ -2095,7 +2098,7 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
def _get_tasks(hist: list, start: int):
return [h.tasks for h in hist[start:]]
assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
async def test_cond_edge_after_send() -> None:
File diff suppressed because it is too large Load Diff
File diff suppressed because it is too large Load Diff
@@ -1,628 +0,0 @@
"""Tests for subgraph lifecycle events and the SubgraphTransformer."""
from __future__ import annotations
import operator
import time
from typing import Annotated, Any
import pytest
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.constants import END, START
from langgraph.errors import GraphInterrupt
from langgraph.graph import StateGraph
from langgraph.stream._event_log import EventLog
from langgraph.stream._mux import StreamMux
from langgraph.stream._types import ProtocolEvent
from langgraph.stream.transformers import (
MessagesTransformer,
SubgraphRunStream,
SubgraphTransformer,
ToolLifecycleTransformer,
ValuesTransformer,
)
from langgraph.types import interrupt
TS = int(time.time() * 1000)
def _lifecycle(
event: str,
*,
namespace: list[str] | None = None,
graph_name: str | None = None,
cause: dict[str, Any] | None = None,
error: str | None = None,
) -> ProtocolEvent:
data: dict[str, Any] = {"event": event}
if graph_name is not None:
data["graph_name"] = graph_name
if cause is not None:
data["cause"] = cause
if error is not None:
data["error"] = error
return {
"type": "event",
"method": "lifecycle",
"params": {
"namespace": namespace or [],
"timestamp": TS,
"data": data,
},
}
def _values(payload: dict[str, Any], *, namespace: list[str]) -> ProtocolEvent:
return {
"type": "event",
"method": "values",
"params": {
"namespace": namespace,
"timestamp": TS,
"data": payload,
},
}
def _subscribe(log: EventLog) -> None:
"""Flip `_subscribed = True` so pushes retain items for test inspection."""
log._subscribed = True
# ---------------------------------------------------------------------------
# Unit tests: feed events directly into the transformer
# ---------------------------------------------------------------------------
_FACTORIES = [
ValuesTransformer,
ToolLifecycleTransformer,
MessagesTransformer,
SubgraphTransformer,
]
def _handle_values_items(handle: SubgraphRunStream) -> list:
return list(handle._mux.extensions["values"]._items) # type: ignore[attr-defined]
def _handle_subgraphs_items(handle: SubgraphRunStream) -> list:
return list(handle._mux.extensions["subgraphs"]._items) # type: ignore[attr-defined]
def _pre_subscribe_handle(handle: SubgraphRunStream) -> None:
"""Flip `_subscribed` on every EventLog inside the handle's mini-mux.
The mini-mux is built via `make_child` with the full factory list,
so values / messages / subgraphs logs all exist as projections.
Tests that feed events directly need them subscribed so pushes
retain items in the deque for `_items` inspection.
"""
for value in handle._mux.extensions.values():
if isinstance(value, EventLog):
_subscribe(value)
class TestSubgraphTransformerUnit:
def _mux(self) -> tuple[StreamMux, SubgraphTransformer]:
mux = StreamMux(factories=_FACTORIES, is_async=False)
transformer = mux.transformer_by_key("subgraphs")
assert isinstance(transformer, SubgraphTransformer)
_subscribe(transformer._root_log)
return mux, transformer
def _handle(self, transformer: SubgraphTransformer) -> SubgraphRunStream:
"""Return the single root handle after pushing one lifecycle started."""
(handle,) = list(transformer._root_log._items)
return handle
def test_root_started_is_ignored(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", graph_name="root"))
assert list(transformer._root_log._items) == []
assert transformer._by_ns == {}
def test_child_started_yields_handle(self) -> None:
mux, transformer = self._mux()
mux.push(
_lifecycle(
"started",
namespace=["task_a:child"],
graph_name="child",
cause={"type": "toolCall", "tool_call_id": "call_abc"},
)
)
handle = self._handle(transformer)
assert handle.path == ("task_a:child",)
assert handle.graph_name == "child"
assert handle.cause == {"type": "toolCall", "tool_call_id": "call_abc"}
assert handle.status == "started"
def test_tool_started_is_synthesized_before_tool_caused_lifecycle(self) -> None:
mux, transformer = self._mux()
events = iter(mux._events)
mux.push(
_values(
{
"messages": [
{
"tool_calls": [
{
"id": "call_abc",
"name": "task",
"args": {"subagent_type": "researcher"},
}
]
}
]
},
namespace=[],
)
)
mux.push(
_lifecycle(
"started",
namespace=["task:child"],
graph_name="child",
cause={"type": "toolCall", "tool_call_id": "call_abc"},
)
)
mux.close()
tool_started, lifecycle_started = list(events)[1:3]
assert tool_started["method"] == "tools"
assert tool_started["params"]["namespace"] == []
assert tool_started["params"]["data"] == {
"event": "tool-started",
"tool_call_id": "call_abc",
"tool_name": "task",
"input": {"subagent_type": "researcher"},
}
assert lifecycle_started["method"] == "lifecycle"
assert tool_started["seq"] < lifecycle_started["seq"]
assert self._handle(transformer).path == ("task:child",)
def test_core_golden_trace_uses_js_wire_shape_and_ordering(self) -> None:
mux, _transformer = self._mux()
events = iter(mux._events)
mux.push(
_values(
{
"messages": [
{
"tool_calls": [
{
"id": "call_abc",
"name": "task",
"args": {"subagent_type": "researcher"},
}
]
}
]
},
namespace=[],
)
)
for data in (
{"event": "message-start", "id": "msg-1", "role": "ai"},
{
"event": "content-block-delta",
"index": 0,
"content": {"type": "text", "text": "hi"},
},
):
mux.push(
{
"type": "event",
"method": "messages",
"params": {
"namespace": ["call_model:task-1"],
"timestamp": TS,
"data": data,
"run_id": "run-1",
},
}
)
mux.push(
_lifecycle(
"started",
namespace=["task:child"],
graph_name="child",
cause={"type": "toolCall", "tool_call_id": "call_abc"},
)
)
mux.close()
trace = [
(event["method"], event["params"]["namespace"], event["params"]["data"])
for event in events
]
assert trace == [
(
"values",
[],
{
"messages": [
{
"tool_calls": [
{
"id": "call_abc",
"name": "task",
"args": {"subagent_type": "researcher"},
}
]
}
]
},
),
(
"messages",
["call_model:task-1"],
{"event": "message-start", "id": "msg-1", "role": "ai"},
),
(
"messages",
["call_model:task-1"],
{
"event": "content-block-start",
"index": 0,
"content": {"type": "text", "text": ""},
},
),
(
"messages",
["call_model:task-1"],
{
"event": "content-block-delta",
"index": 0,
"content": {"type": "text", "text": "hi"},
},
),
(
"tools",
[],
{
"event": "tool-started",
"tool_call_id": "call_abc",
"tool_name": "task",
"input": {"subagent_type": "researcher"},
},
),
(
"lifecycle",
["task:child"],
{
"event": "started",
"graph_name": "child",
"cause": {"type": "toolCall", "tool_call_id": "call_abc"},
},
),
]
def test_status_transitions(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
mux.push(_lifecycle("running", namespace=["t:c"]))
mux.push(_lifecycle("completed", namespace=["t:c"]))
handle = self._handle(transformer)
assert handle.status == "completed"
def test_grandchild_surfaces_under_child(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:child"], graph_name="child"))
child = self._handle(transformer)
_pre_subscribe_handle(child)
mux.push(
_lifecycle(
"started",
namespace=["t:child", "u:grand"],
graph_name="grand",
)
)
(grand,) = _handle_subgraphs_items(child)
assert grand.path == ("t:child", "u:grand")
assert grand.graph_name == "grand"
def test_failed_stores_error(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
mux.push(_lifecycle("failed", namespace=["t:c"], error="boom"))
handle = self._handle(transformer)
assert handle.status == "failed"
assert handle.error == "boom"
def test_values_routed_into_handle(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
handle = self._handle(transformer)
_pre_subscribe_handle(handle)
mux.push(_values({"value": 1}, namespace=["t:c"]))
mux.push(_values({"value": 2}, namespace=["t:c"]))
assert _handle_values_items(handle) == [{"value": 1}, {"value": 2}]
assert handle.output == {"value": 2}
def test_root_values_not_routed(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
handle = self._handle(transformer)
_pre_subscribe_handle(handle)
# Values event at root namespace — must not leak into child handle.
mux.push(_values({"value": "root"}, namespace=[]))
assert _handle_values_items(handle) == []
def test_finalize_closes_dangling(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
handle = self._handle(transformer)
mux.close()
assert handle.status == "completed"
assert handle._mux.extensions["values"]._closed
assert handle._mux.extensions["subgraphs"]._closed
def test_fail_with_graph_interrupt_marks_interrupted(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
handle = self._handle(transformer)
mux.fail(GraphInterrupt())
assert handle.status == "interrupted"
def test_fail_with_generic_error_marks_failed(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
handle = self._handle(transformer)
mux.fail(RuntimeError("explode"))
assert handle.status == "failed"
assert handle.error == "explode"
def test_duplicate_started_ignored(self) -> None:
mux, transformer = self._mux()
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="c"))
mux.push(_lifecycle("started", namespace=["t:c"], graph_name="other"))
handles = list(transformer._root_log._items)
assert len(handles) == 1
assert handles[0].graph_name == "c"
def test_non_lifecycle_non_values_passthrough(self) -> None:
mux, transformer = self._mux()
mux.push(
{
"type": "event",
"method": "messages",
"params": {
"namespace": ["t:c"],
"timestamp": TS,
"data": (
{"event": "message-start", "message_id": "m1"},
{"run_id": "m1"},
),
},
}
)
assert list(transformer._root_log._items) == []
# ---------------------------------------------------------------------------
# End-to-end tests via stream_v2 on real graphs
# ---------------------------------------------------------------------------
class SimpleState(TypedDict):
value: str
items: Annotated[list[str], operator.add]
def _build_nested_graph():
"""Parent graph with a compiled subgraph node."""
def inner_node(state: SimpleState) -> dict:
return {"value": state["value"] + "X", "items": ["x"]}
inner_builder = StateGraph(SimpleState)
inner_builder.add_node("inner_node", inner_node)
inner_builder.add_edge(START, "inner_node")
inner_builder.add_edge("inner_node", END)
inner = inner_builder.compile()
def outer_node(state: SimpleState) -> dict:
return {"value": state["value"] + "Y", "items": ["y"]}
outer_builder = StateGraph(SimpleState)
outer_builder.add_node("outer_node", outer_node)
outer_builder.add_node("sub", inner)
outer_builder.add_edge(START, "outer_node")
outer_builder.add_edge("outer_node", "sub")
outer_builder.add_edge("sub", END)
return outer_builder.compile()
class TestSubgraphTransformerEndToEnd:
def test_flat_graph_yields_no_subgraphs(self) -> None:
builder = StateGraph(SimpleState)
builder.add_node("n", lambda s: {"value": s["value"] + "!", "items": ["!"]})
builder.add_edge(START, "n")
builder.add_edge("n", END)
graph = builder.compile()
run = graph.stream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = []
for sub in run.subgraphs:
collected.append(sub)
assert collected == []
# Output still resolves.
assert run.output is not None
def test_nested_graph_yields_one_child(self) -> None:
graph = _build_nested_graph()
run = graph.stream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = []
for sub in run.subgraphs:
collected.append(sub)
assert len(collected) == 1
child = collected[0]
assert len(child.path) == 1
assert child.path[0].startswith("sub:")
assert child.status == "completed"
def test_error_in_subgraph_fails_child(self) -> None:
def boom(state: SimpleState) -> dict:
raise RuntimeError("subgraph_failed")
inner_builder = StateGraph(SimpleState)
inner_builder.add_node("inner", boom)
inner_builder.add_edge(START, "inner")
inner_builder.add_edge("inner", END)
inner = inner_builder.compile()
outer_builder = StateGraph(SimpleState)
outer_builder.add_node("sub", inner)
outer_builder.add_edge(START, "sub")
outer_builder.add_edge("sub", END)
graph = outer_builder.compile()
run = graph.stream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = []
with pytest.raises(RuntimeError):
for sub in run.subgraphs:
collected.append(sub)
assert len(collected) == 1
assert collected[0].status == "failed"
class TestSubgraphTransformerAsyncEndToEnd:
@pytest.mark.anyio
async def test_nested_graph_yields_one_child(self) -> None:
async def inner(state: SimpleState) -> dict:
return {"value": state["value"] + "X", "items": ["x"]}
inner_builder = StateGraph(SimpleState)
inner_builder.add_node("inner", inner)
inner_builder.add_edge(START, "inner")
inner_builder.add_edge("inner", END)
inner_graph = inner_builder.compile()
outer_builder = StateGraph(SimpleState)
outer_builder.add_node("sub", inner_graph)
outer_builder.add_edge(START, "sub")
outer_builder.add_edge("sub", END)
graph = outer_builder.compile()
run = await graph.astream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = []
async for sub in run.subgraphs:
collected.append(sub)
assert len(collected) == 1
child = collected[0]
assert child.status == "completed"
class TestSubgraphCause:
"""Pregel core emits no `cause`; product transformers populate it."""
def test_cause_not_populated_by_pregel(self) -> None:
graph = _build_nested_graph()
run = graph.stream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = list(run.subgraphs)
assert len(collected) == 1
child = collected[0]
# The child's single-segment path still encodes `node_name:task_id`
# (that's pregel's internal namespace format), but `cause` is now
# product-agnostic and must be populated by a stream transformer,
# not by pregel itself.
assert ":" in child.path[0]
node_name, _, task_id = child.path[0].partition(":")
assert node_name == "sub"
assert task_id # non-empty
assert child.cause is None
class TestSubgraphInterrupt:
"""Interrupts raised inside a subgraph surface as status=interrupted."""
def _build_interrupt_subgraph(self):
def inner_node(state: SimpleState) -> dict:
interrupt("need approval")
return {"value": state["value"] + "X", "items": ["x"]}
inner_builder = StateGraph(SimpleState)
inner_builder.add_node("inner_node", inner_node)
inner_builder.add_edge(START, "inner_node")
inner_builder.add_edge("inner_node", END)
inner = inner_builder.compile()
outer_builder = StateGraph(SimpleState)
outer_builder.add_node("sub", inner)
outer_builder.add_edge(START, "sub")
outer_builder.add_edge("sub", END)
return outer_builder.compile(checkpointer=InMemorySaver())
def test_interrupt_in_subgraph_marks_handle_interrupted(self) -> None:
graph = self._build_interrupt_subgraph()
run = graph.stream_v2(
{"value": "", "items": []},
config={"configurable": {"thread_id": "t1"}},
)
collected: list[SubgraphRunStream] = list(run.subgraphs)
assert run.interrupted is True
assert len(collected) == 1
assert collected[0].status == "interrupted"
class TestSubgraphNameCollision:
"""The subgraph's compiled `name` equaling its node name is detected.
Primary detector `name != langgraph_node` fails here; the
parent_run_id fallback in `_is_nested_pregel_start` is what keeps
the subgraph visible.
"""
def test_name_equals_node_name_still_detected(self) -> None:
def inner_node(state: SimpleState) -> dict:
return {"value": state["value"] + "X", "items": ["x"]}
inner_builder = StateGraph(SimpleState)
inner_builder.add_node("inner_node", inner_node)
inner_builder.add_edge(START, "inner_node")
inner_builder.add_edge("inner_node", END)
# Compile with the same name as the node it will be registered as.
inner = inner_builder.compile(name="sub")
outer_builder = StateGraph(SimpleState)
outer_builder.add_node("sub", inner)
outer_builder.add_edge(START, "sub")
outer_builder.add_edge("sub", END)
graph = outer_builder.compile()
run = graph.stream_v2({"value": "", "items": []})
collected: list[SubgraphRunStream] = list(run.subgraphs)
assert len(collected) == 1
child = collected[0]
assert child.graph_name == "sub"
assert child.status == "completed"
+793 -7
View File
@@ -37,6 +37,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
Returns a list of dicts (newest-first, matching get_state_history order) with:
- id: short checkpoint id suffix (last 6 chars)
- parent_id: short parent checkpoint id suffix or None
- source: checkpoint metadata source (input, loop, fork, update)
- next: tuple of next node names
- values: channel values snapshot
"""
@@ -52,6 +53,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
{
"id": cid[-6:],
"parent_id": pid[-6:] if pid else None,
"source": s.metadata.get("source"),
"next": s.next,
"values": s.values,
}
@@ -280,6 +282,116 @@ def test_replay_from_before_interrupt_refires(
assert call_count["node_b"] == 1 # NOT re-executed (after interrupt)
def test_replay_from_before_interrupt_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from checkpoint before interrupt node, then resume with a new
answer and verify the graph completes with the new value.
Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
Original run:
source=input next=(__start__,) values=[]
source=loop next=(node_a,) values=[]
source=loop next=(ask_human,) values=[a] <-- replay from here
source=loop next=(node_b,) values=[a, human:old_answer]
source=loop next=() values=[a, human:old_answer, b]
After replay (fork created) + resume with "new_answer":
source=input next=(__start__,) values=[]
source=loop next=(node_a,) values=[]
source=loop next=(ask_human,) values=[a] <-- branch point
source=loop next=(node_b,) values=[a, human:old_answer]
source=loop next=() values=[a, human:old_answer, b] (old branch)
source=fork next=(ask_human,) values=[a] <-- fork from branch point
source=loop next=(node_b,) values=[a, human:new_answer]
source=loop next=() values=[a, human:new_answer, b] (new branch)
"""
called: list[str] = []
def node_a(state: State) -> State:
called.append("node_a")
return {"value": ["a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("What is your input?")
return {"value": [f"human:{answer}"]}
def node_b(state: State) -> State:
called.append("node_b")
return {"value": ["b"]}
graph = (
StateGraph(State)
.add_node("node_a", node_a)
.add_node("ask_human", ask_human)
.add_node("node_b", node_b)
.add_edge(START, "node_a")
.add_edge("node_a", "ask_human")
.add_edge("ask_human", "node_b")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: invoke until interrupt, then resume to complete ---
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="old_answer"), config)
original_history = list(graph.get_state_history(config))
original = _checkpoint_summary(original_history)
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Replay from checkpoint before ask_human ---
before_ask = next(s for s in original_history if s.next == ("ask_human",))
called.clear()
replay_result = graph.invoke(None, before_ask.config)
assert replay_result["__interrupt__"][0].value == "What is your input?"
assert "ask_human" in called
assert "node_a" not in called # before the replay point, not re-executed
# A fork checkpoint is now the latest — it branches from the replay point
post_replay = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_replay] == [
("fork", ("ask_human",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("node_b",)),
("loop", ("ask_human",)), # branch point
("loop", ("node_a",)),
("input", ("__start__",)),
]
# --- Resume with a new answer ---
called.clear()
final_result = graph.invoke(Command(resume="new_answer"), config)
assert final_result["value"] == ["a", "human:new_answer", "b"]
assert "ask_human" in called
assert "node_b" in called
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from fork)
("loop", (), {"value": ["a", "human:new_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
("fork", ("ask_human",), {"value": ["a"]}),
# Original branch (preserved)
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_replay_interrupt_stable_across_replays(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -320,8 +432,14 @@ def test_replay_interrupt_stable_across_replays(
r = graph.invoke(None, before_ask.config)
results.append(r)
assert all(r == results[0] for r in results)
assert "__interrupt__" in results[0]
# Each replay creates a fork with a unique interrupt ID, so we compare
# interrupt values and state values rather than full equality.
assert all("__interrupt__" in r for r in results)
assert all(
r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
for r in results
)
assert all(r["value"] == results[0]["value"] for r in results)
def test_fork_from_before_interrupt_refires(
@@ -854,6 +972,356 @@ def test_subgraph_interrupt_replay_from_interrupt_checkpoint(
assert "step_b" not in called
def test_subgraph_interrupt_replay_from_parent_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from the parent checkpoint where a subgraph interrupt fired,
then resume with a new answer. Verifies that a fork is created and the
full graph completes. Checks full checkpoint history at each stage."""
called: list[str] = []
def router(state: State) -> State:
called.append("router")
return {"value": ["routed"]}
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["sub_a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("Provide input:")
return {"value": [f"human:{answer}"]}
def step_b(state: State) -> State:
called.append("step_b")
return {"value": ["sub_b"]}
subgraph = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_human", ask_human)
.add_node("step_b", step_b)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_human")
.add_edge("ask_human", "step_b")
.compile(checkpointer=True)
)
def post_process(state: State) -> State:
called.append("post_process")
return {"value": ["post"]}
graph = (
StateGraph(State)
.add_node("router", router)
.add_node("subgraph_node", subgraph)
.add_node("post_process", post_process)
.add_edge(START, "router")
.add_edge("router", "subgraph_node")
.add_edge("subgraph_node", "post_process")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then resume to complete
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="old_answer"), config)
# Original parent history (newest first)
original_history = list(graph.get_state_history(config))
assert [s.next for s in original_history] == [
(), # done
("post_process",),
("subgraph_node",), # subgraph ran, interrupt fired here
("router",),
("__start__",),
]
# Find the parent checkpoint where the interrupt fired
interrupt_checkpoint = next(
s for s in original_history if s.next == ("subgraph_node",)
)
# Replay from parent checkpoint — subgraph re-executes, interrupt re-fires
called.clear()
replay_result = graph.invoke(None, interrupt_checkpoint.config)
assert "__interrupt__" in replay_result
assert replay_result["__interrupt__"][0].value == "Provide input:"
assert "step_a" in called
assert "ask_human" in called
assert "step_b" not in called
# Verify fork checkpoint was created
post_replay_history = list(graph.get_state_history(config))
assert [s.next for s in post_replay_history] == [
("subgraph_node",), # fork (interrupt pending)
(), # original done
("post_process",),
("subgraph_node",),
("router",),
("__start__",),
]
assert [s.metadata["source"] for s in post_replay_history] == [
"fork",
"loop",
"loop",
"loop",
"loop",
"input",
]
fork = post_replay_history[0]
assert (
fork.parent_config["configurable"]["checkpoint_id"]
== interrupt_checkpoint.config["configurable"]["checkpoint_id"]
)
# Resume with a new answer — full graph should complete
called.clear()
final_result = graph.invoke(Command(resume="new_answer"), config)
assert "__interrupt__" not in final_result
assert "human:new_answer" in final_result["value"]
assert "sub_b" in final_result["value"]
assert "post" in final_result["value"]
assert "ask_human" in called
assert "step_b" in called
assert "post_process" in called
# Final checkpoint history
final_history = list(graph.get_state_history(config))
assert [s.next for s in final_history] == [
(), # new branch done
("post_process",), # new branch post_process
("subgraph_node",), # fork
(), # original done
("post_process",),
("subgraph_node",),
("router",),
("__start__",),
]
assert [s.metadata["source"] for s in final_history] == [
"loop",
"loop",
"fork",
"loop",
"loop",
"loop",
"loop",
"input",
]
def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Resume with Command(resume=...) plus the current head checkpoint_id
in config. The subgraph must continue from the interrupted node, not
restart from scratch. Explicit checkpoint_id triggers is_replaying but
this is a resume, not a time-travel, so ReplayState should not apply."""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["sub_a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("Provide input:")
return {"value": [f"human:{answer}"]}
def step_b(state: State) -> State:
called.append("step_b")
return {"value": ["sub_b"]}
subgraph = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_human", ask_human)
.add_node("step_b", step_b)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_human")
.add_edge("ask_human", "step_b")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("subgraph_node", subgraph)
.add_edge(START, "subgraph_node")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt fires in subgraph
graph.invoke({"value": []}, config)
assert called == ["step_a", "ask_human"]
# Resume with explicit head checkpoint_id in config
head_checkpoint_id = graph.get_state(config).config["configurable"][
"checkpoint_id"
]
called.clear()
resume_config = {
"configurable": {
"thread_id": "1",
"checkpoint_id": head_checkpoint_id,
"checkpoint_ns": "",
}
}
result = graph.invoke(Command(resume="answer"), resume_config)
assert called == ["ask_human", "step_b"]
assert "__interrupt__" not in result
assert result["value"] == ["sub_a", "human:answer", "sub_b"]
def test_subgraph_replay_loads_accumulated_state_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Two parent invocations, then replay from before the subgraph in the
2nd invocation. The subgraph (checkpointer=True) should load its
accumulated state from the 1st invocation via ReplayState, re-fire
the interrupt, and then resume + complete.
This tests the ReplayState path: the parent is replaying and the
subgraph uses list(before=parent_checkpoint_id) to find its
corresponding checkpoint from the original execution.
"""
class SubState(TypedDict):
value: Annotated[list[str], operator.add]
class ParentState(TypedDict):
results: Annotated[list[str], operator.add]
started_state: list[dict] = []
def step_a(state: SubState) -> SubState:
started_state.append(dict(state))
answer = interrupt("question_a")
return {"value": [f"a:{answer}"]}
subgraph = (
StateGraph(SubState)
.add_node("step_a", step_a)
.add_edge(START, "step_a")
.compile(checkpointer=True)
)
def parent_node(state: ParentState) -> ParentState:
return {"results": ["p"]}
graph = (
StateGraph(ParentState)
.add_node("parent_node", parent_node)
.add_node("sub_node", subgraph)
.add_edge(START, "parent_node")
.add_edge("parent_node", "sub_node")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# === 1st invocation: complete with answer "a1" ===
graph.invoke({"results": []}, config)
graph.invoke(Command(resume="a1"), config)
# step_a saw empty state (fresh subgraph)
assert started_state[0] == {"value": []}
# === 2nd invocation: complete with answer "a2" ===
started_state.clear()
graph.invoke({"results": []}, config)
graph.invoke(Command(resume="a2"), config)
# Stateful subgraph retained state from 1st invocation
assert started_state[0] == {"value": ["a:a1"]}
# Original history (newest first)
original_history = list(graph.get_state_history(config))
assert [s.next for s in original_history] == [
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
# Replay from before sub_node in 2nd invocation (newest match)
before_sub_2nd = [s for s in original_history if s.next == ("sub_node",)][0]
started_state.clear()
replay = graph.invoke(None, before_sub_2nd.config)
assert "__interrupt__" in replay
# Subgraph should see accumulated state from END of 1st invocation
assert started_state[0] == {"value": ["a:a1"]}
# Verify fork was created
post_replay_history = list(graph.get_state_history(config))
assert [s.next for s in post_replay_history] == [
("sub_node",), # fork (interrupt pending)
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
assert [s.metadata["source"] for s in post_replay_history] == [
"fork",
"loop",
"loop",
"loop",
"input",
"loop",
"loop",
"loop",
"input",
]
# Resume with a new answer
started_state.clear()
final = graph.invoke(Command(resume="a3"), config)
assert "__interrupt__" not in final
assert final["results"] == ["p", "p"]
# Final history
final_history = list(graph.get_state_history(config))
assert [s.next for s in final_history] == [
(), # new branch done
("sub_node",), # fork
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
assert [s.metadata["source"] for s in final_history] == [
"loop",
"fork",
"loop",
"loop",
"loop",
"input",
"loop",
"loop",
"loop",
"input",
]
def test_subgraph_interrupt_full_flow(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -1290,6 +1758,321 @@ def test_subgraph_time_travel_to_second_interrupt(
assert "ask_1" not in called
def test_subgraph_time_travel_resume_from_first_interrupt(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the first interrupt, then
resume through both interrupts with new answers.
This verifies the key bug fix: after time-traveling to a subgraph
checkpoint with an interrupt, a fork checkpoint is created so that
subsequent resumes find the correct state (not the old branch tip).
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
Parent history after original run completes:
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[]
source=loop next=() values=[step_a_done, ask_1:answer_1, ask_2:answer_2]
After time-traveling to 1st interrupt + resuming with new answers:
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- branch point
source=loop next=() values=[..., ask_2:answer_2] (old branch)
source=fork next=(executor,) values=[] <-- fork from time travel
source=loop next=() values=[..., ask_2:new_answer_2] (new branch)
"""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
graph.invoke({"value": []}, config)
sub_config_at_first = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="answer_1"), config)
graph.invoke(Command(resume="answer_2"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to first interrupt's subgraph checkpoint ---
called.clear()
replay_result = graph.invoke(None, sub_config_at_first)
assert replay_result["__interrupt__"][0].value == "Question 1?"
assert "step_a" not in called # before interrupt, not re-executed
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume both interrupts with new answers ---
called.clear()
resume_1 = graph.invoke(Command(resume="new_answer_1"), config)
assert resume_1["__interrupt__"][0].value == "Question 2?"
assert "ask_1" in called
called.clear()
resume_2 = graph.invoke(Command(resume="new_answer_2"), config)
assert resume_2["value"] == [
"step_a_done",
"ask_1:new_answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_resume_from_second_interrupt(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the second interrupt, then
resume with a new answer. The first interrupt's answer should be preserved.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
Key assertion: after resuming from a time-travel to the 2nd interrupt,
the final state keeps ask_1's original answer but uses the new ask_2 answer.
"""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="answer_1"), config)
sub_config_at_second = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="answer_2"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to second interrupt ---
called.clear()
replay_result = graph.invoke(None, sub_config_at_second)
assert replay_result["__interrupt__"][0].value == "Question 2?"
assert "step_a" not in called
assert "ask_1" not in called # already resolved, not re-executed
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume with a new answer for ask_2 only ---
called.clear()
resume_result = graph.invoke(Command(resume="new_answer_2"), config)
# ask_1's original answer preserved, ask_2 uses the new answer
assert resume_result["value"] == [
"step_a_done",
"ask_1:answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_checkpoint_pattern(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Verify the checkpoint pattern created by time travel to a subgraph
interrupt. A fork checkpoint should branch from the replay point and
become the latest parent checkpoint.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> ask (interrupt) --> END
Original run (after completing):
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- replay point
source=loop next=() values=[a:first]
After time travel to interrupt + resume with "second":
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- branch point
source=loop next=() values=[a:first] (old branch)
source=fork next=(executor,) values=[] <-- fork
source=loop next=() values=[a:second] (new branch)
"""
def ask(state: State) -> State:
answer = interrupt("Q?")
return {"value": [f"a:{answer}"]}
executor = (
StateGraph(State)
.add_node("ask", ask)
.add_edge(START, "ask")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then complete
graph.invoke({"value": []}, config)
sub_config = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="first"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# Time travel to the interrupt
graph.invoke(None, sub_config)
# Fork is now the latest, branching from the original replay point
post_tt = list(graph.get_state_history(config))
post_tt_summary = _checkpoint_summary(post_tt)
assert [(s["source"], s["next"]) for s in post_tt_summary] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()),
("loop", ("executor",)), # <-- replay point / fork parent
("input", ("__start__",)),
]
# Verify the fork's parent is the original replay point
replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
# Resume from the fork — graph completes with new answer
result = graph.invoke(Command(resume="second"), config)
assert result["value"] == ["a:second"]
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch
("loop", (), {"value": ["a:second"]}),
("fork", ("executor",), {"value": []}),
# Original branch
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_after_completion(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -2283,14 +3066,16 @@ def test_replay_creates_branch_preserving_old_checkpoints(
# -- Post-replay checkpoint history (newest first) --
post_replay_history = list(graph.get_state_history(config))
post_summary = _checkpoint_summary(post_replay_history)
assert len(post_summary) == 7 # 5 original + 2 new branch checkpoints
# 5 original + 1 fork + 2 new branch checkpoints = 8
assert len(post_summary) == 8
# Verify the full shape after replay
assert [s["next"] for s in post_summary] == [
(), # new branch tip (C6)
("node_c",), # new branch (C5)
(), # old branch tip (C4)
("node_c",), # old (C3)
(), # new branch tip
("node_c",), # new branch
("node_b",), # fork from replay point
(), # old branch tip
("node_c",), # old
("node_b",), # branch point (C2)
("node_a",), # old (C1)
("__start__",), # old (C0)
@@ -2298,6 +3083,7 @@ def test_replay_creates_branch_preserving_old_checkpoints(
assert [s["values"] for s in post_summary] == [
{"value": ["a", "b2", "c"]}, # new branch tip
{"value": ["a", "b2"]}, # new: node_b re-ran with call_count=2
{"value": ["a"]}, # fork from replay point
{"value": ["a", "b1", "c"]}, # old branch tip preserved
{"value": ["a", "b1"]}, # old
{"value": ["a"]}, # branch point
+403 -7
View File
@@ -46,6 +46,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
Returns a list of dicts (newest-first, matching get_state_history order) with:
- id: short checkpoint id suffix (last 6 chars)
- parent_id: short parent checkpoint id suffix or None
- source: checkpoint metadata source (input, loop, fork, update)
- next: tuple of next node names
- values: channel values snapshot
"""
@@ -61,6 +62,7 @@ def _checkpoint_summary(history: list) -> list[dict]:
{
"id": cid[-6:],
"parent_id": pid[-6:] if pid else None,
"source": s.metadata.get("source"),
"next": s.next,
"values": s.values,
}
@@ -335,8 +337,14 @@ async def test_replay_interrupt_stable_across_replays(
r = await graph.ainvoke(None, before_ask.config)
results.append(r)
assert all(r == results[0] for r in results)
assert "__interrupt__" in results[0]
# Each replay creates a fork with a unique interrupt ID, so we compare
# interrupt values and state values rather than full equality.
assert all("__interrupt__" in r for r in results)
assert all(
r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
for r in results
)
assert all(r["value"] == results[0]["value"] for r in results)
@NEEDS_CONTEXTVARS
@@ -1261,6 +1269,391 @@ async def test_subgraph_time_travel_after_completion_async(
assert "ask_2:answer_2" in replay_result["value"]
@NEEDS_CONTEXTVARS
async def test_replay_from_before_interrupt_then_resume_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from checkpoint before interrupt node, then resume with a new
answer and verify the graph completes with the new value.
Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
"""
called: list[str] = []
async def node_a(state: State) -> State:
called.append("node_a")
return {"value": ["a"]}
async def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("What is your input?")
return {"value": [f"human:{answer}"]}
async def node_b(state: State) -> State:
called.append("node_b")
return {"value": ["b"]}
graph = (
StateGraph(State)
.add_node("node_a", node_a)
.add_node("ask_human", ask_human)
.add_node("node_b", node_b)
.add_edge(START, "node_a")
.add_edge("node_a", "ask_human")
.add_edge("ask_human", "node_b")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: invoke until interrupt, then resume to complete ---
await graph.ainvoke({"value": []}, config)
await graph.ainvoke(Command(resume="old_answer"), config)
original_history = [s async for s in graph.aget_state_history(config)]
original = _checkpoint_summary(original_history)
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Replay from checkpoint before ask_human ---
before_ask = next(s for s in original_history if s.next == ("ask_human",))
called.clear()
replay_result = await graph.ainvoke(None, before_ask.config)
assert replay_result["__interrupt__"][0].value == "What is your input?"
assert "ask_human" in called
assert "node_a" not in called
# A fork checkpoint is now the latest
post_replay = _checkpoint_summary(
[s async for s in graph.aget_state_history(config)]
)
assert [(s["source"], s["next"]) for s in post_replay] == [
("fork", ("ask_human",)),
("loop", ()),
("loop", ("node_b",)),
("loop", ("ask_human",)),
("loop", ("node_a",)),
("input", ("__start__",)),
]
# --- Resume with a new answer ---
called.clear()
final_result = await graph.ainvoke(Command(resume="new_answer"), config)
assert final_result["value"] == ["a", "human:new_answer", "b"]
assert "ask_human" in called
assert "node_b" in called
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from fork)
("loop", (), {"value": ["a", "human:new_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
("fork", ("ask_human",), {"value": ["a"]}),
# Original branch (preserved)
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_resume_from_first_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the first interrupt, then
resume through both interrupts with new answers.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
"""
called: list[str] = []
async def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
async def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
async def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
await graph.ainvoke({"value": []}, config)
sub_config_at_first = (
(await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
)
await graph.ainvoke(Command(resume="answer_1"), config)
await graph.ainvoke(Command(resume="answer_2"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to first interrupt's subgraph checkpoint ---
called.clear()
replay_result = await graph.ainvoke(None, sub_config_at_first)
assert replay_result["__interrupt__"][0].value == "Question 1?"
assert "step_a" not in called
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume both interrupts with new answers ---
called.clear()
resume_1 = await graph.ainvoke(Command(resume="new_answer_1"), config)
assert resume_1["__interrupt__"][0].value == "Question 2?"
assert "ask_1" in called
called.clear()
resume_2 = await graph.ainvoke(Command(resume="new_answer_2"), config)
assert resume_2["value"] == [
"step_a_done",
"ask_1:new_answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_resume_from_second_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the second interrupt, then
resume with a new answer. The first interrupt's answer should be preserved.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
"""
called: list[str] = []
async def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
async def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
async def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
await graph.ainvoke({"value": []}, config)
await graph.ainvoke(Command(resume="answer_1"), config)
sub_config_at_second = (
(await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
)
await graph.ainvoke(Command(resume="answer_2"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to second interrupt ---
called.clear()
replay_result = await graph.ainvoke(None, sub_config_at_second)
assert replay_result["__interrupt__"][0].value == "Question 2?"
assert "step_a" not in called
assert "ask_1" not in called
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume with a new answer for ask_2 only ---
called.clear()
resume_result = await graph.ainvoke(Command(resume="new_answer_2"), config)
assert resume_result["value"] == [
"step_a_done",
"ask_1:answer_1",
"ask_2:new_answer_2",
]
# Verify final history
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_checkpoint_pattern_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Verify the checkpoint pattern created by time travel to a subgraph
interrupt. A fork checkpoint should branch from the replay point.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> ask (interrupt) --> END
"""
async def ask(state: State) -> State:
answer = interrupt("Q?")
return {"value": [f"a:{answer}"]}
executor = (
StateGraph(State)
.add_node("ask", ask)
.add_edge(START, "ask")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then complete
await graph.ainvoke({"value": []}, config)
sub_config = (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
await graph.ainvoke(Command(resume="first"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# Time travel to the interrupt
await graph.ainvoke(None, sub_config)
# Fork is now the latest, branching from the original replay point
post_tt = [s async for s in graph.aget_state_history(config)]
post_tt_summary = _checkpoint_summary(post_tt)
assert [(s["source"], s["next"]) for s in post_tt_summary] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()),
("loop", ("executor",)), # <-- replay point / fork parent
("input", ("__start__",)),
]
# Verify the fork's parent is the original replay point
replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
# Resume from the fork
result = await graph.ainvoke(Command(resume="second"), config)
assert result["value"] == ["a:second"]
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch
("loop", (), {"value": ["a:second"]}),
("fork", ("executor",), {"value": []}),
# Original branch
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_3_levels_deep_time_travel_to_first_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
@@ -2088,13 +2481,15 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
# -- Post-replay checkpoint history (newest first) --
post_replay_history = [s async for s in graph.aget_state_history(config)]
post_summary = _checkpoint_summary(post_replay_history)
assert len(post_summary) == 7 # 5 original + 2 new branch checkpoints
# 5 original + 1 fork + 2 new branch checkpoints = 8
assert len(post_summary) == 8
assert [s["next"] for s in post_summary] == [
(), # new branch tip (C6)
("node_c",), # new branch (C5)
(), # old branch tip (C4)
("node_c",), # old (C3)
(), # new branch tip
("node_c",), # new branch
("node_b",), # fork from replay point
(), # old branch tip
("node_c",), # old
("node_b",), # branch point (C2)
("node_a",), # old (C1)
("__start__",), # old (C0)
@@ -2102,6 +2497,7 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
assert [s["values"] for s in post_summary] == [
{"value": ["a", "b2", "c"]}, # new branch tip
{"value": ["a", "b2"]}, # new: node_b re-ran with call_count=2
{"value": ["a"]}, # fork from replay point
{"value": ["a", "b1", "c"]}, # old branch tip preserved
{"value": ["a", "b1"]}, # old
{"value": ["a"]}, # branch point
@@ -1,290 +0,0 @@
"""Tests for StreamToolCallHandler and emit_tool_output_delta.
These tests exercise the langgraph-core piece in isolation the prebuilt
`ToolCallTransformer` has its own test file. Here we feed real graphs
through `Pregel.stream(stream_mode=["tools", ...])` and inspect the raw
`(ns, mode, payload)` tuples on the `tools` channel.
"""
from __future__ import annotations
from typing import Annotated, Any
import pytest
from langchain_core.messages import AIMessage
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode
from typing_extensions import TypedDict
from langgraph.config import emit_tool_output_delta
from langgraph.constants import END, START
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
class _State(TypedDict):
messages: Annotated[list, add_messages]
def _caller_sync(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
)
]
}
return caller
def _caller_async(tool_name: str, tool_args: dict[str, Any], tc_id: str = "tc1"):
async def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[{"name": tool_name, "args": tool_args, "id": tc_id}],
)
]
}
return caller
def _build_graph(caller, tools) -> Any:
sg = StateGraph(_State)
sg.add_node("caller", caller)
sg.add_node("tools", ToolNode(tools))
sg.add_edge(START, "caller")
sg.add_edge("caller", "tools")
sg.add_edge("tools", END)
return sg.compile()
def _tool_events(stream) -> list[tuple[tuple[str, ...], dict]]:
"""Collect `(ns, payload)` for every `tools`-mode chunk."""
out: list[tuple[tuple[str, ...], dict]] = []
for ns, mode, payload in stream:
if mode == "tools":
out.append((tuple(ns), payload))
return out
class TestSyncGraphSyncTool:
def test_started_finished_cycle(self) -> None:
@tool
def echo(text: str) -> str:
"""echo."""
return f"echoed:{text}"
graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
events = _tool_events(
graph.stream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
)
)
assert [p["event"] for _, p in events] == [
"tool-started",
"tool-finished",
]
assert events[0][1]["tool_call_id"] == "tc1"
assert events[0][1]["tool_name"] == "echo"
assert events[0][1]["input"] == {"text": "hi"}
# ToolNode wraps the return in a ToolMessage.
assert events[1][1]["tool_call_id"] == "tc1"
def test_emit_tool_output_delta_produces_delta_events(self) -> None:
@tool
def streaming_echo(text: str) -> str:
"""stream chunks."""
for chunk in ("a", "b", "c"):
emit_tool_output_delta(chunk)
return text
graph = _build_graph(
_caller_sync("streaming_echo", {"text": "x"}), [streaming_echo]
)
events = _tool_events(
graph.stream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
)
)
deltas = [p["delta"] for _, p in events if p["event"] == "tool-output-delta"]
assert deltas == ["a", "b", "c"]
# The deltas must be bracketed by started and finished.
ordered = [p["event"] for _, p in events]
assert ordered[0] == "tool-started"
assert ordered[-1] == "tool-finished"
def test_tool_error_event(self) -> None:
@tool
def boom() -> str:
"""raises."""
raise ValueError("nope")
graph = _build_graph(_caller_sync("boom", {}), [boom])
events: list[tuple[tuple[str, ...], dict]] = []
with pytest.raises(ValueError, match="nope"):
for ns, mode, payload in graph.stream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
):
if mode == "tools":
events.append((tuple(ns), payload))
kinds = [p["event"] for _, p in events]
assert kinds == ["tool-started", "tool-error"]
assert events[1][1]["message"] == "nope"
def test_emit_outside_tool_is_noop(self) -> None:
# Called at import time (outside any tool body) — must not raise.
emit_tool_output_delta("ignored")
emit_tool_output_delta({"any": "payload"})
def test_no_events_without_tools_mode(self) -> None:
@tool
def echo(text: str) -> str:
"""echo."""
return text
graph = _build_graph(_caller_sync("echo", {"text": "hi"}), [echo])
# No "tools" in stream_mode — handler is not attached and zero
# `tools`-method events fire.
chunks = list(
graph.stream(
{"messages": []},
stream_mode=["values"],
subgraphs=True,
)
)
assert all(
not (isinstance(c, tuple) and len(c) == 3 and c[1] == "tools")
for c in chunks
)
class TestAsyncGraphAsyncTool:
@pytest.mark.anyio
async def test_async_tool_produces_events(self) -> None:
@tool
async def aecho(text: str) -> str:
"""async echo."""
emit_tool_output_delta(text)
return f"got:{text}"
graph = _build_graph(_caller_async("aecho", {"text": "hi"}), [aecho])
events: list[tuple[tuple[str, ...], dict]] = []
async for ns, mode, payload in graph.astream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
):
if mode == "tools":
events.append((tuple(ns), payload))
kinds = [p["event"] for _, p in events]
assert kinds == ["tool-started", "tool-output-delta", "tool-finished"]
assert events[1][1]["delta"] == "hi"
class TestConcurrentToolCalls:
def test_parallel_tool_calls_do_not_bleed(self) -> None:
@tool
def streamer(marker: str) -> str:
"""emits marker twice."""
emit_tool_output_delta(f"{marker}-1")
emit_tool_output_delta(f"{marker}-2")
return marker
def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[
{"name": "streamer", "args": {"marker": "A"}, "id": "a"},
{"name": "streamer", "args": {"marker": "B"}, "id": "b"},
],
)
]
}
graph = _build_graph(caller, [streamer])
events = _tool_events(
graph.stream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
)
)
# Group deltas by tool_call_id.
by_id: dict[str, list[str]] = {}
for _, p in events:
if p["event"] == "tool-output-delta":
by_id.setdefault(p["tool_call_id"], []).append(p["delta"])
assert by_id["a"] == ["A-1", "A-2"]
assert by_id["b"] == ["B-1", "B-2"]
class TestSubgraphNamespacePropagation:
def test_tool_inside_subgraph_emits_with_subgraph_ns(self) -> None:
@tool
def inner_tool(text: str) -> str:
"""inner tool."""
return text
def sub_caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[
{
"name": "inner_tool",
"args": {"text": "x"},
"id": "tc1",
}
],
)
]
}
inner = StateGraph(_State)
inner.add_node("sub_caller", sub_caller)
inner.add_node("sub_tools", ToolNode([inner_tool]))
inner.add_edge(START, "sub_caller")
inner.add_edge("sub_caller", "sub_tools")
inner.add_edge("sub_tools", END)
inner_graph = inner.compile()
outer = StateGraph(_State)
outer.add_node("sub", inner_graph)
outer.add_edge(START, "sub")
outer.add_edge("sub", END)
graph = outer.compile()
events = _tool_events(
graph.stream(
{"messages": []},
stream_mode=["tools"],
subgraphs=True,
)
)
# All `tools` events should carry a non-empty namespace rooted
# at the `sub` node.
assert events, "expected at least one tools event"
for ns, _ in events:
assert ns # non-empty
assert ns[0].startswith("sub:")
+12 -24
View File
@@ -1348,11 +1348,10 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.2"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
{ name = "langchain-protocol" },
{ name = "langsmith" },
{ name = "packaging" },
{ name = "pydantic" },
@@ -1361,26 +1360,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/a8/03/7219502e8ca728d65eb44d7a3eb60239230742a70dbfc9241b9bfd61c4ab/langchain_core-1.3.2.tar.gz", hash = "sha256:fd7a50b2f28ba561fd9d7f5d2760bc9e06cf00cdf820a3ccafe88a94ffa8d5b7", size = 911813, upload-time = "2026-04-24T15:49:23.699Z" }
sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
wheels = [
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[[package]]
name = "langchain-protocol"
version = "0.0.12"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "typing-extensions" },
]
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]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -1452,7 +1439,7 @@ test = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = ">=1.3.2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "../prebuilt" },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1561,7 +1548,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.1"
version = "4.0.2"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -1719,7 +1706,7 @@ inmem = [
requires-dist = [
{ name = "click", specifier = ">=8.1.7" },
{ name = "httpx", specifier = ">=0.24.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
{ name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
{ name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
{ name = "pathspec", specifier = ">=0.11.0" },
@@ -1755,7 +1742,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "../prebuilt" }
dependencies = [
{ name = "langchain-core" },
@@ -1865,7 +1852,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.6.4"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -1875,11 +1862,12 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
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]
[package.optional-dependencies]
@@ -1,7 +1,5 @@
"""langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
from langgraph.prebuilt._tool_call_stream import ToolCallStream
from langgraph.prebuilt._tool_call_transformer import ToolCallTransformer
from langgraph.prebuilt.chat_agent_executor import create_react_agent
from langgraph.prebuilt.tool_node import (
InjectedState,
@@ -15,8 +13,6 @@ from langgraph.prebuilt.tool_validator import ValidationNode
__all__ = [
"create_react_agent",
"ToolNode",
"ToolCallStream",
"ToolCallTransformer",
"tools_condition",
"ValidationNode",
"InjectedState",
@@ -1,117 +0,0 @@
"""In-process handle for a single tool call's streaming execution.
Mirrors the shape of `ChatModelStream` from langchain-core but simpler
a tool has one output channel, no content-block multiplexing. Populated
by `ToolCallTransformer` as `tool-started` / `tool-output-delta` /
`tool-finished` / `tool-error` events flow in on the `tools` channel.
"""
from __future__ import annotations
from collections.abc import AsyncIterator, Iterator
from typing import Any
from langgraph.stream._event_log import EventLog
class ToolCallStream:
"""Scoped view of a single tool call's lifecycle.
Yielded on `run.tool_calls` once per `tool-started` event. Fields
are populated as events arrive:
- `tool_call_id`, `tool_name`, `input`: stable from the start event.
- `output_deltas`: an `EventLog` of delta chunks. Iterate (sync or
async) to consume partial output in arrival order.
- `output`: terminal payload from `tool-finished`, or `None` if the
call failed or is still in flight.
- `error`: terminal error string from `tool-error`, or `None` if the
call succeeded or is still in flight.
- `completed`: True once a terminal event (`tool-finished` or
`tool-error`) has been observed.
`ToolCallStream` is not meant to be constructed by end users it's
produced by `ToolCallTransformer` as events flow through the mux.
"""
def __init__(
self,
tool_call_id: str,
tool_name: str,
input: dict[str, Any] | None = None,
) -> None:
"""Initialize a fresh handle for a tool call.
Args:
tool_call_id: The `tool_call_id` from the AIMessage.
tool_name: The tool's name.
input: The tool's input arguments (as reported by
`on_tool_start`), or `None` if none were captured.
"""
self.tool_call_id = tool_call_id
self.tool_name = tool_name
self.input = input
self._output_deltas: EventLog[Any] = EventLog()
self.output: Any = None
self.error: str | None = None
self.completed = False
@property
def output_deltas(self) -> EventLog[Any]:
"""The EventLog of streamed `tool-output-delta` payloads.
Iterate (sync or async depending on how the run was started)
to consume partial output in arrival order. The log closes when
the tool finishes or errors.
"""
return self._output_deltas
def _bind(self, *, is_async: bool) -> None:
"""Bind the deltas log to sync or async iteration.
Called by `ToolCallTransformer` when constructing this handle so
the log matches the enclosing mux's mode.
"""
self._output_deltas._bind(is_async=is_async)
def _push_delta(self, delta: Any) -> None:
self._output_deltas.push(delta)
def _finish(self, output: Any) -> None:
self.output = output
self.completed = True
self._output_deltas.close()
def _fail(self, message: str) -> None:
self.error = message
self.completed = True
self._output_deltas.close()
def __iter__(self) -> Iterator[Any]:
"""Iterate delta chunks synchronously.
Equivalent to `iter(self.output_deltas)`. Raises `TypeError` if
the underlying log is bound to async mode.
"""
return iter(self._output_deltas)
def __aiter__(self) -> AsyncIterator[Any]:
"""Iterate delta chunks asynchronously.
Equivalent to `aiter(self.output_deltas)`. Raises `TypeError`
if the underlying log is bound to sync mode.
"""
return self._output_deltas.__aiter__()
def __repr__(self) -> str:
status = (
"completed"
if self.completed and self.error is None
else "failed"
if self.completed
else "running"
)
return (
f"ToolCallStream(tool_call_id={self.tool_call_id!r}, "
f"tool_name={self.tool_name!r}, status={status})"
)
@@ -1,128 +0,0 @@
"""Transformer that projects `tools` channel events into `ToolCallStream`s."""
from __future__ import annotations
from collections.abc import Awaitable, Callable
from typing import Any
from langgraph.stream._event_log import EventLog
from langgraph.stream._types import ProtocolEvent, StreamTransformer
from langgraph.prebuilt._tool_call_stream import ToolCallStream
class ToolCallTransformer(StreamTransformer):
"""Project `tools` channel events into `ToolCallStream` handles.
Each `tool-started` event spawns a `ToolCallStream`, pushed onto
`run.tool_calls`. Subsequent `tool-output-delta` events append to
that stream's deltas log; `tool-finished` and `tool-error` close it.
Native transformer the `tool_calls` projection is exposed as a
direct attribute on the run stream.
`EventLog[ToolCallStream]` is used (not `StreamChannel`) because the
live handles are not serializable and should not be auto-forwarded
onto the main event log. Wire consumers subscribe to the `tools`
channel instead, where the raw protocol events flow through
untouched by this transformer (`process` returns `True`).
Registered explicitly by users at compile time via
`builder.compile(transformers=[ToolCallTransformer])` not a
default built-in, so the `tools` channel is user-opt-in.
"""
_native = True
required_stream_modes = ("tools",)
def __init__(self, scope: tuple[str, ...] = ()) -> None:
super().__init__(scope)
self._log: EventLog[ToolCallStream] = EventLog()
self._active: dict[str, ToolCallStream] = {}
self._is_async = False
self._pump_fn: Callable[[], bool] | None = None
self._apump_fn: Callable[[], Awaitable[bool]] | None = None
def init(self) -> dict[str, Any]:
return {"tool_calls": self._log}
def _bind_pump(self, fn: Callable[[], bool]) -> None:
"""Wire the sync pull callback onto this transformer.
Called by `StreamMux.bind_pump`. Stored so each new
`ToolCallStream` created by `process` can wire its deltas log
for pump-driven iteration.
"""
self._pump_fn = fn
self._is_async = False
def _bind_apump(self, fn: Callable[[], Awaitable[bool]]) -> None:
"""Async counterpart to `_bind_pump`."""
self._apump_fn = fn
self._is_async = True
def _new_stream(
self,
tool_call_id: str,
tool_name: str,
tool_input: dict[str, Any] | None,
) -> ToolCallStream:
stream = ToolCallStream(tool_call_id, tool_name, tool_input)
stream._bind(is_async=self._is_async)
if self._apump_fn is not None:
stream._output_deltas._arequest_more = self._apump_fn
if self._pump_fn is not None:
stream._output_deltas._request_more = self._pump_fn
return stream
def process(self, event: ProtocolEvent) -> bool:
# Namespace filtering is handled by the mux via `scope_exact`.
if event["method"] != "tools":
return True
data = event["params"]["data"]
tool_call_id = data.get("tool_call_id")
if tool_call_id is None:
return True
event_type = data.get("event")
stream: ToolCallStream | None
if event_type == "tool-started":
stream = self._new_stream(
tool_call_id,
data.get("tool_name", ""),
data.get("input"),
)
self._active[tool_call_id] = stream
self._log.push(stream)
elif event_type == "tool-output-delta":
stream = self._active.get(tool_call_id)
if stream is not None:
stream._push_delta(data.get("delta"))
elif event_type == "tool-finished":
stream = self._active.pop(tool_call_id, None)
if stream is not None:
stream._finish(data.get("output"))
elif event_type == "tool-error":
stream = self._active.pop(tool_call_id, None)
if stream is not None:
stream._fail(data.get("message", ""))
# Pass-through — wire consumers subscribe to the `tools` channel
# directly and reconstruct handles client-side.
return True
def finalize(self) -> None:
"""Close any still-active tool streams left open at run end."""
for stream in self._active.values():
if not stream.completed:
stream._finish(None)
self._active.clear()
def fail(self, err: BaseException) -> None:
"""Fail any still-active tool streams when the run errors."""
message = str(err)
for stream in self._active.values():
if not stream.completed:
stream._fail(message)
self._active.clear()
+23 -7
View File
@@ -614,6 +614,7 @@ class _InjectedArgs:
store: str | None
runtime: str | None
all_injected_keys: set[str]
_optional_state_args: set[str]
class ToolNode(RunnableCallable):
@@ -807,6 +808,7 @@ class ToolNode(RunnableCallable):
context=runtime.context,
store=runtime.store,
stream_writer=runtime.stream_writer,
tools=list(self.tools_by_name.values()),
execution_info=runtime.execution_info,
server_info=runtime.server_info,
)
@@ -841,6 +843,7 @@ class ToolNode(RunnableCallable):
context=runtime.context,
store=runtime.store,
stream_writer=runtime.stream_writer,
tools=list(self.tools_by_name.values()),
execution_info=runtime.execution_info,
server_info=runtime.server_info,
)
@@ -1333,7 +1336,7 @@ class ToolNode(RunnableCallable):
return tool_call
tool_call_copy: ToolCall = copy(tool_call)
injected_args = {}
injected_args: dict[str, Any] = {}
# Inject state
if injected.state:
@@ -1361,14 +1364,20 @@ class ToolNode(RunnableCallable):
# Extract state values
if isinstance(state, dict):
for tool_arg, state_field in injected.state.items():
injected_args[tool_arg] = (
state[state_field] if state_field else state
)
if not state_field:
injected_args[tool_arg] = state
elif state_field in state:
injected_args[tool_arg] = state[state_field]
elif tool_arg not in injected._optional_state_args:
raise KeyError(state_field)
else:
for tool_arg, state_field in injected.state.items():
injected_args[tool_arg] = (
getattr(state, state_field) if state_field else state
)
if not state_field:
injected_args[tool_arg] = state
elif hasattr(state, state_field):
injected_args[tool_arg] = getattr(state, state_field)
elif tool_arg not in injected._optional_state_args:
raise AttributeError(state_field)
# Inject store
if injected.store:
@@ -1569,6 +1578,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
- `context`: Runtime context (shared with `Runtime`)
- `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
- `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
- `tools`: List of all available `BaseTool` instances
No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
as a parameter.
@@ -1611,6 +1621,7 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
context: ContextT
config: RunnableConfig
stream_writer: StreamWriter
tools: list[BaseTool]
tool_call_id: str | None
store: BaseStore | None
execution_info: ExecutionInfo | None = None
@@ -1859,6 +1870,7 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
store_arg: str | None = None
runtime_arg: str | None = None
all_injected_keys: set[str] = set()
_optional_state_args: set[str] = set()
for name, type_ in all_annotations.items():
# Track all InjectedToolArg-annotated params (including custom subclasses)
@@ -1873,6 +1885,9 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
if state_inj := _get_injection_from_type(type_, InjectedState):
if isinstance(state_inj, InjectedState) and state_inj.field:
state_args[name] = state_inj.field
field_info = full_schema.model_fields.get(name)
if field_info and not field_info.is_required():
_optional_state_args.add(name)
else:
state_args[name] = None
@@ -1889,4 +1904,5 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
store=store_arg,
runtime=runtime_arg,
all_injected_keys=all_injected_keys,
_optional_state_args=_optional_state_args,
)
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
authors = []
requires-python = ">=3.10"
@@ -0,0 +1,285 @@
"""Test InjectedState with NotRequired state fields.
This tests the fix for https://github.com/langchain-ai/langchain/issues/35585
When using InjectedState(<field>) on a tool parameter, and the referenced field is
declared as NotRequired in the custom state schema, the ToolNode should gracefully
handle missing fields by injecting None instead of raising KeyError.
"""
import sys
from typing import Annotated
import pytest
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
from langchain_core.tools import tool
from langgraph.graph.message import add_messages
from pydantic import BaseModel, Field
from typing_extensions import NotRequired
from langgraph.prebuilt import InjectedState, ToolNode, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from .model import FakeToolCallingModel
class CustomAgentStateWithNotRequired(AgentState):
"""Custom state with a NotRequired field (TypedDict style)."""
city: NotRequired[str]
class CustomAgentStatePydanticWithDefault(BaseModel):
"""Custom state with Optional field and default (Pydantic style)."""
messages: Annotated[list[AnyMessage], add_messages]
remaining_steps: int = Field(default=10)
city: str | None = Field(default=None)
@tool
def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
"""Get weather for a given city."""
if city is None:
return "No city provided"
return f"It's always sunny in {city}!"
def _create_mock_runtime(
state: dict | None = None,
store=None,
):
"""Create a mock Runtime for testing ToolNode directly."""
from unittest.mock import Mock
from langgraph.runtime import Runtime
mock_runtime = Mock(spec=Runtime)
mock_runtime.context = {}
return mock_runtime
def _create_config_with_runtime(store=None, state=None):
"""Create a RunnableConfig with mocked runtime for direct ToolNode testing."""
from langgraph.prebuilt.tool_node import ToolRuntime
tool_runtime = ToolRuntime(
state=state or {},
config={},
context={},
store=store,
stream_writer=None,
tools=[],
tool_call_id="test_id",
)
return {
"configurable": {
"__pregel_runtime": _create_mock_runtime(),
"__tool_runtime__": tool_runtime,
}
}
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_injected_state_not_required_field_missing_injects_none():
"""Test that InjectedState with NotRequired field injects None when field is missing.
This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
"""
tool_node = ToolNode([get_weather])
tool_call = {
"name": "get_weather",
"args": {},
"id": "call_1",
"type": "tool_call",
}
ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
# State WITHOUT the "city" field - should inject None instead of raising KeyError
state_without_city: CustomAgentStateWithNotRequired = {
"messages": [HumanMessage("What's the weather?"), ai_msg],
}
result = tool_node.invoke(
state_without_city,
config=_create_config_with_runtime(state=state_without_city),
)
assert len(result["messages"]) == 1
tool_msg = result["messages"][0]
assert isinstance(tool_msg, ToolMessage)
assert "No city provided" in tool_msg.content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_injected_state_not_required_field_present_works():
"""Test that InjectedState with NotRequired field works when field IS present."""
tool_node = ToolNode([get_weather])
tool_call = {
"name": "get_weather",
"args": {},
"id": "call_1",
"type": "tool_call",
}
ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
# State WITH the "city" field - this should work
state_with_city: CustomAgentStateWithNotRequired = {
"messages": [HumanMessage("What's the weather?"), ai_msg],
"city": "San Francisco",
}
result = tool_node.invoke(
state_with_city,
config=_create_config_with_runtime(state=state_with_city),
)
assert len(result["messages"]) == 1
tool_msg = result["messages"][0]
assert isinstance(tool_msg, ToolMessage)
assert "San Francisco" in tool_msg.content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_create_react_agent_injected_state_not_required_field_missing():
"""Test create_react_agent with InjectedState using NotRequired field that is missing.
This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
"""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather],
state_schema=CustomAgentStateWithNotRequired,
)
# Invoke WITHOUT the city field - should work, injecting None
result = agent.invoke(
{"messages": [HumanMessage("What's the weather?")]},
)
# Check that the tool was called successfully with None injected
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "No city provided" in tool_messages[0].content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_create_react_agent_injected_state_not_required_field_present():
"""Test create_react_agent with InjectedState using NotRequired field that IS present."""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather],
state_schema=CustomAgentStateWithNotRequired,
)
# Invoke WITH the city field
result = agent.invoke(
{
"messages": [HumanMessage("What's the weather?")],
"city": "San Francisco",
},
)
# Check that the tool was called successfully
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "San Francisco" in tool_messages[0].content
@tool
def get_weather_optional(city: Annotated[str | None, InjectedState("city")]) -> str:
"""Get weather for a given city (accepts None)."""
if city is None:
return "Please provide a city!"
return f"It's always sunny in {city}!"
def test_pydantic_state_with_default_field_missing_works():
"""Test that Pydantic state with Optional field and default=None works when field is missing.
This is the workaround suggested in the issue comments - using Pydantic BaseModel
with `city: Optional[str] = Field(default=None)` instead of TypedDict with NotRequired.
"""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather_optional],
state_schema=CustomAgentStatePydanticWithDefault,
)
# Invoke WITHOUT the city field - should work because Pydantic provides default
result = agent.invoke(
{"messages": [HumanMessage("What's the weather?")]},
)
# Check that the tool was called successfully with None
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "Please provide a city!" in tool_messages[0].content
def test_pydantic_state_with_default_field_present_works():
"""Test that Pydantic state with Optional field works when field IS present."""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather_optional],
state_schema=CustomAgentStatePydanticWithDefault,
)
# Invoke WITH the city field
result = agent.invoke(
{
"messages": [HumanMessage("What's the weather?")],
"city": "San Francisco",
},
)
# Check that the tool was called successfully
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "San Francisco" in tool_messages[0].content
@@ -1,306 +0,0 @@
"""Tests for ToolCallTransformer and the ToolCallStream projection."""
from __future__ import annotations
import time
from typing import Annotated, Any
import pytest
from langchain_core.messages import AIMessage
from langchain_core.tools import tool
from langgraph.config import emit_tool_output_delta
from langgraph.constants import END, START
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.stream._event_log import EventLog
from langgraph.stream._mux import StreamMux
from langgraph.stream._types import ProtocolEvent
from langgraph.stream.transformers import (
MessagesTransformer,
SubgraphTransformer,
ValuesTransformer,
)
from typing_extensions import TypedDict
from langgraph.prebuilt import ToolCallStream, ToolCallTransformer, ToolNode
TS = int(time.time() * 1000)
def _tool_event(
event: str,
tool_call_id: str,
*,
tool_name: str = "",
input: dict[str, Any] | None = None,
delta: Any = None,
output: Any = None,
message: str = "",
namespace: list[str] | None = None,
) -> ProtocolEvent:
data: dict[str, Any] = {"event": event, "tool_call_id": tool_call_id}
if event == "tool-started":
data["tool_name"] = tool_name
if input is not None:
data["input"] = input
elif event == "tool-output-delta":
data["delta"] = delta
elif event == "tool-finished":
data["output"] = output
elif event == "tool-error":
data["message"] = message
return {
"type": "event",
"method": "tools",
"params": {
"namespace": namespace or [],
"timestamp": TS,
"data": data,
},
}
def _subscribe(log: EventLog) -> None:
log._subscribed = True
def _mux() -> tuple[StreamMux, ToolCallTransformer]:
mux = StreamMux(
factories=[
ValuesTransformer,
MessagesTransformer,
SubgraphTransformer,
ToolCallTransformer,
],
is_async=False,
)
transformer = mux.transformer_by_key("tool_calls")
assert isinstance(transformer, ToolCallTransformer)
_subscribe(transformer._log)
return mux, transformer
class TestToolCallTransformerUnit:
def test_required_stream_modes_declares_tools(self) -> None:
assert ToolCallTransformer.required_stream_modes == ("tools",)
def test_tool_started_yields_handle(self) -> None:
mux, transformer = _mux()
mux.push(
_tool_event(
"tool-started",
"tc1",
tool_name="echo",
input={"text": "hi"},
)
)
handles = list(transformer._log._items)
assert len(handles) == 1
h = handles[0]
assert isinstance(h, ToolCallStream)
assert h.tool_call_id == "tc1"
assert h.tool_name == "echo"
assert h.input == {"text": "hi"}
assert h.completed is False
def test_delta_accumulates_on_active_stream(self) -> None:
mux, transformer = _mux()
mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
_subscribe(transformer._active["tc1"]._output_deltas)
mux.push(_tool_event("tool-output-delta", "tc1", delta="a"))
mux.push(_tool_event("tool-output-delta", "tc1", delta="b"))
stream = transformer._active["tc1"]
assert list(stream._output_deltas._items) == ["a", "b"]
def test_finish_closes_stream(self) -> None:
mux, transformer = _mux()
mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
stream = transformer._active["tc1"]
mux.push(_tool_event("tool-finished", "tc1", output="done"))
assert stream.completed is True
assert stream.output == "done"
assert stream.error is None
assert "tc1" not in transformer._active
def test_error_closes_stream(self) -> None:
mux, transformer = _mux()
mux.push(_tool_event("tool-started", "tc1", tool_name="boom"))
stream = transformer._active["tc1"]
mux.push(_tool_event("tool-error", "tc1", message="nope"))
assert stream.completed is True
assert stream.output is None
assert stream.error == "nope"
assert "tc1" not in transformer._active
def test_concurrent_tool_calls_do_not_bleed(self) -> None:
mux, transformer = _mux()
mux.push(_tool_event("tool-started", "a", tool_name="t"))
mux.push(_tool_event("tool-started", "b", tool_name="t"))
for tc in ("a", "b"):
_subscribe(transformer._active[tc]._output_deltas)
mux.push(_tool_event("tool-output-delta", "a", delta="A1"))
mux.push(_tool_event("tool-output-delta", "b", delta="B1"))
mux.push(_tool_event("tool-output-delta", "a", delta="A2"))
assert list(transformer._active["a"]._output_deltas._items) == ["A1", "A2"]
assert list(transformer._active["b"]._output_deltas._items) == ["B1"]
def test_tools_event_passes_through_main_log(self) -> None:
mux, transformer = _mux()
_subscribe(mux._events)
mux.push(_tool_event("tool-started", "tc1", tool_name="echo"))
kept = [e for e in mux._events._items if e["method"] == "tools"]
assert len(kept) == 1
# ---------------------------------------------------------------------------
# End-to-end tests with a real graph
# ---------------------------------------------------------------------------
class _State(TypedDict):
messages: Annotated[list, add_messages]
def _build_graph(caller, tools):
sg = StateGraph(_State)
sg.add_node("caller", caller)
sg.add_node("tools", ToolNode(tools))
sg.add_edge(START, "caller")
sg.add_edge("caller", "tools")
sg.add_edge("tools", END)
return sg.compile()
class TestToolCallTransformerEndToEnd:
def test_sync_streaming_tool_populates_tool_calls(self) -> None:
@tool
def streamer(text: str) -> str:
"""streams chunks."""
for chunk in ("one", "two"):
emit_tool_output_delta(chunk)
return text
def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[
{"name": "streamer", "args": {"text": "x"}, "id": "tc1"}
],
)
]
}
graph = _build_graph(caller, [streamer])
run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
tool_calls: list[ToolCallStream] = []
for tc in run.tool_calls:
tool_calls.append(tc)
deltas = list(tc.output_deltas)
assert deltas == ["one", "two"]
assert len(tool_calls) == 1
tc = tool_calls[0]
assert tc.tool_call_id == "tc1"
assert tc.tool_name == "streamer"
assert tc.completed is True
assert tc.error is None
def test_stream_modes_union_includes_tools(self) -> None:
@tool
def echo(text: str) -> str:
"""echo."""
return text
def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[
{"name": "echo", "args": {"text": "x"}, "id": "tc1"}
],
)
]
}
graph = _build_graph(caller, [echo])
# Without ToolCallTransformer, no tool_calls projection is
# exposed and no `tools` events flow through (required_stream_modes
# omits it).
run_no_tc = graph.stream_v2({"messages": []})
assert "tool_calls" not in run_no_tc._mux.extensions # type: ignore[attr-defined]
# With ToolCallTransformer, the projection is present.
run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
assert "tool_calls" in run._mux.extensions # type: ignore[attr-defined]
# Drain so the run closes cleanly.
list(run.tool_calls)
@pytest.mark.anyio
async def test_async_streaming_tool_populates_tool_calls(self) -> None:
@tool
async def astreamer(text: str) -> str:
"""async streams."""
emit_tool_output_delta(text)
emit_tool_output_delta(text + "!")
return text
async def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[
{"name": "astreamer", "args": {"text": "hi"}, "id": "tc1"}
],
)
]
}
graph = _build_graph(caller, [astreamer])
run = await graph.astream_v2(
{"messages": []}, transformers=[ToolCallTransformer]
)
collected: list[ToolCallStream] = []
async for tc in run.tool_calls:
collected.append(tc)
deltas = [d async for d in tc.output_deltas]
assert deltas == ["hi", "hi!"]
assert len(collected) == 1
assert collected[0].completed is True
assert collected[0].error is None
def test_tool_error_populates_error_field(self) -> None:
@tool
def boom() -> str:
"""raises."""
raise ValueError("nope")
def caller(state: _State) -> dict:
return {
"messages": [
AIMessage(
content="",
tool_calls=[{"name": "boom", "args": {}, "id": "tc1"}],
)
]
}
graph = _build_graph(caller, [boom])
run = graph.stream_v2({"messages": []}, transformers=[ToolCallTransformer])
collected: list[ToolCallStream] = []
with pytest.raises(ValueError, match="nope"):
for tc in run.tool_calls:
collected.append(tc)
# Drain deltas so the error field is populated before we
# inspect it below.
list(tc.output_deltas)
assert len(collected) == 1
assert collected[0].error == "nope"
assert collected[0].output is None
assert collected[0].completed is True
+29 -8
View File
@@ -2016,8 +2016,8 @@ async def test_tool_node_inject_runtime_dynamic_tool_via_wrap_tool_call_async()
assert tool_message.tool_call_id == "call_dynamic_2"
def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
"""Test that execution_info and server_info are forwarded from Runtime to ToolRuntime."""
def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
"""Test that execution_info, server_info, and tools are forwarded from Runtime to ToolRuntime."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2043,9 +2043,15 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
"""Tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
node = ToolNode([info_tool])
@dec_tool
def other_tool(y: int) -> str:
"""Another tool available to the runtime."""
return str(y)
node = ToolNode([info_tool, other_tool])
tool_call = {
"name": "info_tool",
"args": {"x": 1},
@@ -2054,17 +2060,21 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
node.invoke({"messages": [msg]}, config=config)
result = node.invoke({"messages": [msg]}, config=config)
assert result["messages"][-1].content == "ok"
assert captured["execution_info"] is exec_info
assert captured["execution_info"].thread_id == "t-1"
assert captured["execution_info"].task_id == "tk-1"
assert captured["server_info"] is server_info
assert captured["server_info"].assistant_id == "asst-1"
assert [tool.name for tool in captured["tools"]] == ["info_tool", "other_tool"]
async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> None:
"""Test that execution_info and server_info are forwarded in async path."""
async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async() -> (
None
):
"""Test that execution_info, server_info, and tools are forwarded in async path."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2090,9 +2100,15 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
"""Async tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
node = ToolNode([info_tool_async])
@dec_tool
async def other_tool_async(y: int) -> str:
"""Another async tool available to the runtime."""
return str(y)
node = ToolNode([info_tool_async, other_tool_async])
tool_call = {
"name": "info_tool_async",
"args": {"x": 1},
@@ -2101,12 +2117,17 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
await node.ainvoke({"messages": [msg]}, config=config)
result = await node.ainvoke({"messages": [msg]}, config=config)
assert result["messages"][-1].content == "ok"
assert captured["execution_info"] is exec_info
assert captured["execution_info"].thread_id == "t-2"
assert captured["server_info"] is server_info
assert captured["server_info"].graph_id == "graph-2"
assert [tool.name for tool in captured["tools"]] == [
"info_tool_async",
"other_tool_async",
]
# --- InjectedToolArg security tests ---
+11 -10
View File
@@ -249,7 +249,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0a2"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -261,14 +261,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "../langgraph" }
dependencies = [
{ name = "langchain-core" },
@@ -281,7 +281,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = "==1.3.0a2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "." },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -352,7 +352,7 @@ test = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.1"
version = "4.0.2"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -490,7 +490,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -619,7 +619,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.6.4"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -629,11 +629,12 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
{ url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
]
[[package]]
+10 -10
View File
@@ -262,7 +262,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0a2"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -274,14 +274,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "../langgraph" }
dependencies = [
{ name = "langchain-core" },
@@ -294,7 +294,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = "==1.3.0a2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "../prebuilt" },
{ name = "langgraph-sdk", editable = "." },
@@ -365,7 +365,7 @@ test = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.1"
version = "4.0.2"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -413,7 +413,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "../prebuilt" }
dependencies = [
{ name = "langchain-core" },
@@ -534,7 +534,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.20"
version = "0.7.31"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -547,9 +547,9 @@ dependencies = [
{ name = "xxhash" },
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