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Eugene YurtsevandHunter Lovell d1d4abf70e x 2025-07-28 14:21:55 -07:00
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name: "\U0001F41B Bug Report"
description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
labels: ["bug"]
type: bug
description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
labels: [pending,bug]
body:
- type: markdown
attributes:
value: |
Thank you for taking the time to file a bug report.
> **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy).
For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
* [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
* [API Reference Documentation](https://reference.langchain.com/python/),
* [LangChain ChatBot](https://chat.langchain.com/)
* [GitHub search](https://github.com/langchain-ai/langgraph),
Thank you for taking the time to file a bug report.
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
if there's another way to solve your problem:
* [LangChain Forum](https://forum.langchain.com/),
* [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
* [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
* [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
* [GitHub search](https://github.com/langchain-ai/langgraph),
- type: checkboxes
id: checks
attributes:
label: Checked other resources
description: Please confirm and check all the following options.
description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
options:
- label: This is a bug, not a usage question.
- label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
required: true
- label: I added a clear and descriptive title that summarizes this issue.
- label: I added a clear and detailed title that summarizes the issue.
required: true
- label: I used the GitHub search to find a similar question and didn't find it.
- label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
required: true
- label: I am sure that this is a bug in LangGraph rather than my code.
- label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
required: true
- label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
required: true
- label: This is not related to the langchain-community package.
required: true
- label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
required: true
- type: textarea
id: related
validations:
required: false
attributes:
label: Related Issues / PRs
description: |
If this bug is related to any existing issues or pull requests, please link them here.
placeholder: |
* e.g. #123, #456
- type: textarea
id: reproduction
validations:
required: true
attributes:
label: Reproduction Steps / Example Code (Python)
label: Example Code
description: |
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
* Reduce your code to the minimum required to reproduce the issue if possible.
(This will be automatically formatted into code, so no need for backticks.)
render: python
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
placeholder: |
from langgraph.graph import StateGraph
@@ -75,13 +47,17 @@ body:
chain = StateGraph(list)
chain.invoke('Hello!')
render: python
- type: textarea
id: error
validations:
required: false
attributes:
label: Error Message and Stack Trace (if applicable)
description: |
If you are reporting an error, please copy and paste the full error message and
stack trace.
(This will be automatically formatted into code, so no need for backticks.)
If you are reporting an error, please include the full error message and stack trace.
placeholder: |
Exception + full stack trace
render: shell
- type: textarea
id: description
@@ -102,18 +78,7 @@ body:
attributes:
label: System Info
description: |
Please share your system info with us.
Run the following command in your terminal and paste the output here:
`python -m langchain_core.sys_info`
or if you have an existing python interpreter running:
```python
from langchain_core import sys_info
sys_info.print_sys_info()
```
Run on your machine: `python -m langchain_core.sys_info`
placeholder: |
python -m langchain_core.sys_info
validations:
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blank_issues_enabled: false
version: 2.1
contact_links:
- name: 💬 LangChain Forum
url: https://forum.langchain.com/
about: General community discussions and support
- name: 📚 LangGraph Documentation
url: https://docs.langchain.com/oss/python/langgraph/overview
about: View the official LangGraph documentation
- name: 📚 API Reference Documentation
url: https://reference.langchain.com/python/langgraph/
about: View the official LangGraph API reference documentation
- name: 📚 Documentation issue
url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
about: Report an issue related to the LangGraph documentation
- name: LangChain Forum
url: https://forum.langchain.com/
about: General community discussions, support, and feature requests
+19
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@@ -0,0 +1,19 @@
name: Documentation
description: Report an issue related to the LangGraph documentation.
title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
labels: [documentation]
body:
- type: textarea
attributes:
label: "Issue with current documentation:"
description: >
Please make sure to leave a reference to the document/code you're
referring to.
- type: textarea
attributes:
label: "Idea or request for content:"
description: >
Please describe as clearly as possible what topics you think are missing
from the current documentation.
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name: "\U0001F512 Privileged"
description: You are a LangGraph maintainer. If not, check the other options.
name: 🔒 Privileged
description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
body:
- type: markdown
attributes:
value: |
> **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy).
Thanks for your interest in LangGraph! 🚀
If you are not a LangGraph maintainer, employee, or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
**Note:** Do not begin work on a PR unless explicitly assigned to this issue by a maintainer.
You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository
or are a regular contributor to LangGraph with previous merged merged pull requests.
- type: checkboxes
id: privileged
attributes:
label: Privileged issue
description: Confirm that you are allowed to create an issue here.
options:
- label: I am a LangGraph maintainer.
- label: I am a LangGraph maintainer, or was asked directly by a LangGraph maintainer to create an issue here.
required: true
- type: textarea
id: content
@@ -25,4 +26,4 @@ body:
- type: markdown
attributes:
value: |
Please do not begin work on a PR unless explicitly assigned to this issue by a maintainer.
Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.
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Fixes #
Thank you for contributing to LangGraph! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
<!-- Replace everything above this line with a 1-2 sentence description of your change. Keep the "Fixes #xx" keyword and update the issue number. -->
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres, checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do not include it in the PR.
Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview
- [ ] **PR message**: ***Delete this entire checklist*** and replace with
- **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
> **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy).
- [ ] **Add tests and docs**: If you're adding a new integration, you must include:
1. A test for the integration, preferably unit tests that do not rely on network access,
2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
If you paste a large clearly AI generated description here your PR may be IGNORED or CLOSED!
Thank you for contributing to LangGraph! Follow these steps to have your pull request considered as ready for review.
1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION
- feat(langgraph): add multi-tenant support
- Allowed TYPE and SCOPE values: https://github.com/langchain-ai/langgraph/blob/main/.github/workflows/pr_lint.yml#L19-L43
2. PR description:
- Write 1-2 sentences summarizing the change.
- The `Fixes #xx` line at the top is **required** for external contributions — update the issue number and keep the keyword. This links your PR to the approved issue and auto-closes it on merge.
- If there are any breaking changes, please clearly describe them.
- If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" in the description.
3. Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified.
- We will not consider a PR unless these three are passing in CI.
4. How did you verify your code works?
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
Additional guidelines:
- All external PRs must link to an issue or discussion where a solution has been approved by a maintainer, and you must be assigned to that issue. PRs without prior approval will be closed.
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## Social handles (optional)
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- Make sure optional dependencies are imported within a function.
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- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
-479
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# Threat Model: LangGraph
> Generated: 2026-03-28 | Commit: 0ba22143 | Scope: Full monorepo (all libs/)
> **Disclaimer:** This threat model is automatically generated to help developers and security researchers understand where trust is placed in this system and where boundaries exist. It is experimental, subject to change, and not an authoritative security reference — findings should be validated before acting on them. The analysis may be incomplete or contain inaccuracies. We welcome suggestions and corrections to improve this document.
For vulnerability reporting, see the [GitHub Security Advisories](https://github.com/langchain-ai/langgraph/security/advisories) page.
## Scope
### In Scope
- `libs/langgraph` — Core graph execution engine (Pregel, StateGraph, channels, functional API with `@entrypoint`/`@task`)
- `libs/prebuilt` — High-level agent APIs (ToolNode, create_react_agent, ValidationNode, InjectedState/InjectedStore/ToolRuntime injection)
- `libs/checkpoint` — Checkpoint serialization/deserialization (JsonPlusSerializer, EncryptedSerializer, BaseCache, stores, serde event hooks, SAFE_MSGPACK_TYPES allowlist)
- `libs/checkpoint-postgres` — PostgreSQL checkpoint saver, key-value store, and vector search
- `libs/checkpoint-sqlite` — SQLite checkpoint saver, key-value store, and vector search
- `libs/cli` — CLI for Docker-based deployment (`langgraph up/build/dev/new`), WebhookUrlPolicy
- `libs/sdk-py` — Python SDK client for LangGraph Server API (HttpClient, Auth system, Encryption handlers)
### Out of Scope
- `libs/sdk-js` — Moved to external `langchain-ai/langgraphjs` repository; no source in this repo
- `libs/checkpoint-conformance` — Conformance test suite only; not shipped code
- LangGraph Server / `langgraph-api` — Closed-source server runtime; not in this repo
- LangChain Core (`langchain-core`) — Upstream dependency; separate threat model
- User application code — Tools, prompts, model selection, deployment infrastructure
- LLM provider behavior — Model output content and safety
- LangSmith platform — Observability/tracing backend
- Tests, benchmarks, documentation — Not shipped code
### Assumptions
1. The project is used as a library/framework — users control their own application code, model selection, and deployment.
2. Checkpoint storage backends (databases) are deployed with proper access controls by the user.
3. LLM providers return well-formed responses per their documented API contracts.
4. The `langgraph.json` configuration file is developer-controlled and not user-supplied at runtime.
5. The CLI runs in a developer environment with Docker access.
6. The SDK connects to trusted LangGraph Server endpoints chosen by the user.
7. SDK Encryption handlers are developer-authored server-side code with application-level trust.
---
## System Overview
LangGraph is an open-source Python framework for building stateful, multi-actor AI agent applications. It provides a graph-based execution model (Bulk Synchronous Parallel via the Pregel engine) where user-defined nodes process shared state through typed channels. The framework supports two authoring APIs: the declarative StateGraph API and the functional API (`@entrypoint`/`@task` decorators). It includes checkpointing (persistence of graph state to databases), tool execution (dispatching LLM-generated tool calls with runtime injection of state/store/context), remote graph composition (calling LangGraph Server APIs), Docker-based deployment via a CLI, and a beta SDK encryption framework for custom at-rest encryption handlers.
### Architecture Diagram
```
+---------------------------------------------------------------------------+
| User Application |
| |
| +-----------------------------------------------+ |
| | User Application Code | |
| | (graph nodes, tools; StateGraph builder API | |
| | and functional API @entrypoint/@task both | |
| | compile to the same Pregel execution engine) | |
| +------------------------+----------------------+ |
| | |
| +------------v-----------+ |
| | StateGraph / Pregel | |
| | (core execution engine)| |
| +------+----------+------+ |
| | | |
| InjectedState +--v---------+ |
| InjectedStore | ToolNode | |
| ToolRuntime | (opt-in) | |
| +------------+ |
| | |
| - - - - - - - - - - - | - - - - TB1: User/Framework API - - - - - - - - |
| | |
| +-----v------+ +--------------+ |
| | Checkpoint | | RemoteGraph | |
| | Serializer | | (SDK client) | |
| |(jsonplus) | +------+--------+ |
| +-----+------+ | |
| | | |
| - - - - - - - - - - - | - - - - - - - -|- - TB2: Storage/Network - - - - |
| v v |
| +--------------+ +--------------+ |
| | PostgreSQL | | LangGraph | |
| | / SQLite | | Server API | |
| +--------------+ +--------------+ |
| |
| +----------+ +--------------+ |
| | CLI |------------------>| Docker | |
| |(langgraph| TB4: Config | Engine | |
| | up/build)| +--------------+ |
| +----------+ |
| |
| +--------------------+ |
| | SDK Encryption | TB5: Developer-authored handlers |
| | Handlers (beta) | (server-side execution in langgraph-api) |
| +--------------------+ |
+---------------------------------------------------------------------------+
```
---
## Components
| ID | Component | Description | Trust Level | Default? | Entry Points |
|----|-----------|-------------|-------------|----------|--------------|
| C1 | StateGraph / Pregel | Core graph builder and execution engine with v1/v2 output, durability modes (sync/async/exit), interrupt_before/interrupt_after | framework-controlled | Yes | `StateGraph.add_node()`, `StateGraph.compile()`, `Pregel.invoke()`, `Pregel.stream()` |
| C2 | JsonPlusSerializer | Checkpoint serialization/deserialization with msgpack, JSON, and pickle codecs; 47-entry SAFE_MSGPACK_TYPES allowlist | framework-controlled | Yes | `loads_typed()`, `dumps_typed()`, `_create_msgpack_ext_hook()`, `_reviver()` |
| C3 | ToolNode | Dispatches LLM-generated tool calls to registered BaseTool instances; supports InjectedState/InjectedStore/ToolRuntime injection into tools | framework-controlled | No (explicit opt-in required) | `ToolNode._func()`, `_run_one()`, `_execute_tool_sync()`, `_validate_tool_call()`, `_inject_tool_args()` |
| C4 | RemoteGraph | Client for remote LangGraph Server API; implements PregelProtocol | framework-controlled | No (opt-in) | `RemoteGraph.stream()`, `RemoteGraph.invoke()`, `RemoteGraph.get_state()` |
| C5 | PostgresSaver / PostgresStore | PostgreSQL checkpoint saver, key-value store, and vector search | framework-controlled | No (opt-in) | `from_conn_string()`, `put()`, `get_tuple()`, `search()` |
| C6 | SqliteSaver / SqliteStore | SQLite checkpoint saver, key-value store with JSON path filtering | framework-controlled | No (opt-in) | `from_conn_string()`, `put()`, `get_tuple()`, `search()` |
| C7 | EncryptedSerializer | AES-EAX authenticated encryption wrapper for checkpoint data | framework-controlled | No (opt-in) | `from_pycryptodome_aes()`, `loads_typed()`, `dumps_typed()` |
| C8 | CLI (langgraph_cli) | Docker-based build and deployment tooling; config schema includes WebhookUrlPolicy for SSRF protection | framework-controlled | No (separate install) | `langgraph up`, `langgraph build`, `langgraph dev`, `langgraph new` |
| C9 | SDK Client (langgraph_sdk) | HTTP client for LangGraph Server API with SSE streaming and reconnection | framework-controlled | Yes | `get_client()`, `get_sync_client()`, `HttpClient.request_reconnect()`, `HttpClient.stream()` |
| C10 | User-Registered Tools | BaseTool instances provided by users; may use InjectedState/InjectedStore/ToolRuntime annotations | user-controlled | N/A | Tool `invoke()` / `ainvoke()` methods |
| C11 | User-Registered Nodes | Arbitrary callables added via `add_node()` or `@task`/`@entrypoint` | user-controlled | N/A | Node function signatures |
| C12 | Checkpoint Storage | PostgreSQL or SQLite databases storing serialized graph state | external | N/A | Database connection interface |
| C13 | Functional API | `@entrypoint`/`@task` decorators for function-based workflow authoring with retry/cache policies | framework-controlled | Yes | `entrypoint.__call__()`, `task()`, `_TaskFunction.__call__()` (`libs/langgraph/langgraph/func/__init__.py`) |
| C14 | BaseCache | Cache layer for task results with JsonPlusSerializer (pickle_fallback=False) | framework-controlled | No (opt-in, requires checkpointer) | `get()`, `set()`, `clear()` (`libs/checkpoint/langgraph/cache/base/__init__.py`) |
| C15 | Serde Event Hooks | Monitoring system for serialization/deserialization events (msgpack_blocked, msgpack_unregistered_allowed, msgpack_method_blocked) | framework-controlled | Yes | `register_serde_event_listener()`, `emit_serde_event()` (`libs/checkpoint/langgraph/checkpoint/serde/event_hooks.py`) |
| C16 | Auth System (SDK) | Custom authentication/authorization handler framework | framework-controlled | No (opt-in) | `Auth.authenticate()`, `Auth.on()` handler registration (`libs/sdk-py/langgraph_sdk/auth/__init__.py`) |
| C17 | SDK Encryption Handlers (beta) | Custom at-rest encryption/decryption framework; supports blob and JSON handlers with per-model/field context; server-side execution | framework-controlled | No (opt-in, beta) | `Encryption.encrypt.blob()`, `Encryption.encrypt.json()`, `Encryption.decrypt.blob()`, `Encryption.decrypt.json()`, `Encryption.context()` (`libs/sdk-py/langgraph_sdk/encryption/__init__.py`) |
---
## Data Classification
| ID | PII Category | Specific Fields | Sensitivity | Storage Location(s) | Encrypted at Rest | Retention | Regulatory |
|----|-------------|----------------|-------------|---------------------|-------------------|-----------|------------|
| DC1 | API credentials | `x-api-key` header, `LANGGRAPH_API_KEY`, `LANGSMITH_API_KEY`, `LANGCHAIN_API_KEY` env vars | Critical | Environment variables, HTTP headers in transit | N/A (in-memory) | Session lifetime | All — breach trigger |
| DC2 | Encryption keys | `LANGGRAPH_AES_KEY` env var, `key` parameter to `from_pycryptodome_aes()` | Critical | Environment variable, in-memory | N/A | Application lifetime | All — breach trigger |
| DC3 | Serialized graph state | Checkpoint data in `checkpoints` and `writes` tables (msgpack/JSON/pickle bytes) | High | PostgreSQL (BYTEA), SQLite (BLOB) | Optional via EncryptedSerializer or SDK Encryption Handlers | Unbounded (no default TTL) | GDPR if state contains PII |
| DC4 | Store key-value data | User-stored items in `store` tables via BaseStore | High | PostgreSQL, SQLite | No (plaintext JSON); optional via SDK Encryption Handlers | Configurable TTL, default unbounded | GDPR if contains PII |
| DC5 | Checkpoint metadata | `thread_id`, `checkpoint_ns`, `run_id`, `step`, `source` | Medium | PostgreSQL, SQLite (metadata JSONB/JSON column) | No | Same as DC3 | Minimal |
| DC6 | Agent conversation history | LangChain messages (HumanMessage, AIMessage, ToolMessage) serialized in checkpoint state | High | PostgreSQL, SQLite (within DC3 checkpoint bytes) | Only if DC3 encrypted | Unbounded | GDPR, CCPA if contains user PII |
| DC7 | Connection strings | PostgreSQL URIs, SQLite file paths passed to `from_conn_string()` | Critical | Application code, environment variables | N/A (in-memory) | Application lifetime | All — may contain credentials |
| DC8 | Vector embeddings | Document embeddings in `store_vectors` table | Low | PostgreSQL (pgvector), SQLite (vec extension) | No | Same as DC4 | Minimal |
| DC9 | SDK Encryption context metadata | `EncryptionContext.metadata` dict passed to encryption handlers | Medium | In-memory per request; persisted with encrypted data | N/A (context, not payload) | Request lifetime + persistence alongside encrypted data | Depends on content |
### Data Classification Details
#### DC1: API Credentials
- **Fields**: `x-api-key` HTTP header, `LANGGRAPH_API_KEY`/`LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables
- **Storage**: Environment variables (loaded at runtime), HTTP request headers (in transit)
- **Access**: SDK client code (`libs/sdk-py/langgraph_sdk/_shared/utilities.py:_get_api_key`), any process with env var access
- **Encryption**: TLS in transit (if HTTPS); no at-rest encryption for env vars
- **Retention**: Session/process lifetime
- **Logging exposure**: API key stripped of quotes but could appear in debug logs if HTTP headers are logged. `RESERVED_HEADERS` prevents user override of `x-api-key` but doesn't prevent logging.
- **Cross-border**: Travels with every HTTP request to the LangGraph Server
- **Gaps**: SDK `request_reconnect()` and `stream()` forward `x-api-key` header to server-controlled `Location` redirect URLs without URL validation (see T9)
#### DC2: Encryption Keys
- **Fields**: `LANGGRAPH_AES_KEY` environment variable, `key` bytes parameter
- **Storage**: Environment variable or direct bytes in application code
- **Access**: `libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:from_pycryptodome_aes`
- **Encryption**: N/A — this IS the encryption key
- **Retention**: Application lifetime
- **Logging exposure**: Not logged by framework code
- **Gaps**: Key loaded from env var as UTF-8 string limits entropy to ~6.57 bits/byte (see T7). Cipher name validated with `assert` which is stripped by `python -O` (see T8).
#### DC3: Serialized Graph State
- **Fields**: All channel values serialized via `JsonPlusSerializer.dumps_typed()` — includes complete agent state, conversation history, tool call results, and any user-defined state
- **Storage**: PostgreSQL `checkpoints.checkpoint` (BYTEA), `writes.blob` (BYTEA); SQLite `checkpoints.checkpoint` (BLOB), `writes.blob` (BLOB)
- **Access**: Any code with database credentials; `BaseCheckpointSaver.get_tuple()` / `put()`
- **Encryption**: Optional via `EncryptedSerializer` wrapping (AES-EAX) or SDK Encryption Handlers (beta, server-side). Not encrypted by default.
- **Retention**: Unbounded by default. Optional TTL via `CheckpointerConfig.ttl` (server-side config)
- **Logging exposure**: Serde event hooks emit module/class names of deserialized types but not the data itself
- **Gaps**: Default unbounded retention of potentially PII-containing state. Unencrypted by default. EncryptedSerializer has fallback that accepts unencrypted data (see T10).
#### DC6: Agent Conversation History
- **Fields**: `HumanMessage.content`, `AIMessage.content`, `ToolMessage.content`, `AIMessage.tool_calls` — embedded within DC3 checkpoint bytes
- **Storage**: Same as DC3 (within serialized checkpoint data)
- **Access**: Same as DC3
- **Encryption**: Only if DC3 is encrypted via EncryptedSerializer or SDK Encryption Handlers
- **Retention**: Same as DC3 (unbounded default)
- **Gaps**: Conversation content may include user PII, PHI, or sensitive business data. No field-level encryption or redaction. Retention inherits from DC3 with no conversation-specific policy.
#### DC9: SDK Encryption Context Metadata
- **Fields**: `EncryptionContext.model` (str), `EncryptionContext.field` (str), `EncryptionContext.metadata` (dict)
- **Storage**: In-memory during request processing; persisted alongside encrypted data for later decryption
- **Access**: Encryption/decryption handlers (developer-authored), ContextHandler (receives authenticated BaseUser)
- **Encryption**: N/A — this is context for encryption, not encrypted data itself
- **Retention**: Persisted with encrypted data indefinitely
- **Logging exposure**: Not logged by SDK code
- **Gaps**: `metadata` is a mutable dict — whether cross-request isolation is enforced depends on server-side implementation (langgraph-api, out of scope). ContextHandler registration at `libs/sdk-py/langgraph_sdk/encryption/__init__.py:Encryption.context` does not call `_validate_handler` (missing async/param-count validation, unlike all other handler types).
---
## Trust Boundaries
| ID | Boundary | Description | Controls (Inside) | Does NOT Control (Outside) |
|----|----------|-------------|-------------------|---------------------------|
| TB1 | User/Framework API | Where user-provided code and configuration enters the framework | Graph execution logic, channel semantics, default configs, validation of graph structure, tool injection merge order (system values overwrite LLM values) | User node implementations, tool behavior, model selection, prompt construction, state schema design |
| TB2 | Checkpoint Storage | Where serialized data enters/leaves the persistence layer | Serialization format, allowlists for deserialization (47 safe types, 1 safe method), encryption (if configured), serde event hooks | Database access controls, who can write to the checkpoint tables, storage infrastructure security |
| TB3 | Remote API | Where data crosses the network to/from LangGraph Server | Outbound config sanitization (`_sanitize_config`), SDK HTTP transport, API key handling, `RESERVED_HEADERS` | Remote server behavior, response content integrity, network security (TLS), server-provided Location redirect targets |
| TB4 | CLI Config/Docker | Where developer config drives container image generation | Dockerfile template structure, config schema validation (including WebhookUrlPolicy), list-based subprocess args, build command content validation | `langgraph.json` file content, Docker daemon security, host filesystem |
| TB5 | SDK Encryption Handlers | Where developer-authored encryption handlers process sensitive data | Handler signature validation (async, 2-param for encrypt/decrypt), duplicate registration prevention, EncryptionContext construction | Handler implementation correctness, key management, actual encrypt/decrypt behavior, server-side execution environment |
### Boundary Details
#### TB1: User/Framework API
- **Inside**: Graph compilation validates structure (`libs/langgraph/langgraph/pregel/_validate.py:validate_graph`). Channel types enforce update semantics (`libs/langgraph/langgraph/channels/base.py:BaseChannel.update`). Functional API validates entrypoint has at least one parameter (`libs/langgraph/langgraph/func/__init__.py:entrypoint`). Sensitive config keys filtered from metadata propagation — keys containing "key", "token", "secret", "password", "auth" are excluded (`libs/langgraph/langgraph/_internal/_config.py:_exclude_as_metadata`). Tool injection merge order ensures system-injected values (InjectedState/InjectedStore/ToolRuntime) overwrite any LLM-supplied collisions (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._inject_tool_args` line 1380). Injected parameter names hidden from LLM tool schema via `tool_call_schema` filtering.
- **Outside**: What user nodes do, what tools return, what LLMs generate, how users handle output.
- **Crossing mechanism**: Python function calls — `add_node(callable)`, `add_edge()`, `compile(checkpointer=...)`, `@entrypoint`, `@task`.
#### TB2: Checkpoint Storage
- **Inside**: `JsonPlusSerializer` controls serialization format (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer`). Msgpack type allowlist (`libs/checkpoint/langgraph/checkpoint/serde/_msgpack.py:SAFE_MSGPACK_TYPES` — 47 safe types including stdlib, langchain_core messages, and langgraph types). Msgpack method allowlist (`libs/checkpoint/langgraph/checkpoint/serde/_msgpack.py:SAFE_MSGPACK_METHODS` — 1 safe method: `datetime.datetime.fromisoformat`). JSON module allowlist (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:_check_allowed_json_modules`). Serde event hooks for monitoring (`libs/checkpoint/langgraph/checkpoint/serde/event_hooks.py:emit_serde_event`). Optional `EncryptedSerializer` wrapping (`libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:EncryptedSerializer`). SQLite filter key regex validation (`libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py:_validate_filter_key`). Parameterized SQL queries in both Postgres and SQLite backends.
- **Outside**: Database access controls, who can read/write checkpoint tables, storage backend integrity.
- **Crossing mechanism**: Database read/write operations — serialized bytes stored as BYTEA (Postgres) or BLOB (SQLite).
#### TB3: Remote API
- **Inside**: `_sanitize_config()` strips non-primitive values and drops checkpoint-internal keys from outbound config (`libs/langgraph/langgraph/pregel/remote.py:_sanitize_config`). SDK handles API key from env vars (`libs/sdk-py/langgraph_sdk/_shared/utilities.py:_get_api_key`). `RESERVED_HEADERS` prevents user override of `x-api-key` (`libs/sdk-py/langgraph_sdk/_shared/utilities.py:RESERVED_HEADERS`).
- **Outside**: Remote server response content, network integrity, whether the server is legitimate, server-provided Location redirect targets.
- **Crossing mechanism**: HTTPS requests via `httpx` through `langgraph_sdk`.
#### TB4: CLI Config/Docker
- **Inside**: Config file parsed as JSON (`libs/cli/langgraph_cli/config.py:validate_config_file`). Docker subprocess invoked with list-based args via `asyncio.create_subprocess_exec`, not `shell=True` (`libs/cli/langgraph_cli/exec.py:subp_exec`). Template downloads from hardcoded GitHub URLs (`libs/cli/langgraph_cli/templates.py`). Config schema validation covers store, auth, encryption, http, webhooks, checkpointer, and ui sections (`libs/cli/langgraph_cli/schemas.py`). Build command content validation blocks shell metacharacters (`libs/cli/langgraph_cli/config.py:has_disallowed_build_command_content`). WebhookUrlPolicy (`libs/cli/langgraph_cli/schemas.py:WebhookUrlPolicy`) supports `require_https`, `allowed_domains`, `allowed_ports`, `max_url_length`, `disable_loopback` for SSRF protection.
- **Outside**: Content of `langgraph.json`, Docker daemon behavior, filesystem permissions.
- **Crossing mechanism**: JSON file read, subprocess execution, ZIP download/extraction.
#### TB5: SDK Encryption Handlers
- **Inside**: Handler signature validation — must be async, must accept exactly 2 positional params (`libs/sdk-py/langgraph_sdk/encryption/__init__.py:_validate_handler`). Duplicate handler registration prevention (`DuplicateHandlerError`). `EncryptionContext` construction with model/field/metadata (`libs/sdk-py/langgraph_sdk/encryption/types.py:EncryptionContext`). JSON key preservation constraint documented (enforced server-side).
- **Outside**: Handler implementation correctness, key management strategy, actual encryption/decryption logic, server-side execution in langgraph-api.
- **Crossing mechanism**: Python decorator registration at import time; server-side invocation at runtime.
---
## Data Flows
| ID | Source | Destination | Data Type | Classification | Crosses Boundary | Protocol |
|----|--------|-------------|-----------|----------------|------------------|----------|
| DF1 | C12 (Checkpoint Storage) | C2 (JsonPlusSerializer) | Serialized checkpoint bytes (msgpack/JSON/pickle) | DC3 | TB2 | Database read |
| DF2 | C2 (JsonPlusSerializer) | C1 (Pregel) | Deserialized Python objects (channel state) | DC3, DC6 | TB2 | Function call |
| DF3 | LLM (external) | C3 (ToolNode) | Tool call arguments (JSON strings in AIMessage) | — | TB1 | Function call (via langchain-core) |
| DF4 | C3 (ToolNode) | C10 (User Tools) | Parsed argument dicts merged with injected state/store/runtime | — | TB1 | `tool.invoke(call_args)` |
| DF5 | C4 (RemoteGraph) | C1 (Pregel) | Stream chunks (JSON-deserialized dicts) | — | TB3 | HTTPS / SSE |
| DF6 | `langgraph.json` | C8 (CLI) | Config dict (graphs, env, store, auth, encryption, http, webhooks, checkpointer, ui) | — | TB4 | `json.load()` |
| DF7 | C8 (CLI) | Docker | Dockerfile content with embedded ENV values | — | TB4 | `asyncio.create_subprocess_exec` |
| DF8 | C11 (User Nodes) | C1 (Pregel) | State updates (arbitrary Python objects) | — | TB1 | Channel write |
| DF9 | C9 (SDK Client) | C4 (RemoteGraph) | API responses (JSON) | — | TB3 | HTTPS |
| DF10 | User config | C7 (EncryptedSerializer) | AES key from LANGGRAPH_AES_KEY env var | DC2 | TB2 | `os.getenv()` |
| DF11 | C12 (Checkpoint Storage) | C14 (BaseCache) | Cached task results via JsonPlusSerializer | DC3 | TB2 | Database read |
| DF12 | LangGraph Server | C9 (SDK Client) | HTTP responses with Location header | DC1 | TB3 | HTTP redirect |
| DF13 | C9 (SDK Client) | Redirect target | Request headers including x-api-key | DC1 | TB3 | HTTPS |
| DF14 | C1 (Pregel state) | C3 (ToolNode) | InjectedState/InjectedStore/ToolRuntime values for tool injection | DC3, DC4 | TB1 | Function call (dict merge) |
| DF15 | Developer code | C17 (SDK Encryption Handlers) | Encryption/decryption handler functions and context handler | — | TB5 | Python decorator registration |
### Flow Details
#### DF1: Checkpoint Storage -> JsonPlusSerializer
- **Data**: Serialized graph state as `(type_tag, bytes)` tuples. Type tags include `"msgpack"`, `"json"`, `"pickle"`, `"bytes"`, `"null"`. When encrypted: `"msgpack+aes"`, `"json+aes"`.
- **Validation**: Type tag dispatches to codec. Msgpack: `_create_msgpack_ext_hook` with allowlist check — `SAFE_MSGPACK_TYPES` (47 entries) always checked first, then `allowed_modules` determines behavior for unregistered types (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:_create_msgpack_ext_hook`). JSON: `_reviver` with `lc:2` module allowlist. Pickle: **no restrictions** (`pickle.loads(data_)` if `pickle_fallback=True`, `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer.loads_typed`). The proposed `secure_pickle.py` with `RestrictedUnpickler` was documented in `SECURITY_FIX_SUMMARY.md` but never merged.
- **Trust assumption**: Checkpoint storage is access-controlled. An attacker with write access to the database can craft malicious checkpoint data.
#### DF3: LLM -> ToolNode
- **Data**: Tool call name and arguments from LLM-generated `AIMessage.tool_calls`.
- **Validation**: Tool name checked against registered `tools_by_name` dict — unknown names return error `ToolMessage` (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._validate_tool_call`). Argument values validated only by the target tool's Pydantic schema.
- **Trust assumption**: LLM output is treated as untrusted for tool name routing but argument values pass through to tools without ToolNode-level sanitization.
#### DF4: ToolNode -> User Tools (with Injection)
- **Data**: Parsed argument dicts from LLM, merged with system-injected InjectedState/InjectedStore/ToolRuntime values.
- **Validation**: Four-layer defense: (1) Injected parameter names hidden from LLM via `tool_call_schema` filtering. (2) Dict merge `{**llm_args, **injected_args}` places system values last — system always wins on collision (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._inject_tool_args` line 1380). (3) Pydantic `model_validate` with default `extra="ignore"` drops unknown keys. (4) Output construction only includes declared model fields.
- **Trust assumption**: LLM-provided arguments cannot override system-injected values due to merge order.
#### DF5: RemoteGraph -> Pregel
- **Data**: Stream event chunks containing dicts for `Interrupt`, `Command`, state snapshots.
- **Validation**: **None** on inbound data. `Interrupt(**i)` uses dict-splatting with no schema check (`libs/langgraph/langgraph/pregel/remote.py:RemoteGraph.stream`). `Command(**chunk.data)` uses dict-splatting for parent commands.
- **Trust assumption**: Remote server is trusted. A compromised or malicious server can inject arbitrary field values.
#### DF6: langgraph.json -> CLI
- **Data**: JSON config including `graphs`, `env`, `store`, `auth`, `encryption`, `http`, `webhooks`, `checkpointer`, `ui`, `ui_config` sections.
- **Validation**: Schema validation in `validate_config_file()` (`libs/cli/langgraph_cli/config.py:validate_config_file`). Config values embedded in Dockerfile via `json.dumps()` in single-quoted `ENV` lines (`libs/cli/langgraph_cli/config.py:python_config_to_docker`). Build command content validation (`libs/cli/langgraph_cli/config.py:has_disallowed_build_command_content`) blocks shell metacharacters.
- **Trust assumption**: `langgraph.json` is developer-authored. Single quotes in config values could break Dockerfile `ENV` syntax.
#### DF11: Checkpoint Storage -> BaseCache
- **Data**: Cached task results stored via `BaseCache.set()` and retrieved via `BaseCache.get()`.
- **Validation**: Uses `JsonPlusSerializer(pickle_fallback=False)` by default (`libs/checkpoint/langgraph/cache/base/__init__.py:BaseCache`). Subject to same msgpack deserialization behavior as DF1 (allowed_modules defaults based on `LANGGRAPH_STRICT_MSGPACK`).
- **Trust assumption**: Cache storage has same access controls as checkpoint storage.
#### DF12-13: Server -> SDK -> Redirect Target (API Key Leak)
- **Data**: Server provides `Location` header in HTTP response. SDK follows the redirect and sends all original request headers (including `x-api-key`) to the target URL.
- **Validation**: **None** on Location URL. No allowlist, no same-origin check, no header stripping on cross-origin redirect.
- **Trust assumption**: The LangGraph Server is trusted to not redirect to malicious URLs. Violated if server is compromised.
#### DF14: Pregel State -> ToolNode (Runtime Injection)
- **Data**: Graph state dict (InjectedState), BaseStore instance (InjectedStore), ToolRuntime object (containing state, config, store, context, stream_writer, tool_call_id).
- **Validation**: Injection targets determined by tool type annotations at compile time. Injected values overwrite any LLM-provided values with matching keys (safe merge order). Pydantic validation on tool input drops extra keys not in the tool's declared schema.
- **Trust assumption**: System-injected values are trusted; LLM-provided values cannot interfere due to merge order guarantees.
#### DF15: Developer Code -> SDK Encryption Handlers
- **Data**: Async Python callables registered via decorators for blob/JSON encryption/decryption and context derivation.
- **Validation**: `_validate_handler` checks async-ness and 2-param signature for encrypt/decrypt handlers. `DuplicateHandlerError` prevents double registration. **Gap**: `Encryption.context()` method does NOT call `_validate_handler` — a sync function or wrong param count passes registration and fails only at server-side invocation (`libs/sdk-py/langgraph_sdk/encryption/__init__.py:Encryption.context`).
- **Trust assumption**: Handler authors are application developers with server-level trust.
---
## Threats
| ID | Data Flow | Classification | Threat | Boundary | Severity | Validation | Code Reference |
|----|-----------|----------------|--------|----------|----------|------------|----------------|
| T1 | DF1, DF11 | DC3 | Arbitrary code execution via msgpack deserialization when strict mode is OFF (default) | TB2 | High | Verified | `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:_create_msgpack_ext_hook` |
| T2 | DF1 | DC3 | Arbitrary code execution via `pickle.loads` when `pickle_fallback=True` | TB2 | High | Verified | `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer.loads_typed` |
| T3 | DF1 | DC3 | Arbitrary module import/execution via JSON `lc:2` constructor when `allowed_json_modules=True` | TB2 | High | Verified | `libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer._revive_lc2` |
| T4 | DF5 | — | Unvalidated dict-splatting from remote API into `Interrupt`/`Command` objects | TB3 | Medium | Likely | `libs/langgraph/langgraph/pregel/remote.py:RemoteGraph.stream` |
| T5 | DF6, DF7 | — | Dockerfile ENV injection via single-quote in `langgraph.json` config values | TB4 | Low | Likely | `libs/cli/langgraph_cli/config.py:python_config_to_docker` |
| T6 | DF7 | — | ZIP slip in `langgraph new` template extraction | TB4 | Low | Unverified | `libs/cli/langgraph_cli/templates.py:_download_repo_with_requests` |
| T7 | DF10 | DC2 | AES key entropy limited to printable characters via env var string encoding | TB2 | Info | — | `libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:EncryptedSerializer.from_pycryptodome_aes` |
| T8 | DF10 | DC2 | EncryptedSerializer cipher name check uses `assert` (stripped with `python -O`) | TB2 | Low | Verified | `libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:PycryptodomeAesCipher.decrypt` |
| T9 | DF12, DF13 | DC1 | SDK API key leak via server-controlled Location redirect to attacker-controlled URL | TB3 | Medium | Verified | `libs/sdk-py/langgraph_sdk/_async/http.py:HttpClient.request_reconnect`, `libs/sdk-py/langgraph_sdk/_async/http.py:HttpClient.stream` |
| T10 | DF1 | DC3 | EncryptedSerializer silently accepts unencrypted data — attacker bypasses encryption by writing plain type tags | TB2 | Medium | Verified | `libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:EncryptedSerializer.loads_typed` |
| T11 | DF1, DF11 | DC3, DC6 | Unbounded retention of checkpoint data containing PII/conversation history | TB2 | Medium | — | `libs/checkpoint/langgraph/checkpoint/base/__init__.py:BaseCheckpointSaver` |
### Threat Details
#### T1: Msgpack Deserialization RCE (Default Config)
- **Flow**: DF1 (Checkpoint Storage -> JsonPlusSerializer), DF11 (Checkpoint Storage -> BaseCache)
- **Description**: When `LANGGRAPH_STRICT_MSGPACK` is not set (the default), the msgpack `_create_msgpack_ext_hook` allows **any** `(module, class)` pair stored in checkpoint data to be imported via `importlib.import_module` and instantiated with attacker-controlled arguments. The `SAFE_MSGPACK_TYPES` allowlist (47 entries) is checked first, but unregistered types are logged as warnings and allowed through when `allowed_modules=True` (the default when strict mode is off). Seven EXT codes are processed: `EXT_CONSTRUCTOR_SINGLE_ARG` (0), `EXT_CONSTRUCTOR_POS_ARGS` (1), `EXT_CONSTRUCTOR_KW_ARGS` (2), `EXT_METHOD_SINGLE_ARG` (3), `EXT_PYDANTIC_V1` (4), `EXT_PYDANTIC_V2` (5), `EXT_NUMPY_ARRAY` (6). The `BaseCache` component uses `JsonPlusSerializer(pickle_fallback=False)` but inherits the same msgpack `allowed_modules` default behavior. The proposed `RestrictedUnpickler` (`secure_pickle.py`) documented in `SECURITY_FIX_SUMMARY.md` was never merged — pickle remains unrestricted when enabled.
- **Preconditions**: Attacker must have write access to the checkpoint database (PostgreSQL or SQLite). This requires compromised database credentials or a co-located attacker.
#### T2: Pickle Deserialization RCE
- **Flow**: DF1 (Checkpoint Storage -> JsonPlusSerializer)
- **Description**: When `pickle_fallback=True` is explicitly passed to `JsonPlusSerializer`, checkpoint data with type tag `"pickle"` is deserialized via `pickle.loads()` with zero restrictions (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer.loads_typed`).
- **Preconditions**: (1) Application or checkpointer explicitly enables `pickle_fallback=True`. (2) Attacker writes `("pickle", <payload>)` to checkpoint storage.
#### T3: JSON lc:2 Constructor RCE
- **Flow**: DF1 (Checkpoint Storage -> JsonPlusSerializer)
- **Description**: The JSON `_reviver` handles `lc:2` type constructors by importing the module path from checkpoint JSON data via `importlib.import_module` (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:JsonPlusSerializer._revive_lc2`). If `allowed_json_modules=True` (explicit opt-in), any module reachable in the Python environment can be imported and instantiated. The method also supports method chaining — a `method` key in the JSON can call arbitrary methods on the imported class.
- **Preconditions**: (1) `allowed_json_modules` set to `True` (not the default). (2) Attacker writes crafted JSON to checkpoint storage.
#### T4: RemoteGraph Unvalidated Inbound Data
- **Flow**: DF5 (RemoteGraph -> Pregel)
- **Description**: Stream events from the remote LangGraph Server are deserialized from JSON and dict-splatted into `Interrupt(**i)` and `Command(**chunk.data)` without schema validation (`libs/langgraph/langgraph/pregel/remote.py:RemoteGraph.stream`). A compromised or malicious remote server can inject unexpected fields. `Command.update` can carry arbitrary state modifications; `Command.goto` can alter graph execution flow. `Interrupt` accepts `**deprecated_kwargs` which includes a `ns` parameter that can override interrupt ID generation via `xxh3_128_hexdigest`.
- **Preconditions**: User connects `RemoteGraph` to a compromised or attacker-controlled server URL.
#### T5: Dockerfile ENV Single-Quote Injection
- **Flow**: DF6, DF7 (langgraph.json -> CLI -> Dockerfile)
- **Description**: Config values from `langgraph.json` are serialized via `json.dumps()` and embedded in single-quoted `ENV` directives across multiple config sections (store, auth, encryption, http, webhooks, checkpointer, ui, ui_config, graphs). JSON does not escape single quotes, so a config value containing `'` could break the Dockerfile syntax or inject additional Dockerfile instructions. The pattern is duplicated in two Dockerfile generation functions (`libs/cli/langgraph_cli/config.py:python_config_to_docker` and `libs/cli/langgraph_cli/config.py:node_config_to_docker`).
- **Preconditions**: A `langgraph.json` config value contains a single quote character.
#### T6: ZIP Slip in Template Extraction
- **Flow**: DF7 (CLI template download)
- **Description**: `langgraph new` downloads a ZIP from GitHub and uses `zip_file.extractall(path)`. If the archive contains path-traversal entries (e.g., `../../etc/cron.d/exploit`), files could be written outside the target directory.
- **Preconditions**: The GitHub-hosted template archive must contain malicious path entries. This requires compromise of the upstream template repo.
#### T7: AES Key Entropy via Environment Variable
- **Flow**: DF10 (User config -> EncryptedSerializer)
- **Description**: The AES key is loaded from `LANGGRAPH_AES_KEY` as a UTF-8 string and `.encode()`d to bytes (`libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:EncryptedSerializer.from_pycryptodome_aes`). This limits key entropy to printable characters (~6.57 bits/byte vs. 8 bits/byte for random bytes), reducing effective key strength for AES-128 from 128 bits to ~105 bits.
- **Preconditions**: User relies on environment variable path for key loading (vs. passing raw bytes directly via `key=` parameter).
#### T8: EncryptedSerializer Assert Bypass
- **Flow**: DF10 (Encrypted checkpoint data)
- **Description**: The cipher name check in `decrypt()` uses `assert ciphername == "aes"` (`libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:PycryptodomeAesCipher.decrypt`), which is stripped when Python runs with `-O` (optimize) flag. The `ciphername` value comes from the type tag in checkpoint storage (split from the `type+cipher` format).
- **Preconditions**: Python running with `-O` flag AND attacker can write to checkpoint storage.
#### T9: SDK API Key Leak via Server-Controlled Location Redirect
- **Flow**: DF12 (Server -> SDK), DF13 (SDK -> Redirect target)
- **Description**: The SDK's `HttpClient.request_reconnect()` (`libs/sdk-py/langgraph_sdk/_async/http.py:HttpClient.request_reconnect`) follows server-provided `Location` headers and forwards the full `request_headers` dict (including the `x-api-key` authentication header) to the redirected URL. The `HttpClient.stream()` method (`libs/sdk-py/langgraph_sdk/_async/http.py:HttpClient.stream`) also follows `Location` headers for SSE reconnection and forwards `reconnect_headers` (which include `x-api-key`) to the server-controlled URL. No URL validation, same-origin check, or sensitive header stripping is performed before following the redirect. The same pattern exists in the sync client (`libs/sdk-py/langgraph_sdk/_sync/http.py`).
- **Preconditions**: (1) User connects SDK to a LangGraph Server that is compromised or attacker-controlled. (2) The server returns a response with a `Location` header pointing to an attacker-controlled URL.
#### T10: EncryptedSerializer Encryption Bypass via Unencrypted Data Injection
- **Flow**: DF1 (Checkpoint Storage -> EncryptedSerializer)
- **Description**: `EncryptedSerializer.loads_typed()` (`libs/checkpoint/langgraph/checkpoint/serde/encrypted.py:EncryptedSerializer.loads_typed`) checks if the type tag contains a `+` delimiter. If it does not (e.g., type tag is `"msgpack"` instead of `"msgpack+aes"`), the data is passed directly to the inner serde's `loads_typed()` **without any decryption or MAC verification**. An attacker with write access to checkpoint storage can bypass the encryption layer entirely by writing data with a plain type tag.
- **Preconditions**: (1) Application uses `EncryptedSerializer` for checkpoint protection. (2) Attacker has write access to checkpoint storage.
#### T11: Unbounded Checkpoint Data Retention
- **Flow**: DF1, DF11 (Checkpoint Storage lifecycle)
- **Description**: Checkpoint data (DC3, DC6) is retained indefinitely by default. No built-in TTL, pruning, or data lifecycle management in the library-level checkpoint savers. Conversation history containing user PII may accumulate without bounds.
- **Preconditions**: Application uses checkpointing (the primary use case). No explicit cleanup configured.
---
## Input Source Coverage
| Input Source | Data Flows | Threats | Validation Points | Responsibility | Gaps |
|-------------|-----------|---------|-------------------|----------------|------|
| User direct input (graph state, config) | DF8 | — | Graph structure validation (`libs/langgraph/langgraph/pregel/_validate.py:validate_graph`), channel type enforcement (`libs/langgraph/langgraph/channels/base.py:BaseChannel`), sensitive key filtering (`libs/langgraph/langgraph/_internal/_config.py:_exclude_as_metadata`) | User | Node implementation safety is user's responsibility |
| LLM output (tool calls) | DF3, DF4, DF14 | — | Tool name allowlist (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._validate_tool_call`), tool Pydantic schemas, injection merge order (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._inject_tool_args`), `tool_call_schema` filtering of injected params | Shared (project validates name and injection safety; user validates args via tool schema) | No ToolNode-level argument sanitization beyond injection overwrite |
| Checkpoint storage data | DF1, DF2, DF11 | T1, T2, T3, T10 | Msgpack allowlist (`libs/checkpoint/langgraph/checkpoint/serde/_msgpack.py:SAFE_MSGPACK_TYPES` — 47 entries), msgpack method allowlist (`libs/checkpoint/langgraph/checkpoint/serde/_msgpack.py:SAFE_MSGPACK_METHODS`), JSON allowlist (`libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py:_check_allowed_json_modules`), pickle gating, serde event hooks, optional encryption | Shared (project owns serializer defaults; user owns DB access controls) | Default msgpack mode allows unregistered types; EncryptedSerializer accepts unencrypted data; proposed secure_pickle.py never merged |
| Remote API responses | DF5, DF9, DF12, DF13 | T4, T9 | Outbound config sanitization (`libs/langgraph/langgraph/pregel/remote.py:_sanitize_config`); no inbound validation; no redirect URL validation | User (user chooses which server to trust) | No inbound schema validation; API key forwarded on redirects |
| Configuration (langgraph.json) | DF6, DF7 | T5 | JSON schema validation (`libs/cli/langgraph_cli/config.py:validate_config_file`), build command content validation (`libs/cli/langgraph_cli/config.py:has_disallowed_build_command_content`), list-based subprocess args, WebhookUrlPolicy (`libs/cli/langgraph_cli/schemas.py:WebhookUrlPolicy`) | User (developer-controlled file) | Single-quote not escaped in ENV embedding |
| Configuration (env vars) | DF10 | T7, T8 | AES key length validation, EAX MAC verification | User (deployer controls env) | Key entropy, assert-based check |
| Developer encryption handlers | DF15 | — | Handler signature validation (`libs/sdk-py/langgraph_sdk/encryption/__init__.py:_validate_handler`), duplicate prevention | User (developer-authored code) | `context()` handler missing `_validate_handler` call |
---
## Out-of-Scope Threats
Threats that appear valid in isolation but fall outside project responsibility because they depend on conditions the project does not control.
| Pattern | Why Out of Scope | Project Responsibility Ends At |
|---------|-----------------|-------------------------------|
| Prompt injection leading to arbitrary tool execution | Project does not control LLM model behavior, user prompt construction, or which tools are registered. ToolNode routes by name only to user-registered tools. | Providing tool name allowlist routing (`libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._validate_tool_call`); user owns tool registration and argument handling |
| State poisoning via malicious node output | User-registered nodes (including `@task`-decorated functions) can write arbitrary values to channels. The framework executes nodes as provided. | Enforcing channel type contracts (`libs/langgraph/langgraph/channels/base.py:BaseChannel.update`); user owns node implementation correctness |
| Cross-session state access via thread_id guessing | Checkpoint savers index by `thread_id`. Without application-level auth, any caller with a valid thread_id can access that thread's state. | Providing the `Auth` handler system for access control (`libs/sdk-py/langgraph_sdk/auth/__init__.py:Auth`); user must implement auth handlers |
| Tool shadowing via duplicate registration | If a user registers two tools with the same name, ToolNode uses the last one. This is user misconfiguration. | Documenting tool registration semantics |
| Indirect prompt injection via tool output | LLM reads tool output and may follow injected instructions. This is a fundamental LLM limitation, not a framework vulnerability. | Not including tool output in system prompts; user owns output handling |
| Model selecting dangerous tool arguments | An LLM may generate SQL injection, path traversal, or command injection payloads as tool arguments. The risk depends entirely on what the user's tools do with those arguments. | Routing tool calls to registered tools only; user owns tool input validation |
| RCE via user-provided node code | `add_node()` and `@entrypoint`/`@task` accept arbitrary callables. A malicious node can do anything. This is by design — the user controls their own code. | Executing nodes within the graph runtime; user owns node code safety |
| SSRF via RemoteGraph URL | User provides the `url` parameter to `RemoteGraph`. Pointing it at an internal service is the user's decision. | Documenting that `url` should be a trusted endpoint; user owns URL selection |
| Malicious SDK Encryption handler | Encryption handlers are developer-authored server-side code. A malicious handler has full process access, equivalent to any application code. | Validating handler signature (async, param count); handler behavior is the developer's responsibility |
### Rationale
**Prompt injection and tool execution**: LangGraph's `ToolNode` validates tool names against the registered set but does not inspect or sanitize argument values. This is the correct boundary — the framework cannot know what constitutes a "safe" argument for an arbitrary user-defined tool. The tool's own Pydantic schema and implementation must validate inputs. The framework's responsibility is to not execute unregistered tools and to correctly route registered ones. The injection system (InjectedState/InjectedStore/ToolRuntime) is safe because system-injected values always overwrite LLM-supplied collisions via dict merge order, and injected parameter names are hidden from the LLM's tool schema.
**State integrity**: LangGraph channels enforce type contracts (e.g., `LastValue` accepts one value per step, `BinaryOperatorAggregate` applies a reducer). The framework validates graph structure at compile time (`libs/langgraph/langgraph/pregel/_validate.py:validate_graph`). However, the semantic correctness of state updates is the user's responsibility — the framework cannot know what values are "valid" for a user-defined state schema.
**Checkpoint access control**: The framework provides `BaseCheckpointSaver` as an abstract interface and the `Auth` handler system for authorization (`libs/sdk-py/langgraph_sdk/auth/__init__.py:Auth`). It does not enforce authentication by default because it operates as a library, not a server. The `langgraph-api` server layer (out of scope) is responsible for enforcing auth on API endpoints. Users embedding LangGraph directly must implement their own access controls.
**Encryption handler safety**: The SDK Encryption module (`libs/sdk-py/langgraph_sdk/encryption/`) provides a registration framework for developer-authored encryption handlers. These handlers run server-side with full process access, identical to any application code. A buggy or malicious handler could return crafted data, but this is the same trust model as any developer-written code. The SDK validates handler shape (async, 2-param) but not handler behavior — this is the correct boundary for developer-trust-level code.
---
## Investigated and Dismissed
| ID | Original Threat | Investigation | Evidence | Conclusion |
|----|----------------|---------------|----------|------------|
| D1 | SQL injection via filter keys in PostgreSQL store | Traced filter key handling through `libs/checkpoint-postgres/langgraph/store/postgres/base.py:_get_filter_condition`. All filter operations use parameterized queries with `%s` placeholders. Keys map to `json_extract` path operators with type-safe wrappers. | `libs/checkpoint-postgres/langgraph/store/postgres/base.py:_get_filter_condition` — parameterized `%s` for all value bindings; key names used in `value->%s` path expressions are also parameterized | Disproven: All SQL operations in PostgreSQL store are fully parameterized. No injection vector. |
| D2 | SQL injection via filter keys in SQLite store (post-fix) | Traced current filter handling through `libs/checkpoint-sqlite/langgraph/store/sqlite/base.py` and `libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py:_validate_filter_key`. Regex `^[a-zA-Z0-9_.-]+$` applied to all filter keys before use in `json_extract()` expressions. | `libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py:_validate_filter_key` — regex validation blocks injection characters. Published advisories GHSA-9rwj-6rc7-p77c and GHSA-7p73-8jqx-23r8 confirmed fixed. | Disproven: SQL injection in SQLite store filter keys is remediated by regex validation. |
| D3 | Command injection via CLI subprocess execution | Traced CLI subprocess invocation path. `libs/cli/langgraph_cli/exec.py:subp_exec` uses `asyncio.create_subprocess_exec` with list-based arguments (not `shell=True`). `has_disallowed_build_command_content` blocks shell metacharacters in user-provided Dockerfile lines. | `libs/cli/langgraph_cli/exec.py:subp_exec` — explicit exec-style invocation; `libs/cli/langgraph_cli/config.py:has_disallowed_build_command_content` — regex blocks `\|`, `;`, `$`, `>`, `<`, backtick, `\`, single `&` | Disproven: CLI uses exec-style subprocess and validates build command content. No shell injection vector. |
| D4 | Tool argument injection via InjectedState/InjectedStore dict-splatting | Investigated whether LLM-generated tool call arguments could override system-injected values (InjectedState, InjectedStore, ToolRuntime) via key collision in the dict merge at `libs/prebuilt/langgraph/prebuilt/tool_node.py:ToolNode._inject_tool_args` line 1380. Traced four independent defense layers. | (1) `tool_call_schema` at langchain-core `base.py` filters injected params from LLM schema. (2) `{**llm_args, **injected_args}` merge puts system values last — system wins on collision. (3) Pydantic `model_validate` with `extra="ignore"` drops unknown keys. (4) Output construction at `base.py` only includes declared model fields. | Disproven: Four-layer defense prevents LLM arguments from overriding system-injected values. Merge order guarantees system values win. No adversarial collision path exists. |
---
## External Context
### Published Security Advisories
| GHSA ID | Severity | Summary | CWEs | Relevance |
|---------|----------|---------|------|-----------|
| GHSA-g48c-2wqr-h844 | Medium | Unsafe msgpack deserialization in LangGraph checkpoint loading | — | Directly relates to T1 — patched in 1.0.10, confirms attack path via crafted msgpack payloads |
| GHSA-mhr3-j7m5-c7c9 | Medium | BaseCache Deserialization RCE | CWE-502 | Directly relates to T1 — msgpack deserialization in cache layer |
| GHSA-9rwj-6rc7-p77c | High | SQL injection via metadata filter key in SQLite checkpointer | CWE-89 | Fixed via `_validate_filter_key()` regex — see D2 |
| GHSA-wwqv-p2pp-99h5 | High | RCE in JSON mode of JsonPlusSerializer | CWE-502 | Directly relates to T3 — `lc:2` constructor import |
| GHSA-7p73-8jqx-23r8 | High | SQLite Filter Key SQL Injection in SqliteStore | CWE-89 | Fixed via `_validate_filter_key()` regex — see D2 |
**Pattern**: 3 of 5 published advisories involve CWE-502 (insecure deserialization) in the checkpoint serialization layer. This confirms the checkpoint storage boundary (TB2) as the highest-risk area. The extensive closed advisory history (~15 deserialization bypass attempts) further validates this assessment. No new published advisories since the prior assessment (2026-03-27).
---
## Revision History
| Date | Author | Changes |
|------|--------|---------|
| 2026-03-04 | Generated | Initial threat model |
| 2026-03-04 | Updated | Added C13 (Functional API), C14 (BaseCache), DF11. Updated T1 for BaseCache/serde event hooks. Added GHSA-mhr3-j7m5-c7c9 and GHSA-9rwj-6rc7-p77c. Updated CLI config scope. Added External Context section. |
| 2026-03-27 | Deep refinement | **Mode upgraded to Deep.** Added: Data Classification section (DC1-DC8 with detailed analysis for Critical/High entries). Added: C15 (Serde Event Hooks), C16 (Auth System). Added: Default? column to Components. Added: Classification column to Data Flows. Added: DF12-DF13 (SDK redirect flows). Added: T9 (SDK API key leak via Location redirect), T10 (EncryptedSerializer encryption bypass), T11 (unbounded checkpoint retention). Added: Validation column to Threats with flaw validation for High/Critical. Added: Investigated and Dismissed section (D1-D3: SQL injection and CLI command injection disproven). Added: Input Source Coverage section. Updated external context with GHSA-g48c-2wqr-h844 (new published advisory). Updated all code references to file:SymbolName notation. Expanded trust boundary details. |
| 2026-03-30 | Diagram and Default? corrections | Fixed architecture diagram: merged "User Code" and "User-Registered Tools" into single "User Application Code" boundary; removed @entrypoint/@task as separate diagram elements (both compile to Pregel — authoring style, not separate component). Fixed Default? column: C3 ToolNode → No (explicit opt-in required); C8 CLI → No (separate install). |
| 2026-03-28 | Deep update | **Added:** C17 (SDK Encryption Handlers — beta at-rest encryption framework). DC9 (SDK Encryption context metadata). TB5 (SDK Encryption Handler boundary). DF14 (ToolRuntime injection flow), DF15 (Encryption handler registration flow). D4 (Tool argument injection via InjectedState dict-splatting — disproven with 4-layer defense evidence). **Updated:** C1 description (v1/v2 output, durability modes, interrupt_before/after). C2 description (SAFE_MSGPACK_TYPES now 47 entries including langchain_core messages, Document, GetOp). C3 description (InjectedState/InjectedStore/ToolRuntime injection support, _inject_tool_args entry point). C8 description (WebhookUrlPolicy for SSRF protection). TB1 details (tool injection merge order guarantees). TB2 details (47 safe types, updated allowlist composition). TB4 details (WebhookUrlPolicy). DF4 description (injection merge semantics). T1 details (noted secure_pickle.py proposed but never merged). T4 details (Interrupt deprecated_kwargs ns parameter). Input Source Coverage (LLM output row updated with injection validation points, encryption handler row added). Out-of-Scope Threats (malicious encryption handler pattern added). Commit updated to 0ba22143. External context confirmed no new published advisories. |
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@@ -1,35 +0,0 @@
# Helper to set up Python and uv with caching
name: uv-install
description: Set up Python and uv with caching
inputs:
python-version:
description: Python version, supporting MAJOR.MINOR only
required: true
enable-cache:
description: Enable caching for uv dependencies
required: false
default: "true"
cache-suffix:
description: Custom cache key suffix for cache invalidation
required: false
default: ""
working-directory:
description: Working directory for cache glob scoping
required: false
default: "**"
runs:
using: composite
steps:
- name: Install uv and set the python version
uses: astral-sh/setup-uv@v7
with:
python-version: ${{ inputs.python-version }}
enable-cache: ${{ inputs.enable-cache }}
cache-dependency-glob: |
${{ inputs.working-directory }}/pyproject.toml
${{ inputs.working-directory }}/uv.lock
${{ inputs.working-directory }}/requirements*.txt
cache-suffix: ${{ inputs.cache-suffix }}
+11 -181
View File
@@ -3,186 +3,16 @@ updates:
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
interval: "weekly"
- package-ecosystem: "uv"
directory: "/libs/checkpoint"
- package-ecosystem: "pip"
directories:
- "libs/checkpoint"
- "libs/checkpoint-postgres"
- "libs/checkpoint-sqlite"
- "libs/cli"
- "libs/langgraph"
- "libs/prebuilt"
- "libs/sdk-py"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-conformance"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-postgres"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-sqlite"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/cli"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/langgraph"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/prebuilt"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/sdk-py"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "npm"
directory: "/libs/cli/js-examples"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "npm"
directory: "/libs/cli/js-monorepo-example"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
interval: "weekly"
-5
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Before

Width:  |  Height:  |  Size: 4.7 KiB

+3 -8
View File
@@ -1,15 +1,10 @@
import ast
import os
from itertools import filterfalse
from typing import Dict, List, Tuple
from typing import List, Tuple
ROOT_PATH = os.path.abspath(os.path.join(__file__, "..", "..", ".."))
CLIENT_PATH = os.path.join(ROOT_PATH, "libs", "sdk-py", "langgraph_sdk", "client.py")
ASYNC_TO_SYNC_METHOD_MAP: Dict[str, str] = {
"aclose": "close",
"__aenter__": "__enter__",
"__aexit__": "__exit__",
}
def get_class_methods(node: ast.ClassDef) -> List[str]:
@@ -27,7 +22,7 @@ def find_classes(tree: ast.AST) -> List[Tuple[str, List[str]]]:
def compare_sync_async_methods(sync_methods: List[str], async_methods: List[str]) -> List[str]:
sync_set = set(sync_methods)
async_set = {ASYNC_TO_SYNC_METHOD_MAP.get(async_method, async_method) for async_method in async_methods}
async_set = set(async_methods)
missing_in_sync = list(async_set - sync_set)
missing_in_async = list(sync_set - async_set)
return missing_in_sync + missing_in_async
@@ -38,7 +33,7 @@ def main():
tree = ast.parse(file.read())
classes = find_classes(tree)
def is_sync(class_spec: Tuple[str, List[str]]) -> bool:
return class_spec[0].startswith("Sync")
+84 -146
View File
@@ -1,164 +1,108 @@
import logging
import asyncio
import json
import os
import pathlib
import sys
import time
from urllib import error, request
import langgraph_cli
import langgraph_cli.config
import langgraph_cli.docker
from langgraph_cli.cli import prepare_args_and_stdin
from langgraph_cli.constants import DEFAULT_PORT
import langgraph_cli.config
from langgraph_cli.exec import Runner, subp_exec
from langgraph_cli.progress import Progress
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
from langgraph_cli.constants import DEFAULT_PORT
def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
"""Spin up API with Postgres/Redis via docker compose and wait until ready."""
logger.info("Starting test...")
def test(
config: pathlib.Path,
port: int,
tag: str,
verbose: bool,
):
with Runner() as runner, Progress(message="Pulling...") as set:
# Detect docker/compose capabilities
# check docker available
capabilities = langgraph_cli.docker.check_capabilities(runner)
# Validate config and prepare compose stdin/args using built image
# open config
config_json = langgraph_cli.config.validate_config_file(config)
args, stdin = prepare_args_and_stdin(
capabilities=capabilities,
config_path=config,
config=config_json,
docker_compose=None,
port=port,
watch=False,
debugger_port=None,
debugger_base_url=f"http://127.0.0.1:{port}",
postgres_uri=None,
api_version=None,
image=tag,
base_image=None,
)
# Compose up with wait (implies detach), similar to `langgraph up --wait`
args_up = [*args, "up", "--remove-orphans", "--wait"]
compose_cmd = ["docker", "compose"]
if capabilities.compose_type == "standalone":
compose_cmd = ["docker-compose"]
set("Starting...")
try:
runner.run(
subp_exec(
*compose_cmd,
*args_up,
input=stdin,
verbose=verbose,
)
set("Running...")
args = [
"run",
"--rm",
"-p",
f"{port}:8000",
]
if isinstance(config_json["env"], str):
args.extend(
[
"--env-file",
str(config.parent / config_json["env"]),
]
)
except Exception as e: # noqa: BLE001
# On failure, show diagnostics then ensure clean teardown
sys.stderr.write(f"docker compose up failed: {e}\n")
try:
sys.stderr.write("\n== docker compose ps ==\n")
runner.run(
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
)
except Exception:
pass
try:
sys.stderr.write("\n== docker compose logs (api) ==\n")
runner.run(
subp_exec(
*compose_cmd,
*args,
"logs",
"langgraph-api",
input=stdin,
verbose=True,
)
)
except Exception:
pass
finally:
try:
runner.run(
subp_exec(
*compose_cmd,
*args,
"down",
"-v",
"--remove-orphans",
input=stdin,
verbose=False,
)
)
finally:
raise
set("")
base_url = f"http://localhost:{port}"
ok_url = f"{base_url}/ok"
logger.info(f"Waiting for {ok_url} to respond with 200...")
deadline = time.time() + 30
last_err: Exception | None = None
while time.time() < deadline:
try:
with request.urlopen(ok_url, timeout=2) as resp:
if resp.status == 200:
sys.stdout.write(
f"""Ready!\n- API: {base_url}\n- /ok: 200 OK\n"""
)
sys.stdout.flush()
break
else:
last_err = RuntimeError(f"Unexpected status: {resp.status}")
logger.error(f"Unexpected status: {resp.status}")
except error.URLError as e:
logger.error(f"URLError: {e}")
last_err = e
except Exception as e: # noqa: BLE001
logger.error(f"Exception: {e}")
last_err = e
time.sleep(0.5)
else:
logger.error("Timeout waiting for /ok to return 200")
# Bring stack down before raising
args_down = [*args, "down", "-v", "--remove-orphans"]
try:
runner.run(
subp_exec(
*compose_cmd,
*args_down,
input=stdin,
verbose=verbose,
)
)
finally:
raise SystemExit(
f"/ok did not return 202 within timeout. Last error: {last_err}"
for k, v in config_json["env"].items():
args.extend(
[
"-e",
f"{k}={v}",
]
)
if capabilities.healthcheck_start_interval:
args.extend(
[
"--health-interval",
"5s",
"--health-retries",
"1",
"--health-start-period",
"10s",
"--health-start-interval",
"1s",
]
)
else:
args.extend(
[
"--health-interval",
"5s",
"--health-retries",
"2",
]
)
_task = None
def on_stdout(line: str):
nonlocal _task
if "GET /ok" in line or "Uvicorn running on" in line:
set("")
sys.stdout.write(
f"""Ready!
- API: http://localhost:{port}
"""
)
sys.stdout.flush()
_task.cancel()
return True
return False
async def subp_exec_task(*args, **kwargs):
nonlocal _task
_task = asyncio.create_task(subp_exec(*args, **kwargs))
await _task
# Clean up: bring compose stack down to free ports for next test
logger.info("Test succeeded. Bringing down compose stack...")
try:
args_down = [*args, "down", "-v", "--remove-orphans"]
runner.run(
subp_exec(
*compose_cmd,
*args_down,
input=stdin,
subp_exec_task(
"docker",
*args,
tag,
verbose=verbose,
on_stdout=on_stdout,
)
)
logger.info("Compose stack down. Finishing...")
except Exception:
logger.exception("Failed to bring down compose stack")
except asyncio.CancelledError:
pass
logger.info("Test finished")
if __name__ == "__main__":
import argparse
@@ -166,12 +110,6 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-t", "--tag", type=str)
parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT)
parser.add_argument("-p", "--port", default=DEFAULT_PORT)
args = parser.parse_args()
try:
test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
except BaseException:
logger.exception("Test failed")
raise
logger.info("Test execution finished")
test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
+38 -132
View File
@@ -2,9 +2,6 @@ name: CLI integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
permissions:
contents: read
@@ -16,153 +13,62 @@ jobs:
matrix:
python-version:
- "3.10"
- "3.14"
example:
- name: A
workdir: libs/cli/examples
tag: langgraph-test-a
- name: B
workdir: libs/cli/examples/graphs
tag: langgraph-test-b
- name: C
workdir: libs/cli/examples/graphs_reqs_a
tag: langgraph-test-c
- name: D
workdir: libs/cli/examples/graphs_reqs_b
tag: langgraph-test-d
- "3.11"
name: "CLI integration test"
env:
HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
defaults:
run:
working-directory: libs/cli
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "libs/cli/**"
- name: Set up Python ${{ matrix.python-version }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
uses: ./.github/actions/uv_setup
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: "false"
working-directory: libs/cli
enable-cache: true
cache-suffix: "cli-integration-test"
ignore-nothing-to-cache: true
- name: Setup env
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: cat .env.example > .env
- name: Install cli globally
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
if: steps.changed-files.outputs.all
run: pip install -e .
- name: Build service ${{ matrix.example.name }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
working-directory: ${{ matrix.example.workdir }}
- name: Build and test service A
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: |
langgraph build -t ${{ matrix.example.tag }}
- name: Test service ${{ matrix.example.name }}
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: ${{ matrix.example.workdir }}
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
# The build-arg isn't used; just testing that we accept other args
langgraph build -t langgraph-test-a --base-image "langchain/langgraph-trial"
cp .env.example .envg
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
- name: Build and test service B
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs
run: |
# Prepare environment file from local or parent example directory
if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
# Run the integration test using the built tag
REPO_ROOT=$(git rev-parse --show-toplevel)
timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
langgraph build -t langgraph-test-b --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
- name: Build and test service C
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_a
run: |
langgraph build -t langgraph-test-c --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
- name: Build and test service D
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_b
run: |
langgraph build -t langgraph-test-d --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
- name: Build JS service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: steps.changed-files.outputs.all
working-directory: libs/cli/js-examples
run: |
langgraph build -t langgraph-test-e
- name: Build JS monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/js-monorepo-example
run: |
langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
- name: Build Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/python-monorepo-example
run: |
langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
- name: Test Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/python-monorepo-example
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp apps/agent/.env.example apps/agent/.env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
- name: Build prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
run: |
langgraph build -t langgraph-test-h
- name: Test prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp ../.env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
echo "Finished starting up langgraph-test-h"
LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
if [ "$LANGGRAPH_VERSION" != "1.1.5" ]; then
echo "LANGGRAPH_VERSION != 1.1.5; $LANGGRAPH_VERSION"
exit 1
fi
LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
if [ "$LANGCHAIN_OPENAI_VERSION" != "1.1.14" ]; then
echo "LANGCHAIN_OPENAI_VERSION != 1.1.14; $LANGCHAIN_OPENAI_VERSION"
exit 1
fi
LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")
if [ "$LANGCHAIN_ANTHROPIC_VERSION" != "1.0.0a5" ]; then
echo "LANGCHAIN_ANTHROPIC_VERSION != 1.0.0a5; $LANGCHAIN_ANTHROPIC_VERSION"
exit 1
fi
- name: Build and test prerelease reqs fail service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs_fail
run: |
langgraph build -t langgraph-test-i || [ $? -eq 1 ]
- name: Build uv simple service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/uv-examples/simple
run: |
langgraph build -t langgraph-test-uv-simple
- name: Test uv simple service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/uv-examples/simple
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp .env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-uv-simple
- name: Build uv monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/uv-examples/monorepo/apps/agent
run: |
langgraph build -t langgraph-test-uv-monorepo
- name: Test uv monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/uv-examples/monorepo/apps/agent
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp .env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-uv-monorepo
+15 -16
View File
@@ -31,29 +31,28 @@ jobs:
- "3.12"
name: "lint #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "${{ inputs.working-directory }}/**"
- name: Set up Python ${{ matrix.python-version }}
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: ./.github/actions/uv_setup
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: lint-${{ inputs.working-directory }}
working-directory: ${{ inputs.working-directory }}
- name: Install dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group lint
run: uv sync --frozen --group dev
- name: Get .mypy_cache to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v4
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
@@ -62,7 +61,7 @@ jobs:
key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing package code with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_package > /dev/null 2>&1; then
@@ -73,13 +72,13 @@ jobs:
fi
- name: Install test dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --group lint
run: uv sync --group dev
- name: Get .mypy_cache_test to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v4
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
@@ -88,7 +87,7 @@ jobs:
key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing tests with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_tests > /dev/null 2>&1; then
+6 -6
View File
@@ -17,23 +17,23 @@ jobs:
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: test-${{ inputs.working-directory }}
working-directory: ${{ inputs.working-directory }}
- name: Login to Docker Hub
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -42,7 +42,7 @@ jobs:
- name: Install dependencies
shell: bash
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group test --no-dev
run: uv sync --frozen --group dev
- name: Run tests
shell: bash
+6 -13
View File
@@ -12,26 +12,26 @@ jobs:
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
defaults:
run:
working-directory: libs/langgraph
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: "test-langgraph"
working-directory: libs/langgraph
- name: Login to Docker Hub
uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -39,19 +39,12 @@ jobs:
- name: Install dependencies
shell: bash
run: uv sync --frozen --group test --no-dev
run: uv sync --frozen --group dev
- name: Run tests
shell: bash
run: make test_parallel
- name: Run strict msgpack pregel tests
if: ${{ matrix.python-version == '3.13' }}
shell: bash
env:
LANGGRAPH_STRICT_MSGPACK: "true"
run: make test TEST="tests/test_pregel.py tests/test_pregel_async.py"
- name: Ensure the tests did not create any additional files
shell: bash
run: |
+9 -8
View File
@@ -16,6 +16,7 @@ permissions:
jobs:
build:
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
outputs:
@@ -23,14 +24,14 @@ jobs:
version: ${{ steps.check-version.outputs.version }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ env.PYTHON_VERSION }}
uses: ./.github/actions/uv_setup
- name: Set up Python $${ env.PYTHON_VERSION }}
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
working-directory: ${{ inputs.working-directory }}
# We want to keep this build stage *separate* from the release stage,
# so that there's no sharing of permissions between them.
@@ -48,7 +49,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
@@ -74,15 +75,15 @@ jobs:
id-token: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
- name: Publish to test PyPI
uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # release/v1
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ${{ inputs.working-directory }}/dist/
verbose: true
+5 -5
View File
@@ -17,20 +17,20 @@ jobs:
run:
working-directory: libs/langgraph
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
working-directory: libs/langgraph
- name: Install dependencies
run: uv sync --group test
run: uv sync --group dev
- name: Run benchmarks
run: OUTPUT=out/benchmark-baseline.json make -s benchmark
- name: Save outputs
uses: actions/cache/save@v5
uses: actions/cache/save@v4
with:
key: ${{ runner.os }}-benchmark-baseline-${{ env.SHA }}
path: |
+10 -14
View File
@@ -15,22 +15,22 @@ jobs:
run:
working-directory: libs/langgraph
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- id: files
name: Get changed files
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
format: json
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
working-directory: libs/langgraph
- name: Install dependencies
run: uv sync --group test
run: uv sync --group dev
- name: Download baseline
uses: actions/cache/restore@v5
uses: actions/cache/restore@v4
with:
key: ${{ runner.os }}-benchmark-baseline
restore-keys: |
@@ -57,19 +57,15 @@ jobs:
echo EOF
} >> "$GITHUB_OUTPUT"
- name: Annotation
uses: actions/github-script@v9
env:
CHANGED_FILES: ${{ steps.files.outputs.added_modified_renamed }}
BENCHMARK_OUTPUT: ${{ steps.benchmark.outputs.OUTPUT }}
COMPARE_OUTPUT: ${{ steps.compare.outputs.OUTPUT }}
uses: actions/github-script@v7
with:
script: |
const file = JSON.parse(process.env.CHANGED_FILES || "[]")[0]
core.notice(process.env.BENCHMARK_OUTPUT || "", {
const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]
core.notice(`${{ steps.benchmark.outputs.OUTPUT }}`, {
title: 'Benchmark results',
file,
})
core.notice(process.env.COMPARE_OUTPUT || "", {
core.notice(`${{ steps.compare.outputs.OUTPUT }}`, {
title: 'Comparison against main',
file,
})
+13 -20
View File
@@ -2,12 +2,9 @@
name: CI
on:
workflow_dispatch:
push:
branches:
- main
branches: [main, v1]
pull_request:
permissions:
contents: read
@@ -26,12 +23,11 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
python: ${{ steps.filter.outputs.python || 'true' }}
deps: ${{ steps.filter.outputs.deps || 'true' }}
python: ${{ steps.filter.outputs.python }}
deps: ${{ steps.filter.outputs.deps }}
steps:
- uses: actions/checkout@v6
- uses: dorny/paths-filter@fbd0ab8f3e69293af611ebaee6363fc25e6d187d # v4
if: github.event_name != 'workflow_dispatch'
- uses: actions/checkout@v4
- uses: dorny/paths-filter@v3
id: filter
with:
filters: |
@@ -42,7 +38,6 @@ jobs:
- 'libs/checkpoint/**'
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/checkpoint-conformance/**'
- 'libs/prebuilt/**'
deps:
- '**/pyproject.toml'
@@ -61,7 +56,7 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
@@ -81,9 +76,7 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/sdk-py",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
uses: ./.github/workflows/_test.yml
@@ -105,9 +98,9 @@ jobs:
name: "Check SDK methods matching"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v6
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Run check_sdk_methods script
@@ -121,15 +114,15 @@ jobs:
strategy:
matrix:
python-version:
- "3.13"
- "3.11"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: "3.13"
python-version: "3.11"
enable-cache: true
cache-suffix: "schema-check-cli"
working-directory: libs/cli
- name: Install CLI dependencies
run: |
cd libs/cli
@@ -0,0 +1,11 @@
LangChain
LangGraph
LangSmith
thead
stdio
nd
jupyter
lets
lite
uis
deque
+49
View File
@@ -0,0 +1,49 @@
---
name: CI / cd . / make spell_check
on:
push:
branches: [main]
pull_request:
branches: [main]
permissions:
contents: read
defaults:
run:
working-directory: docs
jobs:
codespell:
name: (Check for spelling errors)
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Install Dependencies
run: |
pip install toml codespell==2.3.0 jupytext
- name: Extract Ignore Words List
run: |
# Use a Python script to extract the ignore words list from pyproject.toml
python ../.github/workflows/extract_ignored_words_list.py
id: extract_ignore_words
- name: Codespell
uses: codespell-project/actions-codespell@v2.1
with:
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
# We do this to avoid spellchecking cell outputs
- name: Codespell Notebooks
run: make codespell
- name: Codespell LangGraph Library
run: |
# Change to root directory to check the main LangGraph library
cd ..
codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map,*.pyc,__pycache__/*" --ignore-words-list="${{ steps.extract_ignore_words.outputs.ignore_words_list }}" libs/langgraph/langgraph/
-49
View File
@@ -1,49 +0,0 @@
name: Deploy Redirects to GitHub Pages
on:
push:
branches:
- main
paths:
- 'docs/**'
- '.github/workflows/deploy-redirects.yml'
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: "pages"
cancel-in-progress: false
jobs:
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Setup Python
uses: actions/setup-python@v6
with:
python-version: '3.11'
- name: Generate redirect files
run: python docs/generate_redirects.py
- name: Setup Pages
uses: actions/configure-pages@v6
- name: Upload artifact
uses: actions/upload-pages-artifact@v5
with:
path: 'docs/_site'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v5
+150
View File
@@ -0,0 +1,150 @@
name: Deploy Docs
on:
push:
branches:
- main
pull_request:
branches:
- main
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: "pages"
cancel-in-progress: false
defaults:
run:
working-directory: docs
jobs:
get-changed-files:
runs-on: ubuntu-latest
outputs:
changed-files: ${{ steps.changed-files.outputs.added_modified }}
steps:
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "docs/docs/**"
deploy:
runs-on: ubuntu-latest
timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
env:
GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: astral-sh/setup-uv@v6
with:
python-version: "3.12"
enable-cache: true
cache-suffix: "docs"
- name: Install dependencies
run: |
yarn
uv sync --all-groups
# we run this installation only for internal PRs
# as GITHUB_TOKEN is not available for PRs from outside contributors
if [ -n "${GITHUB_TOKEN}" ]; then
uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
fi
- name: Run unit tests
# Run unit tests on the docs build pipeline
run: make tests
- name: Lint Docs
# This step lints the docs using the existing linting set up.
# It should be very fast and should not require any external services.
run: make lint-docs
- name: Build llms-text
run: make llms-text
- name: Build site
run: |
# If this is main branch, then we want to download stats. we do this
# with the env variable DOWNLOAD_STATS=true
if [ "${{ github.ref }}" == "refs/heads/main" ]; then
DOWNLOAD_STATS=true make build-docs
else
make build-docs
fi
env:
MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
- name: Check links in notebooks
env:
LANGCHAIN_API_KEY: test
if: github.event_name == 'schedule'
run: |
if [ "${{ github.event_name }}" == "schedule" ]; then
echo "Running link check on all HTML files matching notebooks in docs directory..."
uv run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "https://academy\.langchain\.com/.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://twitter.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "http://localhost:2024.*" \
--check-links-ignore "http://127.0.0.1:.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links-ignore "https://python\.langchain\.com/.*" \
--check-links-ignore "https://openai\.com/.*" \
--check-links-ignore "https://www\.uber\.com/.*" \
--check-links-ignore "https://pepy\.tech/.*" \
--check-links-ignore "docs/docs/static/wordmark_*" \
--check-links $(find site -name "index.html" | grep -v 'storm/index.html')
else
echo "Fetching changes from origin/main..."
git fetch origin main
echo "Checking for changed notebook files..."
CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
echo "Changed files: ${CHANGED_FILES}"
if [ -n "${CHANGED_FILES}" ]; then
echo "Running link check on HTML files matching changed notebook files..."
uv run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "https://academy\.langchain\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "http://localhost:2024.*" \
--check-links-ignore "http://127.0.0.1:.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://twitter.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links-ignore "docs/docs/static/wordmark_*" \
--check-links ${CHANGED_FILES} \
|| ([ $? = 5 ] && exit 0 || exit $?)
else
echo "No notebook files changed."
fi
fi
- name: Configure GitHub Pages
if: github.ref == 'refs/heads/main'
uses: actions/configure-pages@v5
- name: Upload Pages Artifact
# if: github.ref == 'refs/heads/main'
uses: actions/upload-pages-artifact@v3
with:
path: ./docs/site/
- name: Deploy to GitHub Pages
if: github.ref == 'refs/heads/main'
id: deployment
uses: actions/deploy-pages@v4
@@ -0,0 +1,10 @@
import toml
pyproject_toml = toml.load("pyproject.toml")
# Extract the ignore words list (adjust the key as per your TOML structure)
ignore_words_list = (
pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
)
print(f"::set-output name=ignore_words_list::{ignore_words_list}") # noqa: T201
+49
View File
@@ -0,0 +1,49 @@
name: Check Docs & Links
on:
pull_request:
branches:
- main
push:
branches:
- main
schedule:
- cron: "0 5 * * *"
workflow_dispatch:
permissions:
contents: read
jobs:
markdown-link-check:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Check links in Markdown files
uses: gaurav-nelson/github-action-markdown-link-check@v1
with:
folder-path: "docs/"
check-modified-files-only: ${{ github.event_name != 'schedule' }}
file-path: "./README.md"
config-file: "./.markdown-link-check.config.json"
check-readmes-synced:
# This checks that the repo README.md is identical to the libs/langgraph/README.md
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 1
- name: Check README.md is in sync
run: |
if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
echo "README.md is out of sync with libs/langgraph/README.md"
diff -C 3 README.md libs/langgraph/README.md
exit 1
fi
+1 -2
View File
@@ -12,7 +12,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Validate PR Title
uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6
uses: amannn/action-semantic-pull-request@v5
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
@@ -40,7 +40,6 @@ jobs:
sdk-py
docs
ci
deps
requireScope: false
ignoreLabels: |
ignore-lint-pr-title
+21 -31
View File
@@ -16,6 +16,7 @@ env:
jobs:
build:
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
outputs:
@@ -25,15 +26,14 @@ jobs:
tag: ${{ steps.check-version.outputs.tag }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
enable-cache: false
working-directory: ${{ inputs.working-directory }}
# We want to keep this build stage *separate* from the release stage,
# so that there's no sharing of permissions between them.
@@ -51,7 +51,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -62,13 +62,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
run: |
PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
if grep -q 'dynamic.*=.*\[.*"version".*\]' pyproject.toml; then
# handle dynamic versioning
DIR_NAME=$(echo "$PKG_NAME" | tr '-' '_')
VERSION=$(grep -m 1 '^__version__' "${DIR_NAME}/__init__.py" | cut -d '"' -f 2)
else
VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
fi
VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
if [ -z $SHORT_PKG_NAME ]; then
TAG="$VERSION"
@@ -87,7 +81,7 @@ jobs:
outputs:
release-body: ${{ steps.generate-release-body.outputs.release-body }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
repository: langchain-ai/langgraph
path: langgraph
@@ -158,7 +152,7 @@ jobs:
- test-pypi-publish
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
# We explicitly *don't* set up caching here. This ensures our tests are
# maximally sensitive to catching breakage.
@@ -174,11 +168,10 @@ jobs:
# used in the real world.
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: false
working-directory: ${{ inputs.working-directory }}
enable-cache: true
- name: Import published package
shell: bash
@@ -223,7 +216,7 @@ jobs:
uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
- name: Import test dependencies
run: uv sync --group test
run: uv sync --group dev
working-directory: ${{ inputs.working-directory }}
# Overwrite the local version of the package with the test PyPI version.
@@ -262,23 +255,22 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
enable-cache: false
working-directory: ${{ inputs.working-directory }}
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
- name: Publish package distributions to PyPI
uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # release/v1
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ${{ inputs.working-directory }}/dist/
verbose: true
@@ -304,28 +296,26 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
enable-cache: false
working-directory: ${{ inputs.working-directory }}
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
- name: Create Tag
uses: ncipollo/release-action@339a81892b84b4eeb0f6e744e4574d79d0d9b8dd # v1
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
generateReleaseNotes: false
tag: ${{needs.build.outputs.tag}}
name: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
body: ${{ needs.release-notes.outputs.release-body }}
commit: ${{ github.sha }}
-195
View File
@@ -1,195 +0,0 @@
# Reopen PRs that were auto-closed by require_issue_link.yml when the
# contributor was not assigned to the linked issue. When a maintainer
# assigns the contributor to the issue, this workflow finds matching
# closed PRs, verifies the issue link, and reopens them.
#
# Uses the default GITHUB_TOKEN (not a PAT or app token) so that the
# reopen and label-removal events do NOT re-trigger other workflows.
# GitHub suppresses events created by the default GITHUB_TOKEN within
# workflow runs to prevent infinite loops.
name: Reopen PR on Issue Assignment
on:
issues:
types: [assigned]
permissions:
contents: read
jobs:
reopen-linked-prs:
runs-on: ubuntu-latest
permissions:
actions: write
pull-requests: write
steps:
- name: Find and reopen matching PRs
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const issueNumber = context.payload.issue.number;
const assignee = context.payload.assignee.login;
console.log(
`Issue #${issueNumber} assigned to ${assignee} — searching for closed PRs to reopen`,
);
const q = [
`is:pr`,
`is:closed`,
`author:${assignee}`,
`label:missing-issue-link`,
`repo:${owner}/${repo}`,
].join(' ');
let data;
try {
({ data } = await github.rest.search.issuesAndPullRequests({
q,
per_page: 30,
}));
} catch (e) {
throw new Error(
`Failed to search for closed PRs to reopen after assigning ${assignee} ` +
`to #${issueNumber} (HTTP ${e.status ?? 'unknown'}): ${e.message}`,
);
}
if (data.total_count === 0) {
console.log('No matching closed PRs found');
return;
}
console.log(`Found ${data.total_count} candidate PR(s)`);
// Must stay in sync with the identical pattern in require_issue_link.yml
const pattern = /(?:close[sd]?|fix(?:e[sd])?|resolve[sd]?)\s*#(\d+)/gi;
for (const item of data.items) {
const prNumber = item.number;
const body = item.body || '';
const matches = [...body.matchAll(pattern)];
const referencedIssues = matches.map(m => parseInt(m[1], 10));
if (!referencedIssues.includes(issueNumber)) {
console.log(`PR #${prNumber} does not reference #${issueNumber} — skipping`);
continue;
}
// Skip if already bypassed
const labels = item.labels.map(l => l.name);
if (labels.includes('bypass-issue-check')) {
console.log(`PR #${prNumber} already has bypass-issue-check — skipping`);
continue;
}
// Reopen first, remove label second — a closed PR that still has
// missing-issue-link is recoverable; a closed PR with the label
// stripped is invisible to both workflows.
try {
await github.rest.pulls.update({
owner,
repo,
pull_number: prNumber,
state: 'open',
});
console.log(`Reopened PR #${prNumber}`);
} catch (e) {
if (e.status === 422) {
// Head branch deleted — PR is unrecoverable. Notify the
// contributor so they know to open a new PR.
core.warning(`Cannot reopen PR #${prNumber}: head branch was likely deleted`);
try {
await github.rest.issues.createComment({
owner,
repo,
issue_number: prNumber,
body:
`You have been assigned to #${issueNumber}, but this PR could not be ` +
`reopened because the head branch has been deleted. Please open a new ` +
`PR referencing the issue.`,
});
} catch (commentErr) {
core.warning(
`Also failed to post comment on PR #${prNumber}: ${commentErr.message}`,
);
}
continue;
}
// Transient errors (rate limit, 5xx) should fail the job so
// the label is NOT removed and the run can be retried.
throw e;
}
// Remove missing-issue-link label only after successful reopen
try {
await github.rest.issues.removeLabel({
owner,
repo,
issue_number: prNumber,
name: 'missing-issue-link',
});
console.log(`Removed missing-issue-link from PR #${prNumber}`);
} catch (e) {
if (e.status !== 404) throw e;
}
// Minimize stale enforcement comment (best-effort;
// sync w/ require_issue_link.yml minimize blocks)
try {
const marker = '<!-- require-issue-link -->';
const comments = await github.paginate(
github.rest.issues.listComments,
{ owner, repo, issue_number: prNumber, per_page: 100 },
);
const stale = comments.find(c => c.body && c.body.includes(marker));
if (stale) {
await github.graphql(`
mutation($id: ID!) {
minimizeComment(input: {subjectId: $id, classifier: OUTDATED}) {
minimizedComment { isMinimized }
}
}
`, { id: stale.node_id });
console.log(`Minimized stale enforcement comment ${stale.id} as outdated`);
}
} catch (e) {
core.warning(`Could not minimize stale comment on PR #${prNumber}: ${e.message}`);
}
// Re-run the failed require_issue_link check so it picks up the
// new assignment. The re-run uses the original event payload but
// fetches live issue data, so the assignment check will pass.
//
// Limitation: we look up runs by the PR's current head SHA. If the
// contributor pushed new commits while the PR was closed, head.sha
// won't match the SHA of the original failed run and the query will
// return 0 results. This is acceptable because any push after reopen
// triggers a fresh require_issue_link run against the new SHA.
try {
const { data: pr } = await github.rest.pulls.get({
owner, repo, pull_number: prNumber,
});
const { data: runs } = await github.rest.actions.listWorkflowRuns({
owner, repo,
workflow_id: 'require_issue_link.yml',
head_sha: pr.head.sha,
status: 'failure',
per_page: 1,
});
if (runs.workflow_runs.length > 0) {
await github.rest.actions.reRunWorkflowFailedJobs({
owner, repo,
run_id: runs.workflow_runs[0].id,
});
console.log(`Re-ran failed require_issue_link run ${runs.workflow_runs[0].id} for PR #${prNumber}`);
} else {
console.log(`No failed require_issue_link runs found for PR #${prNumber} — skipping re-run`);
}
} catch (e) {
core.warning(`Could not re-run require_issue_link check for PR #${prNumber} (HTTP ${e.status ?? 'unknown'}): ${e.message}`);
}
}
-467
View File
@@ -1,467 +0,0 @@
# Require external PRs to reference an approved issue (e.g. Fixes #NNN) and
# the PR author to be assigned to that issue. On failure the PR is
# labeled "missing-issue-link", commented on, and closed.
#
# Maintainer override: an org member can reopen the PR or remove
# "missing-issue-link" — both add "bypass-issue-check" and reopen.
#
# Dependency: tag-external-prs.yml must apply the "external" label
# first. This workflow does NOT trigger on "opened" (new PRs have no labels
# yet, so the gate would always skip).
name: Require Issue Link
on:
pull_request_target:
# NEVER CHECK OUT UNTRUSTED CODE FROM A PR's HEAD IN A pull_request_target JOB.
# Doing so would allow attackers to execute arbitrary code in the context of your repository.
types: [edited, reopened, labeled, unlabeled]
# ──────────────────────────────────────────────────────────────────────────────
# Enforcement gate: set to 'true' to activate the issue link requirement.
# When 'false', the workflow still runs the check logic (useful for dry-run
# visibility) but will NOT label, comment, close, or fail PRs.
# ──────────────────────────────────────────────────────────────────────────────
env:
ENFORCE_ISSUE_LINK: "true"
permissions:
contents: read
jobs:
check-issue-link:
# Run when the "external" label is added, on edit/reopen if already labeled,
# or when "missing-issue-link" is removed (triggers maintainer override check).
# Skip entirely when the PR already carries "trusted-contributor" or
# "bypass-issue-check".
if: >-
!contains(github.event.pull_request.labels.*.name, 'trusted-contributor') &&
!contains(github.event.pull_request.labels.*.name, 'bypass-issue-check') &&
(
(github.event.action == 'labeled' && github.event.label.name == 'external') ||
(github.event.action == 'unlabeled' && github.event.label.name == 'missing-issue-link' && contains(github.event.pull_request.labels.*.name, 'external')) ||
(github.event.action != 'labeled' && github.event.action != 'unlabeled' && contains(github.event.pull_request.labels.*.name, 'external'))
)
runs-on: ubuntu-latest
permissions:
actions: write
pull-requests: write
steps:
- name: Check for issue link and assignee
id: check-link
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
const action = context.payload.action;
// ── Helper: ensure a label exists, then add it to the PR ────────
async function ensureAndAddLabel(labelName, color) {
try {
await github.rest.issues.getLabel({ owner, repo, name: labelName });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({ owner, repo, name: labelName, color });
} catch (createErr) {
// 422 = label was created by a concurrent run between our
// GET and POST — safe to ignore.
if (createErr.status !== 422) throw createErr;
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: prNumber, labels: [labelName],
});
}
// ── Helper: check if the user who triggered this event (reopened
// the PR / removed the label) has write+ access on the repo ───
// Uses the repo collaborator permission endpoint instead of the
// org membership endpoint. The org endpoint requires the caller
// to be an org member, which GITHUB_TOKEN (an app installation
// token) never is — so it always returns 403.
async function senderIsOrgMember() {
const sender = context.payload.sender?.login;
if (!sender) {
throw new Error('Event has no sender — cannot check permissions');
}
try {
const { data } = await github.rest.repos.getCollaboratorPermissionLevel({
owner, repo, username: sender,
});
const perm = data.permission;
if (['admin', 'maintain', 'write'].includes(perm)) {
console.log(`${sender} has ${perm} permission — treating as maintainer`);
return { isMember: true, login: sender };
}
console.log(`${sender} has ${perm} permission — not a maintainer`);
return { isMember: false, login: sender };
} catch (e) {
if (e.status === 404) {
console.log(`Cannot check permissions for ${sender} — treating as non-maintainer`);
return { isMember: false, login: sender };
}
const status = e.status ?? 'unknown';
throw new Error(
`Permission check failed for ${sender} (HTTP ${status}): ${e.message}`,
);
}
}
// ── Helper: apply maintainer bypass (shared by both override paths) ──
async function applyMaintainerBypass(reason) {
console.log(reason);
// Remove missing-issue-link if present
try {
await github.rest.issues.removeLabel({
owner, repo, issue_number: prNumber, name: 'missing-issue-link',
});
} catch (e) {
if (e.status !== 404) throw e;
}
// Reopen before adding bypass label — a failed reopen is more
// actionable than a closed PR with a bypass label stuck on it.
if (context.payload.pull_request.state === 'closed') {
try {
await github.rest.pulls.update({
owner, repo, pull_number: prNumber, state: 'open',
});
console.log(`Reopened PR #${prNumber}`);
} catch (e) {
// 422 if head branch deleted; 403 if permissions insufficient.
// Bypass labels still apply — maintainer can reopen manually.
core.warning(
`Could not reopen PR #${prNumber} (HTTP ${e.status ?? 'unknown'}): ${e.message}. ` +
`Bypass labels were applied — a maintainer may need to reopen manually.`,
);
}
}
// Add bypass-issue-check so future triggers skip enforcement
await ensureAndAddLabel('bypass-issue-check', '0e8a16');
// Minimize stale enforcement comment (best-effort; must not
// abort bypass — sync w/ reopen_on_assignment.yml & step below)
try {
const marker = '<!-- require-issue-link -->';
const comments = await github.paginate(
github.rest.issues.listComments,
{ owner, repo, issue_number: prNumber, per_page: 100 },
);
const stale = comments.find(c => c.body && c.body.includes(marker));
if (stale) {
await github.graphql(`
mutation($id: ID!) {
minimizeComment(input: {subjectId: $id, classifier: OUTDATED}) {
minimizedComment { isMinimized }
}
}
`, { id: stale.node_id });
console.log(`Minimized stale enforcement comment ${stale.id} as outdated`);
}
} catch (e) {
core.warning(`Could not minimize stale comment on PR #${prNumber}: ${e.message}`);
}
core.setOutput('has-link', 'true');
core.setOutput('is-assigned', 'true');
}
// ── Maintainer override: removed "missing-issue-link" label ─────
if (action === 'unlabeled') {
const { isMember, login } = await senderIsOrgMember();
if (isMember) {
await applyMaintainerBypass(
`Maintainer ${login} removed missing-issue-link from PR #${prNumber} — bypassing enforcement`,
);
return;
}
// Non-member removed the label — re-add it defensively and
// set failure outputs so downstream steps (comment, close) fire.
// NOTE: addLabels fires a "labeled" event, but the job-level gate
// only matches labeled events for "external", so no re-trigger.
console.log(`Non-member ${login} removed missing-issue-link — re-adding`);
try {
await ensureAndAddLabel('missing-issue-link', 'b76e79');
} catch (e) {
core.warning(
`Failed to re-add missing-issue-link (HTTP ${e.status ?? 'unknown'}): ${e.message}. ` +
`Downstream step will retry.`,
);
}
core.setOutput('has-link', 'false');
core.setOutput('is-assigned', 'false');
return;
}
// ── Maintainer override: reopened PR with "missing-issue-link" ──
const prLabels = context.payload.pull_request.labels.map(l => l.name);
if (action === 'reopened' && prLabels.includes('missing-issue-link')) {
const { isMember, login } = await senderIsOrgMember();
if (isMember) {
await applyMaintainerBypass(
`Maintainer ${login} reopened PR #${prNumber} — bypassing enforcement`,
);
return;
}
console.log(`Non-member ${login} reopened PR — proceeding with check`);
}
// ── Fetch live labels (race guard) ──────────────────────────────
const { data: liveLabels } = await github.rest.issues.listLabelsOnIssue({
owner, repo, issue_number: prNumber,
});
const liveNames = liveLabels.map(l => l.name);
if (liveNames.includes('trusted-contributor') || liveNames.includes('bypass-issue-check')) {
console.log('PR has trusted-contributor or bypass-issue-check label — bypassing');
core.setOutput('has-link', 'true');
core.setOutput('is-assigned', 'true');
return;
}
const body = context.payload.pull_request.body || '';
const pattern = /(?:close[sd]?|fix(?:e[sd])?|resolve[sd]?)\s*#(\d+)/gi;
const matches = [...body.matchAll(pattern)];
if (matches.length === 0) {
console.log('No issue link found in PR body');
core.setOutput('has-link', 'false');
core.setOutput('is-assigned', 'false');
return;
}
const issues = matches.map(m => `#${m[1]}`).join(', ');
console.log(`Found issue link(s): ${issues}`);
core.setOutput('has-link', 'true');
// Check whether the PR author is assigned to at least one linked issue
const prAuthor = context.payload.pull_request.user.login;
const MAX_ISSUES = 5;
const allIssueNumbers = [...new Set(matches.map(m => parseInt(m[1], 10)))];
const issueNumbers = allIssueNumbers.slice(0, MAX_ISSUES);
if (allIssueNumbers.length > MAX_ISSUES) {
core.warning(
`PR references ${allIssueNumbers.length} issues — only checking the first ${MAX_ISSUES}`,
);
}
let assignedToAny = false;
for (const num of issueNumbers) {
try {
const { data: issue } = await github.rest.issues.get({
owner, repo, issue_number: num,
});
const assignees = issue.assignees.map(a => a.login.toLowerCase());
if (assignees.includes(prAuthor.toLowerCase())) {
console.log(`PR author "${prAuthor}" is assigned to #${num}`);
assignedToAny = true;
break;
} else {
console.log(`PR author "${prAuthor}" is NOT assigned to #${num} (assignees: ${assignees.join(', ') || 'none'})`);
}
} catch (error) {
if (error.status === 404) {
console.log(`Issue #${num} not found — skipping`);
} else {
// Non-404 errors (rate limit, server error) must not be
// silently skipped — they could cause false enforcement
// (closing a legitimate PR whose assignment can't be verified).
throw new Error(
`Cannot verify assignee for issue #${num} (${error.status}): ${error.message}`,
);
}
}
}
core.setOutput('is-assigned', assignedToAny ? 'true' : 'false');
- name: Add missing-issue-link label
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
(steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
const labelName = 'missing-issue-link';
// Ensure the label exists (no checkout/shared helper available)
try {
await github.rest.issues.getLabel({ owner, repo, name: labelName });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({
owner, repo, name: labelName, color: 'b76e79',
});
} catch (createErr) {
if (createErr.status !== 422) throw createErr;
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: prNumber, labels: [labelName],
});
- name: Remove missing-issue-link label and reopen PR
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
steps.check-link.outputs.has-link == 'true' && steps.check-link.outputs.is-assigned == 'true'
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
try {
await github.rest.issues.removeLabel({
owner, repo, issue_number: prNumber, name: 'missing-issue-link',
});
} catch (error) {
if (error.status !== 404) throw error;
}
// Reopen if this workflow previously closed the PR. We check the
// event payload labels (not live labels) because we already removed
// missing-issue-link above; the payload still reflects pre-step state.
const labels = context.payload.pull_request.labels.map(l => l.name);
if (context.payload.pull_request.state === 'closed' && labels.includes('missing-issue-link')) {
await github.rest.pulls.update({
owner,
repo,
pull_number: prNumber,
state: 'open',
});
console.log(`Reopened PR #${prNumber}`);
}
// Minimize stale enforcement comment (best-effort;
// sync w/ applyMaintainerBypass above & reopen_on_assignment.yml)
try {
const marker = '<!-- require-issue-link -->';
const comments = await github.paginate(
github.rest.issues.listComments,
{ owner, repo, issue_number: prNumber, per_page: 100 },
);
const stale = comments.find(c => c.body && c.body.includes(marker));
if (stale) {
await github.graphql(`
mutation($id: ID!) {
minimizeComment(input: {subjectId: $id, classifier: OUTDATED}) {
minimizedComment { isMinimized }
}
}
`, { id: stale.node_id });
console.log(`Minimized stale enforcement comment ${stale.id} as outdated`);
}
} catch (e) {
core.warning(`Could not minimize stale comment on PR #${prNumber}: ${e.message}`);
}
- name: Post comment, close PR, and fail
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
(steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
const hasLink = '${{ steps.check-link.outputs.has-link }}' === 'true';
const isAssigned = '${{ steps.check-link.outputs.is-assigned }}' === 'true';
const marker = '<!-- require-issue-link -->';
let lines;
if (!hasLink) {
lines = [
marker,
'**This PR has been automatically closed** because it does not link to an approved issue.',
'',
'All external contributions must reference an approved issue or discussion. Please:',
'1. Find or [open an issue](https://github.com/' + owner + '/' + repo + '/issues/new/choose) describing the change',
'2. Wait for a maintainer to approve and assign you',
'3. Add `Fixes #<issue_number>`, `Closes #<issue_number>`, or `Resolves #<issue_number>` to your PR description and the PR will be reopened automatically',
'',
'*Maintainers: reopen this PR or remove the `missing-issue-link` label to bypass this check.*',
];
} else {
lines = [
marker,
'**This PR has been automatically closed** because you are not assigned to the linked issue.',
'',
'External contributors must be assigned to an issue before opening a PR for it. Please:',
'1. Comment on the linked issue to request assignment from a maintainer',
'2. Once assigned, your PR will be reopened automatically',
'',
'*Maintainers: reopen this PR or remove the `missing-issue-link` label to bypass this check.*',
];
}
const body = lines.join('\n');
// Deduplicate: check for existing comment with the marker
const comments = await github.paginate(
github.rest.issues.listComments,
{ owner, repo, issue_number: prNumber, per_page: 100 },
);
const existing = comments.find(c => c.body && c.body.includes(marker));
if (!existing) {
await github.rest.issues.createComment({
owner,
repo,
issue_number: prNumber,
body,
});
console.log('Posted requirement comment');
} else if (existing.body !== body) {
await github.rest.issues.updateComment({
owner,
repo,
comment_id: existing.id,
body,
});
console.log('Updated existing comment with new message');
} else {
console.log('Comment already exists — skipping');
}
// Close the PR
if (context.payload.pull_request.state === 'open') {
await github.rest.pulls.update({
owner,
repo,
pull_number: prNumber,
state: 'closed',
});
console.log(`Closed PR #${prNumber}`);
}
// Cancel all other in-progress and queued workflow runs for this PR
const headSha = context.payload.pull_request.head.sha;
for (const status of ['in_progress', 'queued']) {
const runs = await github.paginate(
github.rest.actions.listWorkflowRunsForRepo,
{ owner, repo, head_sha: headSha, status, per_page: 100 },
);
for (const run of runs) {
if (run.id === context.runId) continue;
try {
await github.rest.actions.cancelWorkflowRun({
owner, repo, run_id: run.id,
});
console.log(`Cancelled ${status} run ${run.id} (${run.name})`);
} catch (err) {
console.log(`Could not cancel run ${run.id}: ${err.message}`);
}
}
}
const reason = !hasLink
? 'PR must reference an issue using auto-close keywords (e.g., "Fixes #123").'
: 'PR author must be assigned to the linked issue.';
core.setFailed(reason);
+85
View File
@@ -0,0 +1,85 @@
name: Run notebooks
on:
workflow_dispatch:
workflow_call:
inputs:
changed-files:
required: false
type: string
description: "JSON string of changed files"
schedule:
- cron: "0 13 * * *"
permissions:
contents: read
defaults:
run:
working-directory: docs
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
lib-version:
- "development"
- "latest"
steps:
- uses: actions/checkout@v4
- name: Set up Python + Poetry
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "test-langgraph-notebooks"
- name: Install dependencies
run: |
uv sync --group test
uv run pip install jupyter
- name: Start services
run: make start-services
- name: Pre-download tiktoken files
run: |
uv run python _scripts/download_tiktoken.py
- name: Prepare notebooks
run: |
if [ "${{ matrix.lib-version }}" = "development" ]; then
uv run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
else
uv run python _scripts/prepare_notebooks_for_ci.py
fi
- name: Run notebooks
env:
# these won't actually be used because of the VCR cassettes
# but need to set them to avoid triggering getpass()
OPENAI_API_KEY: "very-secret-key"
ANTHROPIC_API_KEY: "very-secret-key"
TAVILY_API_KEY: "very-secret-key"
LANGSMITH_API_KEY: "very-secret-key"
NOMIC_API_KEY: "very-secret-key"
COHERE_API_KEY: "very-secret-key"
FIREWORKS_API_KEY: "very-secret-key"
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
echo "Running all notebooks"
./_scripts/execute_notebooks.sh
else
CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
if [ -n "$CHANGED_FILES" ]; then
echo "Running changed notebooks: $CHANGED_FILES"
./_scripts/execute_notebooks.sh $CHANGED_FILES
else
echo "No notebook files changed, skipping execution"
fi
fi
- name: Stop services
run: make stop-services
-402
View File
@@ -1,402 +0,0 @@
# Automatically tag issues as "external" or "internal" based on whether
# the author is a member of the langchain-ai GitHub organization, and
# apply contributor tier labels to external contributors based on their
# merged PR history.
#
# PR labeling is handled by tag-external-prs.yml.
# PR + issue backfill lives in the backfill job below (workflow_dispatch).
#
# Setup Requirements:
# 1. Create a GitHub App with permissions:
# - Repository: Issues (write), Pull requests (write)
# - Organization: Members (read)
# 2. Install the app on your organization and this repository
# 3. Add these repository secrets:
# - ORG_MEMBERSHIP_APP_ID: Your app's ID
# - ORG_MEMBERSHIP_APP_PRIVATE_KEY: Your app's private key
#
# The GitHub App token is required to check private organization membership.
# Without it, the workflow will fail.
name: Tag External Issues
on:
issues:
types: [opened]
workflow_dispatch:
inputs:
backfill_type:
description: "Backfill type (for initial run)"
default: "both"
type: choice
options:
- prs
- issues
- both
max_items:
description: "Maximum number of items to process"
default: "100"
type: string
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.issue.number || github.run_id }}
cancel-in-progress: true
jobs:
tag-external:
if: github.event_name == 'issues'
runs-on: ubuntu-latest
permissions:
issues: write
steps:
- name: Generate GitHub App token
id: app-token
uses: actions/create-github-app-token@v3
with:
app-id: ${{ secrets.ORG_MEMBERSHIP_APP_ID }}
private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}
- name: Check if contributor is external
if: steps.app-token.outcome == 'success'
id: check-membership
uses: actions/github-script@v9
with:
github-token: ${{ steps.app-token.outputs.token }}
script: |
const { owner, repo } = context.repo;
const author = context.payload.sender.login;
const senderType = context.payload.sender.type;
if (senderType === 'Bot') {
console.log(`${author} is a Bot — treating as internal`);
core.setOutput('is-external', 'false');
return;
}
try {
const membership = await github.rest.orgs.getMembershipForUser({
org: 'langchain-ai',
username: author,
});
const isExternal = membership.data.state !== 'active';
console.log(
isExternal
? `${author} has pending membership — treating as external`
: `${author} is an active member of langchain-ai`,
);
core.setOutput('is-external', isExternal ? 'true' : 'false');
} catch (e) {
if (e.status === 404) {
console.log(`${author} is not a member of langchain-ai`);
core.setOutput('is-external', 'true');
} else {
throw new Error(
`Membership check failed for ${author} (${e.status}): ${e.message}`,
);
}
}
- name: Apply contributor tier label
if: steps.check-membership.outputs.is-external == 'true'
uses: actions/github-script@v9
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const { owner, repo } = context.repo;
const issue = context.payload.issue;
const author = issue.user.login;
const issueNumber = issue.number;
const TRUSTED_THRESHOLD = 5;
const LABEL_COLOR = 'b76e79';
let mergedCount;
try {
const result = await github.rest.search.issuesAndPullRequests({
q: `repo:${owner}/${repo} is:pr is:merged author:"${author}"`,
per_page: 1,
});
mergedCount = result?.data?.total_count;
} catch (error) {
if (error?.status !== 422) throw error;
core.warning(`Search failed for ${author}; skipping tier label.`);
return;
}
if (mergedCount == null) {
core.warning(`Search response missing total_count for ${author}; skipping tier label.`);
return;
}
const tierLabel = mergedCount >= TRUSTED_THRESHOLD ? 'trusted-contributor' : null;
if (tierLabel) {
try {
await github.rest.issues.getLabel({ owner, repo, name: tierLabel });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({ owner, repo, name: tierLabel, color: LABEL_COLOR });
} catch (createErr) {
if (createErr.status !== 422) throw createErr;
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: issueNumber, labels: [tierLabel],
});
console.log(`Applied '${tierLabel}' to #${issueNumber} (${mergedCount} merged PRs)`);
} else {
console.log(`No tier label for ${author} (${mergedCount} merged PRs)`);
}
- name: Add external label
if: steps.check-membership.outputs.is-external == 'true'
uses: actions/github-script@v9
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const { owner, repo } = context.repo;
const issue_number = context.payload.issue.number;
await github.rest.issues.addLabels({
owner, repo, issue_number, labels: ['external'],
});
console.log(`Added 'external' label to issue #${issue_number}`);
- name: Add internal label
if: steps.check-membership.outputs.is-external == 'false'
uses: actions/github-script@v9
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const { owner, repo } = context.repo;
const issue_number = context.payload.issue.number;
await github.rest.issues.addLabels({
owner, repo, issue_number, labels: ['internal'],
});
console.log(`Added 'internal' label to issue #${issue_number}`);
backfill:
if: github.event_name == 'workflow_dispatch'
runs-on: ubuntu-latest
permissions:
contents: read
issues: write
pull-requests: write
steps:
- name: Generate GitHub App token
id: app-token
uses: actions/create-github-app-token@v3
with:
app-id: ${{ secrets.ORG_MEMBERSHIP_APP_ID }}
private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}
- name: Backfill labels
uses: actions/github-script@v9
with:
github-token: ${{ steps.app-token.outputs.token }}
script: |
const { owner, repo } = context.repo;
const rawMax = '${{ inputs.max_items }}';
const maxItems = parseInt(rawMax, 10);
if (isNaN(maxItems) || maxItems <= 0) {
core.setFailed(`Invalid max_items: "${rawMax}" — must be a positive integer`);
return;
}
const backfillType = '${{ inputs.backfill_type }}';
const TRUSTED_THRESHOLD = 5;
const LABEL_COLOR = 'b76e79';
const tierLabels = ['trusted-contributor'];
// ── Helpers ─────────────────────────────────────────────────
async function ensureLabel(name) {
try {
await github.rest.issues.getLabel({ owner, repo, name });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({ owner, repo, name, color: LABEL_COLOR });
} catch (createErr) {
if (createErr.status !== 422) throw createErr;
}
}
}
async function checkMembership(author, userType) {
if (userType === 'Bot') {
console.log(`${author} is a Bot — treating as internal`);
return { isExternal: false };
}
try {
const membership = await github.rest.orgs.getMembershipForUser({
org: 'langchain-ai',
username: author,
});
const isExternal = membership.data.state !== 'active';
console.log(
isExternal
? `${author} has pending membership — treating as external`
: `${author} is an active member of langchain-ai`,
);
return { isExternal };
} catch (e) {
if (e.status === 404) {
console.log(`${author} is not a member of langchain-ai`);
return { isExternal: true };
}
throw new Error(
`Membership check failed for ${author} (${e.status}): ${e.message}`,
);
}
}
async function getContributorInfo(contributorCache, author, userType) {
if (contributorCache.has(author)) return contributorCache.get(author);
const { isExternal } = await checkMembership(author, userType);
let mergedCount = null;
if (isExternal) {
try {
const result = await github.rest.search.issuesAndPullRequests({
q: `repo:${owner}/${repo} is:pr is:merged author:"${author}"`,
per_page: 1,
});
mergedCount = result?.data?.total_count ?? null;
} catch (e) {
if (e?.status !== 422) throw e;
core.warning(`Search failed for ${author}; skipping tier.`);
}
}
const info = { isExternal, mergedCount };
contributorCache.set(author, info);
return info;
}
// ── Setup ────────────────────────────────────────────────────
for (const name of tierLabels) {
await ensureLabel(name);
}
const contributorCache = new Map();
let processed = 0;
let failures = 0;
// ── Backfill PRs ─────────────────────────────────────────────
if (backfillType === 'prs' || backfillType === 'both') {
const prs = await github.paginate(github.rest.pulls.list, {
owner, repo, state: 'open', per_page: 100,
});
for (const pr of prs) {
if (processed >= maxItems) break;
try {
const author = pr.user.login;
const info = await getContributorInfo(contributorCache, author, pr.user.type);
const labels = [info.isExternal ? 'external' : 'internal'];
if (info.isExternal && info.mergedCount != null && info.mergedCount >= TRUSTED_THRESHOLD) {
labels.push('trusted-contributor');
}
// Ensure all labels exist before batch add
for (const name of labels) {
await ensureLabel(name);
}
// Remove stale tier labels
const currentLabels = (await github.paginate(
github.rest.issues.listLabelsOnIssue,
{ owner, repo, issue_number: pr.number, per_page: 100 },
)).map(l => l.name ?? '');
for (const name of currentLabels) {
if (tierLabels.includes(name) && !labels.includes(name)) {
try {
await github.rest.issues.removeLabel({
owner, repo, issue_number: pr.number, name,
});
} catch (e) {
if (e.status !== 404) throw e;
}
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: pr.number, labels,
});
console.log(`PR #${pr.number} (${author}): ${labels.join(', ')}`);
processed++;
} catch (e) {
failures++;
core.warning(`Failed to process PR #${pr.number}: ${e.message}`);
}
}
}
// ── Backfill issues ──────────────────────────────────────────
if (backfillType === 'issues' || backfillType === 'both') {
const issues = await github.paginate(github.rest.issues.listForRepo, {
owner, repo, state: 'open', per_page: 100,
});
for (const issue of issues) {
if (processed >= maxItems) break;
if (issue.pull_request) continue;
try {
const author = issue.user.login;
const info = await getContributorInfo(contributorCache, author, issue.user.type);
const labels = [info.isExternal ? 'external' : 'internal'];
if (info.isExternal && info.mergedCount != null && info.mergedCount >= TRUSTED_THRESHOLD) {
labels.push('trusted-contributor');
}
// Ensure all labels exist before batch add
for (const name of labels) {
await ensureLabel(name);
}
// Remove stale tier labels
const currentLabels = (await github.paginate(
github.rest.issues.listLabelsOnIssue,
{ owner, repo, issue_number: issue.number, per_page: 100 },
)).map(l => l.name ?? '');
for (const name of currentLabels) {
if (tierLabels.includes(name) && !labels.includes(name)) {
try {
await github.rest.issues.removeLabel({
owner, repo, issue_number: issue.number, name,
});
} catch (e) {
if (e.status !== 404) throw e;
}
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: issue.number, labels,
});
console.log(`Issue #${issue.number} (${author}): ${labels.join(', ')}`);
processed++;
} catch (e) {
failures++;
core.warning(`Failed to process issue #${issue.number}: ${e.message}`);
}
}
}
console.log(`\nBackfill complete. Processed ${processed} items, ${failures} failures. ${contributorCache.size} unique authors.`);
-173
View File
@@ -1,173 +0,0 @@
# Automatically tag pull requests as "external" or "internal" based on
# whether the author is a member of the langchain-ai GitHub organization,
# and apply contributor tier labels to external contributors based on
# their merged PR history.
#
# Issue labeling is handled by tag-external-issues.yml.
# Backfill (workflow_dispatch) also lives in tag-external-issues.yml.
#
# Setup Requirements:
# 1. Create a GitHub App with permissions:
# - Repository: Pull requests (write)
# - Organization: Members (read)
# 2. Install the app on your organization and this repository
# 3. Add these repository secrets:
# - ORG_MEMBERSHIP_APP_ID: Your app's ID
# - ORG_MEMBERSHIP_APP_PRIVATE_KEY: Your app's private key
#
# The GitHub App token is required to check private organization membership.
# Without it, the workflow will fail.
name: Tag External PRs
on:
pull_request_target:
types: [opened]
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.run_id }}
cancel-in-progress: true
jobs:
tag-external:
runs-on: ubuntu-latest
permissions:
pull-requests: write
steps:
- name: Generate GitHub App token
id: app-token
uses: actions/create-github-app-token@v3
with:
app-id: ${{ secrets.ORG_MEMBERSHIP_APP_ID }}
private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}
- name: Check if contributor is external
if: steps.app-token.outcome == 'success'
id: check-membership
uses: actions/github-script@v9
with:
github-token: ${{ steps.app-token.outputs.token }}
script: |
const { owner, repo } = context.repo;
const author = context.payload.sender.login;
const senderType = context.payload.sender.type;
if (senderType === 'Bot') {
console.log(`${author} is a Bot — treating as internal`);
core.setOutput('is-external', 'false');
return;
}
try {
const membership = await github.rest.orgs.getMembershipForUser({
org: 'langchain-ai',
username: author,
});
const isExternal = membership.data.state !== 'active';
console.log(
isExternal
? `${author} has pending membership — treating as external`
: `${author} is an active member of langchain-ai`,
);
core.setOutput('is-external', isExternal ? 'true' : 'false');
} catch (e) {
if (e.status === 404) {
console.log(`${author} is not a member of langchain-ai`);
core.setOutput('is-external', 'true');
} else {
throw new Error(
`Membership check failed for ${author} (${e.status}): ${e.message}`,
);
}
}
# Apply tier label BEFORE the external label so that
# "trusted-contributor" is already present when the "external" labeled
# event fires and triggers require_issue_link.yml.
- name: Apply contributor tier label
if: steps.check-membership.outputs.is-external == 'true'
uses: actions/github-script@v9
with:
# Use App token so the "labeled" event propagates to downstream
# workflows (e.g. require_issue_link.yml).
github-token: ${{ steps.app-token.outputs.token }}
script: |
const { owner, repo } = context.repo;
const pr = context.payload.pull_request;
const author = pr.user.login;
const prNumber = pr.number;
const TRUSTED_THRESHOLD = 5;
const LABEL_COLOR = 'b76e79';
let mergedCount;
try {
const result = await github.rest.search.issuesAndPullRequests({
q: `repo:${owner}/${repo} is:pr is:merged author:"${author}"`,
per_page: 1,
});
mergedCount = result?.data?.total_count;
} catch (error) {
if (error?.status !== 422) throw error;
core.warning(`Search failed for ${author}; skipping tier label.`);
return;
}
if (mergedCount == null) {
core.warning(`Search response missing total_count for ${author}; skipping tier label.`);
return;
}
const tierLabel = mergedCount >= TRUSTED_THRESHOLD ? 'trusted-contributor' : null;
if (tierLabel) {
try {
await github.rest.issues.getLabel({ owner, repo, name: tierLabel });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({ owner, repo, name: tierLabel, color: LABEL_COLOR });
} catch (createErr) {
if (createErr.status !== 422) throw createErr;
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: prNumber, labels: [tierLabel],
});
console.log(`Applied '${tierLabel}' to PR #${prNumber} (${mergedCount} merged PRs)`);
} else {
console.log(`No tier label for ${author} (${mergedCount} merged PRs)`);
}
- name: Add external label
if: steps.check-membership.outputs.is-external == 'true'
uses: actions/github-script@v9
with:
# Use App token so the "labeled" event propagates to downstream
# workflows (e.g. require_issue_link.yml). Events created by the
# default GITHUB_TOKEN do not trigger additional workflow runs.
github-token: ${{ steps.app-token.outputs.token }}
script: |
const { owner, repo } = context.repo;
const issue_number = context.payload.pull_request.number;
await github.rest.issues.addLabels({
owner, repo, issue_number, labels: ['external'],
});
console.log(`Added 'external' label to PR #${issue_number}`);
- name: Add internal label
if: steps.check-membership.outputs.is-external == 'false'
uses: actions/github-script@v9
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const { owner, repo } = context.repo;
const issue_number = context.payload.pull_request.number;
await github.rest.issues.addLabels({
owner, repo, issue_number, labels: ['internal'],
});
console.log(`Added 'internal' label to PR #${issue_number}`);
+8 -6
View File
@@ -16,23 +16,25 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up uv
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v6
with:
python-version: "3.10"
# use minimum supported Python version
python-version: "3.9"
enable-cache: true
cache-suffix: "uv-lock-upgrade"
- name: Run uv lock --upgrade in all Python packages
run: make lock-upgrade
- name: Create Pull Request
uses: peter-evans/create-pull-request@5f6978faf089d4d20b00c7766989d076bb2fc7f1 # v8
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.GITHUB_TOKEN }}
commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
title: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
body: |
This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
+82 -1
View File
@@ -6,6 +6,9 @@ __pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
@@ -51,12 +54,27 @@ coverage.xml
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
docs/docs/_build/
# PyBuilder
target/
@@ -71,9 +89,23 @@ ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.envrc
@@ -85,6 +117,16 @@ ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
@@ -96,8 +138,47 @@ dmypy.json
# macOS display setting files
.DS_Store
# Wandb directory
wandb/
# asdf tool versions
.tool-versions
/.ruff_cache/
*.pkl
*.bin
# integration test artifacts
data_map*
\[('_type', 'fake'), ('stop', None)]
# Replit files
*replit*
node_modules
docs/.yarn/
docs/node_modules/
docs/.docusaurus/
docs/.cache-loader/
docs/_dist
docs/api_reference/api_reference.rst
docs/api_reference/experimental_api_reference.rst
docs/api_reference/_build
docs/api_reference/*/
!docs/api_reference/_static/
!docs/api_reference/templates/
!docs/api_reference/themes/
docs/docs_skeleton/build
docs/docs_skeleton/node_modules
docs/docs_skeleton/yarn.lock
# Any new jupyter notebooks
# not intended for the repo
Untitled*.ipynb
Chinook.db
.vercel
.turbo
.editorconfig
.scratch
.worktrees/
+4
View File
@@ -0,0 +1,4 @@
{
"aliveStatusCodes": [200, 206, 402],
"ignorePatterns": ["*dcbadge.vercel.app*"]
}
-14
View File
@@ -1,14 +0,0 @@
{
"MD013": false,
"MD024": {
"siblings_only": true
},
"MD025": false,
"MD033": false,
"MD034": false,
"MD036": false,
"MD041": false,
"MD046": {
"style": "fenced"
}
}
+2 -4
View File
@@ -10,7 +10,7 @@ When you modify code in any library, run the following commands in that library'
To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
```txt
```
TEST=path/to/test.py make test
```
@@ -28,7 +28,7 @@ Below is a high-level overview:
- **langgraph** core framework for building stateful, multi-actor agents.
- **prebuilt** high-level APIs for creating and running agents and tools.
- **sdk-js** JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** Python SDK for the LangGraph Server API.
- **sdk-py** Python SDK for the LangGraph Platform API.
### Dependency map
@@ -53,5 +53,3 @@ sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
-57
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@@ -1,57 +0,0 @@
# AGENTS Instructions
This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
When you modify code in any library, run the following commands in that library's directory before creating a pull request:
- `make format` run code formatters
- `make lint` run the linter
- `make test` execute the test suite
To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
```
TEST=path/to/test.py make test
```
Other pytest arguments can also be supplied inside the `TEST` variable.
## Libraries
The repository contains several Python and JavaScript/TypeScript libraries.
Below is a high-level overview:
- **checkpoint** base interfaces for LangGraph checkpointers.
- **checkpoint-postgres** Postgres implementation of the checkpoint saver.
- **checkpoint-sqlite** SQLite implementation of the checkpoint saver.
- **cli** official command-line interface for LangGraph.
- **langgraph** core framework for building stateful, multi-actor agents.
- **prebuilt** high-level APIs for creating and running agents and tools.
- **sdk-js** JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** Python SDK for the LangGraph Server API.
### Dependency map
The diagram below lists downstream libraries for each production dependency as
declared in that library's `pyproject.toml` (or `package.json`).
```text
checkpoint
├── checkpoint-postgres
├── checkpoint-sqlite
├── prebuilt
└── langgraph
prebuilt
└── langgraph
sdk-py
├── langgraph
└── cli
sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
+293
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@@ -0,0 +1,293 @@
# Contributing to LangGraph
Thank you for being interested in contributing to LangGraph!
## General guidelines
Here are some things to keep in mind for all types of contributions:
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
- If you would like comments or feedback, please tag a maintainer.
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
- Look for duplicate PRs or issues that have already been opened before opening a new one.
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
### Bugfixes
For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
### New features
For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
## Contribute Documentation
Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
community improvements to our current documentation. Please read the resources below before getting started:
- [Documentation style guide](#documentation-style-guide)
- [Documentation setup](#setup)
## Documentation Style Guide
As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
## Philosophy
LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
[How-to guides](#how-to-guides),
[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
They should **avoid** giving
multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
To quote the Diataxis website:
> A tutorial serves the users *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
In LangGraph, these are often higher level guides that show off end-to-end use cases.
Some examples include:
- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
Here are some high-level tips on writing a good tutorial:
- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
- Be specific, not abstract and follow one path.
- No need to go deeply into alternative approaches, but its ok to reference them, ideally with a link to an appropriate how-to guide.
- Get "a point on the board" as soon as possible - something the user can run that outputs something.
- You can iterate and expand afterwards.
- Try to frequently checkpoint at given steps where the user can run code and see progress.
- Focus on results, not technical explanation.
- Crosslink heavily to appropriate conceptual/reference pages
- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
- It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
- Do not shy away from repetition.
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
should still give some background or list the scenarios where the information contained within can be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
Some examples include:
- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
Here are some high-level tips on writing a good how-to guide:
- Clearly explain what you are guiding the user through at the start
- Assume higher intent than a tutorial and show what the user needs to do to get that task done
- Assume familiarity of concepts, but explain why suggested actions are helpful
- Crosslink heavily to conceptual/reference pages
- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
### Conceptual guides
LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
To quote the Diataxis website:
> The perspective of explanation is higher and wider than that of the other types. It does not take the users eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
Some examples include:
- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
- [Tool calling](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling)
Here are some high-level tips on writing a good conceptual guide:
- Explain design decisions. Why does concept X exist and why was it designed this way?
- Use analogies and reference other concepts and alternatives
- Avoid blending in too much reference content
- You can and should reference content covered in other guides, but make sure to link to them
### References
References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
how to use something specific.
To quote the Diataxis website:
> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
Many of the reference pages in LangChain are automatically generated from code,
but here are some high-level tips on writing a good docstring:
- Be concise
- Discuss special cases and deviations from a user's expectations
- Go into detail on required inputs and outputs
- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## General guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
We generally do not merge new tutorials from outside contributors without an actual need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
## Setup
LangGraph documentation consists of two components:
1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
this comprehensive resource serves as the primary user-facing documentation.
It covers a wide array of topics, including tutorials, use cases, integrations,
and more, offering extensive guidance on building with LangGraph.
The content for this documentation lives in the `/docs` directory of the monorepo.
2. In-code Documentation: This is documentation of the codebase itself, which is also
used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
We appreciate all contributions to the documentation, whether it be fixing a typo,
adding a new tutorial or example and whether it be in the main documentation or the API Reference.
### 📜 Main Documentation
The content for the main documentation is located in the `/docs` directory of the monorepo.
The documentation is written using a combination of ipython notebooks (`.ipynb` files)
and markdown (`.md` files). The notebooks are converted to markdown
and then built using [MkDocs](https://www.mkdocs.org/).
Feel free to make contributions to the main documentation! 🥰
After modifying the documentation:
1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
2. Optionally build the documentation locally to verify that the changes look good.
3. Make a pull request with the changes.
### ⚒️ Linting and Building Documentation Locally
After writing up the documentation, you may want to lint and build the documentation
locally to ensure that it looks good and is free of errors.
If you're unable to build it locally that's okay as well, as you will be able to
see a preview of the documentation on the pull request page.
From the **monorepo root**, run the following command to install the dependencies:
<!-- TODO -->
```bash
poetry install --with docs --no-root
```
#### Building
The code that builds the documentation is located in the `/docs` directory of the monorepo.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make clean-docs
```
You can build and preview the documentation as outlined below:
```bash
make serve-docs
```
#### Linting
To spell check the docs, run the following from the `docs` directory:
```bash
codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
```
### In-code Documentation
The in-code documentation is autogenerated from docstrings.
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
Here is an example of a well-documented function:
```python
def my_function(arg1: int, arg2: str) -> float:
"""This is a short description of the function. (It should be a single sentence.)
This is a longer description of the function. It should explain what
the function does, what the arguments are, and what the return value is.
It should wrap at 88 characters.
Examples:
This is a section for examples of how to use the function.
.. code-block:: python
my_function(1, "hello")
Args:
arg1: This is a description of arg1. We do not need to specify the type since
it is already specified in the function signature.
arg2: This is a description of arg2.
Returns:
This is a description of the return value.
"""
return 3.14
```
+57 -55
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@@ -1,82 +1,84 @@
<div align="center">
<a href="https://www.langchain.com/langgraph">
<picture>
<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
</picture>
</a>
</div>
<div align="center">
<h3>Low-level orchestration framework for building stateful agents.</h3>
</div>
<div align="center">
<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
<a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
<a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
<a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
</div>
<picture class="github-only">
<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
</picture>
<div>
<br>
</div>
[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
[![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
Trusted by companies shaping the future of agents including Klarna, Replit, Elastic, and more LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
```bash
## Get started
Install LangGraph:
```
pip install -U langgraph
```
> [!TIP]
> If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
For an equivalent JS/TS library, check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
```python
# pip install -qU "langchain[anthropic]" to call the model
## Why use LangGraph?
from langgraph.prebuilt import create_react_agent
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
- **[Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- **[Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- **[Debugging with LangSmith](https://www.langchain.com/langsmith)** — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- **[Production-ready deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_weather],
prompt="You are a helpful assistant"
)
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
```
## LangGraph ecosystem
For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
## Core benefits
To improve your LLM application development, pair LangGraph with:
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) Provides integrations and composable components to streamline LLM application development.
- [LangSmith](https://www.langchain.com/langsmith) Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
---
## LangGraphs ecosystem
## Documentation
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) Comprehensive documentation, including conceptual overviews and guides
- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) API reference docs for LangGraph packages
- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) Get started building with LangGraph
- [Chat LangChain](https://chat.langchain.com/) Chat with the LangChain documentation and get answers to your questions
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
- [LangChain](https://python.langchain.com/docs/introduction/) Provides integrations and composable components to streamline LLM application development.
**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
> [!NOTE]
> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
## Additional resources
- **[Guides](https://docs.langchain.com/oss/python/learn)** Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** Learn the basics of LangGraph in our free, structured course.
- **[Case studies](https://www.langchain.com/built-with-langgraph)** Hear how industry leaders use LangGraph to ship AI applications at scale.
- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) Learn how to contribute to LangChain projects and find good first issues.
- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) Our community guidelines and standards for participation.
---
- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
## Acknowledgements
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
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_site/
site/
.vercel
+66
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@@ -0,0 +1,66 @@
.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell llms-text build-prebuilt tests
build-prebuilt:
# Use to create an update to date prebuilt page.
# Looks up download stats for each of the prebuilt packages and
# generates the final prebuilt page.
@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
set -x; \
uv run python -m _scripts.third_party_page.get_download_stats stats.yml; \
set +x; \
else \
set -x; \
uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
set +x; \
fi
uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
build-docs: build-prebuilt
uv run python -m mkdocs build --clean -f mkdocs.yml --strict
llms-text:
uv run python -m _scripts.generate_llms_text docs/llms-full.txt
install-vercel-deps:
curl -sL "https://astral.sh/uv/install.sh" | bash -s
export PATH="${HOME}/.cargo/bin:${PATH}"
uv venv --python 3.11
uv sync --all-groups
tests:
# Run unit tests
uv run pytest tests/unit_tests
vercel-build-docs: install-vercel-deps
make build-docs
serve-clean-docs: clean-docs
uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
serve-docs:
uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph -w ../libs/checkpoint -w ../libs/sdk-py --dirty
clean-docs:
find ./docs -name "*.ipynb" -type f -delete
rm -rf site
## Run format against the project documentation.
format-docs:
uv run ruff format docs
uv run ruff check --fix docs
# Check the docs for linting violations
lint-docs:
uv run ruff format --check docs
uv run ruff check docs
codespell:
./codespell_notebooks.sh .
start-services:
docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
stop-services:
docker compose -f test-compose.yml down
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# Setup
To setup requirements for building docs you can run:
```bash
uv sync --group test
```
## Serving documentation locally
To run the documentation server locally you can run:
```bash
make serve-docs
```
This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
## Execute notebooks
If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
```bash
python _scripts/prepare_notebooks_for_ci.py
./_scripts/execute_notebooks.sh
```
**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
```bash
python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
./_scripts/execute_notebooks.sh
```
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
## Adding new notebooks
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
Then, run
```bash
jupyter execute <path_to_notebook>
```
Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
## Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
To delete cassettes for a notebook, you can run:
```bash
rm cassettes/<notebook_name>*
```
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import functools
from urllib3 import __version__ as urllib3version # type: ignore[import-untyped]
from urllib3 import connection # type: ignore[import-untyped]
def _ensure_str(s, encoding="utf-8", errors="strict") -> str:
if isinstance(s, str):
return s
if isinstance(s, bytes):
return s.decode(encoding, errors)
return str(s)
# Copied from https://github.com/urllib3/urllib3/blob/1c994dfc8c5d5ecaee8ed3eb585d4785f5febf6e/src/urllib3/connection.py#L231
def request(self, method, url, body=None, headers=None):
"""Make the request.
This function is based on the urllib3 request method, with modifications
to handle potential issues when using vcrpy in concurrent workloads.
Args:
self: The HTTPConnection instance.
method (str): The HTTP method (e.g., 'GET', 'POST').
url (str): The URL for the request.
body (Optional[Any]): The body of the request.
headers (Optional[dict]): Headers to send with the request.
Returns:
The result of calling the parent request method.
"""
# Update the inner socket's timeout value to send the request.
# This only triggers if the connection is reused.
if getattr(self, "sock", None) is not None:
self.sock.settimeout(self.timeout)
if headers is None:
headers = {}
else:
# Avoid modifying the headers passed into .request()
headers = headers.copy()
if "user-agent" not in (_ensure_str(k.lower()) for k in headers):
headers["User-Agent"] = connection._get_default_user_agent()
# The above is all the same ^^^
# The following is different:
return self._parent_request(method, url, body=body, headers=headers)
_PATCHED = False
def patch_urllib3():
"""Patch the request method of urllib3 to avoid type errors when using vcrpy.
In concurrent workloads (such as the tracing background queue), the
connection pool can get in a state where an HTTPConnection is created
before vcrpy patches the HTTPConnection class. In urllib3 >= 2.0 this isn't
a problem since they use the proper super().request(...) syntax, but in older
versions, super(HTTPConnection, self).request is used, resulting in a TypeError
since self is no longer a subclass of "HTTPConnection" (which at this point
is vcr.stubs.VCRConnection).
This method patches the class to fix the super() syntax to avoid mixed inheritance.
In the case of the LangSmith tracing logic, it doesn't really matter since we always
exclude cache checks for calls to LangSmith.
The patch is only applied for urllib3 versions older than 2.0.
"""
global _PATCHED
if _PATCHED:
return
from packaging import version
if version.parse(urllib3version) >= version.parse("2.0"):
_PATCHED = True
return
# Lookup the parent class and its request method
parent_class = connection.HTTPConnection.__bases__[0]
parent_request = parent_class.request
def new_request(self, *args, **kwargs):
"""Handle parent request.
This method binds the parent's request method to self and then
calls our modified request function.
"""
self._parent_request = functools.partial(parent_request, self)
return request(self, *args, **kwargs)
connection.HTTPConnection.request = new_request
_PATCHED = True
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"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
import argparse
import requests
from langchain_anthropic import ChatAnthropic
# Load reference TypeScript snippets
URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
response = requests.get(URL)
response.raise_for_status()
reference_snippets = response.text
# Initialize model
model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
TRANSLATION_PROMPT = (
"You are a helpful assistant that translates Python-based technical "
"documentation written in Markdown to equivalent TypeScript-based documentation. "
"The input is a Markdown file written in mkdocs format. It contains "
"Python code snippets embedded in prose. "
"Your task is to rewrite the content by translating the Python code to "
"idiomatic TypeScript, using the provided TypeScript reference snippets "
"to ensure accurate and consistent usage (e.g., correct imports, function "
"names, and patterns). "
"Remove the original Python code and replace it with the corresponding "
"TypeScript version. "
"Do not alter the surrounding prose unless a change is necessary to "
"reflect differences between Python and TypeScript. "
"Preserve the structure and formatting of the original Markdown document. "
"Do not make stylistic or structural changes unless they directly support "
"the translation. "
"Use the reference TypeScript snippets as guidance whenever possible to "
"maintain alignment with existing conventions.\n\n"
f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
)
CONSOLIDATION_PROMPT = (
"You are a helpful assistant that consolidates parallel Python and JavaScript (TypeScript) technical documentation "
"written in Markdown into a single unified Markdown document. "
"The input consists of two documents: the first is for Python users, and the second is for JavaScript/TypeScript users. "
"Your task is to merge these into one Markdown file using language-specific fenced blocks to separate the content where needed. "
"Use the following syntax to distinguish content for each language:\n\n"
":::python\n"
"# Python-specific content\n"
":::\n\n"
":::js\n"
"# JavaScript/TypeScript-specific content\n"
":::\n\n"
"Follow these consolidation rules:\n"
"- When content (prose or code) is the same or nearly identical in both versions, include it only once—outside of any fenced block.\n"
"- When content differs between the Python and JS versions, wrap each version in its corresponding fenced block.\n"
"- Prefer **paragraph-level separation** of language-specific content. Do not combine Python and JS snippets or terminology in the same sentence or paragraph using conditional phrases.\n"
" For example, avoid inline constructs like:\n"
" `The :::python add_messages ::: :::js reducer ::: function...`\n"
" Instead, write two distinct paragraphs:\n\n"
" :::python\n"
" The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.\n"
" ::: \n\n"
" :::js\n"
" The `reducer` function in our `StateAnnotation` will append the LLM's response messages to whatever messages are already in the state.\n"
" :::\n\n"
"- Preserve the overall structure, ordering, and formatting of the original Markdown documents.\n"
"- Do not rephrase or unify content unless it is logically and semantically identical.\n"
"- Use the fenced blocks for both prose and code as needed, and ensure output is clean, readable Markdown suitable for tools that parse these directives.\n"
"Your goal is to produce a cleanly merged documentation file that serves both Python and JavaScript users without redundancy, while maximizing clarity and separation of language-specific details."
)
def translate_python_to_ts(markdown_content: str) -> str:
response = model.invoke(
[
{
"role": "system",
"content": TRANSLATION_PROMPT,
"cache_control": {"type": "ephemeral"},
},
{"role": "user", "content": markdown_content},
]
)
return response.content
def consolidate_python_and_ts(combined_content: str) -> str:
response = model.invoke(
[
{
"role": "system",
"content": CONSOLIDATION_PROMPT,
"cache_control": {"type": "ephemeral"},
},
{"role": "user", "content": combined_content},
]
)
return response.content
def main(file_path: str, translate_only: bool, consolidate_only: bool) -> None:
with open(file_path, "r", encoding="utf-8") as f:
markdown_content = f.read()
if translate_only:
translated = translate_python_to_ts(markdown_content)
output_path = file_path.replace(".md", ".translated.md")
with open(output_path, "w", encoding="utf-8") as f:
f.write(translated)
print(f"Translated JS/TS version written to: {output_path}")
elif consolidate_only:
consolidated = consolidate_python_and_ts(markdown_content)
with open(file_path, "w", encoding="utf-8") as f:
f.write(consolidated)
print(f"Consolidated content written to: {file_path}")
else:
# Default behavior: translate first, then consolidate both
translated = translate_python_to_ts(markdown_content)
combined = f"{markdown_content.strip()}\n\n\n{translated.strip()}"
consolidated = consolidate_python_and_ts(combined)
with open(file_path, "w", encoding="utf-8") as f:
f.write(consolidated)
print(f"Translated and consolidated content written to: {file_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=(
"Translate Python markdown to TypeScript and/or consolidate "
"Python-JS markdown into one file."
)
)
parser.add_argument("file_path", type=str, help="Path to the markdown file.")
parser.add_argument(
"--translate-only",
action="store_true",
help="Only generate the JS translation.",
)
parser.add_argument(
"--consolidate-only",
action="store_true",
help="Only consolidate pre-paired Python and JS content.",
)
args = parser.parse_args()
if args.translate_only and args.consolidate_only:
raise ValueError(
"Cannot use both --translate-only and --consolidate-only at the same time."
)
main(
args.file_path,
translate_only=args.translate_only,
consolidate_only=args.consolidate_only,
)
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import tiktoken
# This will trigger the download and caching of the necessary files
for encoding in ("gpt2", "gpt-3.5"):
tiktoken.encoding_for_model(encoding)
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#!/bin/bash
# Read the list of notebooks to skip from the JSON file
SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
# Function to execute a single notebook
execute_notebook() {
file="$1"
echo "Starting execution of $file"
start_time=$(date +%s)
if ! output=$(time uv run jupyter execute "$file" 2>&1); then
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Error in $file. Execution time: $execution_time seconds"
echo "Error details: $output"
exit 1
fi
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Finished $file. Execution time: $execution_time seconds"
}
export -f execute_notebook
# Check if custom notebook paths are provided
if [ $# -gt 0 ]; then
notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
else
# Find all notebooks and filter out those in the skip list
notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
fi
# Execute notebooks sequentially
for file in $notebooks; do
execute_notebook "$file"
done
@@ -0,0 +1,284 @@
import ast
import importlib
import logging
import re
from functools import lru_cache
from typing import List, Optional
from typing_extensions import TypedDict
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Base URL for all class documentation
_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
# (alias/re-exported modules, source module, class, docs namespace)
MANUAL_API_REFERENCES_LANGGRAPH = [
(
["langgraph.prebuilt"],
"langgraph.prebuilt.chat_agent_executor",
"create_react_agent",
"prebuilt",
),
(
[],
"langgraph.prebuilt.chat_agent_executor",
"AgentState",
"prebuilt",
),
(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"tools_condition",
"prebuilt",
),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"InjectedState",
"prebuilt",
),
# Graph
(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
(["langgraph.graph"], "langgraph.constants", "START", "constants"),
(["langgraph.graph"], "langgraph.constants", "END", "constants"),
(["langgraph.constants"], "langgraph.types", "Send", "types"),
(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
(["langgraph.constants"], "langgraph.types", "interrupt", "types"),
(["langgraph.constants"], "langgraph.types", "Command", "types"),
(["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
(["langgraph.config"], "langgraph.config", "get_store", "config"),
(["langgraph.func"], "langgraph.func", "entrypoint", "func"),
(["langgraph.func"], "langgraph.func", "task", "func"),
(["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
(["langgraph.types"], "langgraph.types", "StreamMode", "types"),
(["langgraph.types"], "langgraph.types", "StreamWriter", "types"),
([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
# other prebuilts
(["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
(["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
]
WELL_KNOWN_LANGGRAPH_OBJECTS = {
(module_, class_): (source_module, namespace)
for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
for module_ in modules + [source_module]
}
@lru_cache(maxsize=10_000)
def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
"""Get full module name using inspect, with LRU cache to memoize results."""
try:
module = importlib.import_module(module_path)
symbol = getattr(module, class_name)
# First check the __module__ attribute on the symbol.
mod_name = getattr(symbol, "__module__", None)
# If __module__ is not set or comes from typing,
# assume the definition is in module_path.
if mod_name is None or mod_name.startswith("typing"):
return module_path
return mod_name
except AttributeError as e:
logger.warning(f"API Reference: Could not find module for {class_name}, {e}")
return None
except ImportError as e:
logger.warning(f"API Reference: Failed to load for class {class_name}, {e}")
return None
class ImportInformation(TypedDict):
imported: str # The name of the class that was imported.
source: str # The full module path from which the class was imported.
docs: str # The URL pointing to the class's documentation.
path: str # The path of the file where the markdown content originated.
def get_imports(code: str, path: str) -> List[ImportInformation]:
"""Retrieve all import references from the given code for specified ecosystems.
Args:
code: The source code from which to extract import references.
path: The path of the file where the markdown content originated.
Returns:
A list of import information for each import found.
"""
# Parse the code into an AST.
try:
tree = ast.parse(code)
except SyntaxError:
return []
found_imports = []
# Walk through the AST and process ImportFrom nodes.
for node in ast.walk(tree):
if isinstance(node, ast.ImportFrom):
# node.module is the source module.
if node.module is None:
continue
for alias in node.names:
if not (
node.module.startswith("langchain")
or node.module.startswith("langgraph")
):
continue
found_imports.append(
{
"source": node.module,
# alias.name is the original name even if an alias exists.
"imported": alias.name,
}
)
imports: list[ImportInformation] = []
for found_import in found_imports:
module = found_import["source"]
if module.startswith("langchain_mcp_adapters"):
package_ecosystem = "langgraph"
elif module.startswith("langchain"):
# Handles things like `langchain` or `langchain_anthropic`
package_ecosystem = "langchain"
elif module.startswith("langgraph"):
package_ecosystem = "langgraph"
else:
continue
class_name = found_import["imported"]
module_path = _get_full_module_name(module, class_name)
if not module_path:
continue
if len(module_path.split(".")) < 2:
continue
if package_ecosystem == "langchain":
pkg = module_path.split(".")[0].replace("langchain_", "")
top_level_mod = module_path.split(".")[1]
url = (
_LANGCHAIN_API_REFERENCE
+ pkg
+ "/"
+ top_level_mod
+ "/"
+ module_path
+ "."
+ class_name
+ ".html"
)
elif package_ecosystem == "langgraph":
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
# Likely not documented yet
continue
source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
(module, class_name)
]
url = (
_LANGGRAPH_API_REFERENCE
+ namespace
+ "/#"
+ source_module
+ "."
+ class_name
)
else:
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
# Add the import information to our list
imports.append(
{
"imported": class_name,
"source": module,
"docs": url,
"path": path,
}
)
return imports
def update_markdown_with_imports(markdown: str, path: str) -> str:
"""Update markdown to include API reference links for imports in Python code blocks.
This function scans the markdown content for Python code blocks, extracts any
imports, and appends links to their API documentation.
Args:
markdown: The markdown content to process.
path: The path of the file where the markdown content originated.
Returns:
Updated markdown with API reference links prepended to Python code blocks.
Example:
Given a markdown with a Python code block:
```python
from langchain.nlp import TextGenerator
```
This function will append an API reference link to the `TextGenerator` class
from the `langchain.nlp` module if it's recognized.
"""
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```",
re.DOTALL,
)
def replace_code_block(match: re.Match) -> str:
"""Replace the matched code block with additional API reference links if imports are found.
Args:
match (re.Match): The regex match object containing the code block.
Returns:
str: The modified code block with API reference links prepended if applicable.
"""
indent = match.group("indent")
code_block = match.group("code")
# Retrieve import information from the code block
imports = get_imports(code_block, "__unused__")
original_code_block = match.group(0)
# If no imports are found, return the original code block
if not imports:
return original_code_block
# Generate API reference links for each import
api_links = " | ".join(
f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
)
# Return the code block with prepended API reference links
return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
# Apply the replace_code_block function to all matches in the markdown
updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
return updated_markdown
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"""Experimental script to generate consolidated llms text from the docs."""
import asyncio
import glob
import os
import re
from typing import TypedDict, List, Optional
import yaml
from langchain.chat_models import init_chat_model
from langchain_core.rate_limiters import InMemoryRateLimiter
from mkdocs.structure.files import File
from mkdocs.structure.pages import Page
from pydantic import BaseModel, Field
from yaml import SafeLoader
from _scripts.notebook_hooks import _on_page_markdown_with_config
HERE = os.path.dirname(os.path.abspath(__file__))
# Get source directory (parent of HERE / docs)
SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
async def convert_ipynb_to_md(file_path: str) -> Optional[str]:
"""Process a file (markdown or notebook) to markdown format.
Args:
file_path: Path to the file to process
Returns:
Processed markdown content if successful, None otherwise
"""
rel_path = os.path.relpath(file_path, SOURCE_DIR)
# Create File and Page objects to match mkdocs structure
file_obj = File(
path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
)
page = Page(
title="",
file=file_obj,
config={},
)
try:
# Read raw content
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
# Convert to markdown without logic to resolve API references
processed_content = _on_page_markdown_with_config(
content, page, add_api_references=False, remove_base64_images=True
)
# Remove self-closing img tags <img ... />
processed_content = re.sub(r"<img[^>]*/>", "", processed_content)
# Remove img tags with content <img ...>...</img>
processed_content = re.sub(
r"<img[^>]*>.*?</img>", "", processed_content, flags=re.DOTALL
)
return processed_content
except Exception as e:
print(f"Error processing file {file_path}: {e}")
return None
async def generate_full_llms_text(output_file: str) -> None:
"""Generate a consolidated text file from markdown/notebook files for LLM training.
Args:
output_file: Path to output the consolidated text file
"""
# Collect all markdown and notebook files
all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "concepts/*.ipynb"), recursive=True)
)
all_content = []
# Process files concurrently
tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
results = await asyncio.gather(*tasks)
# Combine results with file paths
for file_path, processed_content in zip(all_files, results):
if processed_content:
rel_path = os.path.relpath(file_path, SOURCE_DIR)
# Add file name
all_content.append(f"---\n{rel_path}\n---")
# Add content
all_content.append(processed_content)
# Write consolidated output
with open(output_file, "w", encoding="utf-8") as f:
f.write("\n\n".join(all_content))
def no_op_constructor(*args):
"""No-op"""
SafeLoader.add_multi_constructor(
"tag:yaml.org,2002:python/name",
no_op_constructor,
)
class NavItem(TypedDict):
title: str
url: str
hierarchy: tuple[str, ...]
description: str
def _flatten_nav(
nav: list[dict[str, str | list] | str], path: tuple[str, ...] = ()
) -> list[NavItem]:
flat: List[NavItem] = []
for item in nav:
if isinstance(item, dict):
for title, node in item.items():
new_path = path + (title,)
if isinstance(node, str):
# Leaf page
flat.append(
{
"title": title,
"url": node,
"hierarchy": new_path,
"description": "",
}
)
elif isinstance(node, list):
# Dive in, carrying along the updated path
flat.extend(_flatten_nav(node, new_path))
else:
raise TypeError(
f"Unexpected node type {type(node)} under {title!r}"
)
elif isinstance(item, str):
# Bare string entry → use itself as title, and as URL
new_path = path + (item,)
flat.append(
{"title": item, "url": item, "hierarchy": new_path, "description": ""}
)
else:
raise TypeError(f"Unexpected item type {type(item)} in nav")
return flat
class PageInfo(BaseModel):
title: str = Field(description="The title of the page")
description: str = Field(
description="A short description of the page no longer than 3 sentences "
"explaining the kind of content that can be found in the page."
)
async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
"""Open the contents of each nav item and come up with a better title and description."""
rate_limiter = InMemoryRateLimiter(requests_per_second=10)
model = init_chat_model("gpt-4o-mini", temperature=0.0, rate_limiter=rate_limiter)
model = model.with_structured_output(PageInfo)
async def process_single_item(item: NavItem) -> NavItem:
path = item["url"]
file_path = os.path.join(SOURCE_DIR, path)
# Process the file content (handles both markdown and notebooks)
if path.endswith(".ipynb"):
content = await convert_ipynb_to_md(file_path)
else:
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
if not content:
return item
# Generate a better title and description
response = await model.ainvoke(
[
{
"role": "system",
"content": "You are a technical documentation writer. "
"You are given a markdown page of documentation. "
"Please come up with an appropriate title and "
"description for the page. The description should "
"be a short summary of the page content that is "
"no longer than 3 sentences.",
},
{
"role": "user",
"content": "The markdown page is as follows:\n\n" + content,
},
]
)
return {
"title": response.title,
"url": item["url"],
"hierarchy": item["hierarchy"],
"description": response.description,
}
# Remove any items that start with http:// or https:// looking only for
# local file at this stages.
nav_items = [
item
for item in nav_items
if not item["url"].startswith(("http://", "https://"))
]
# Process items in parallel
tasks = [process_single_item(item) for item in nav_items]
new_nav_items = await asyncio.gather(*tasks)
return new_nav_items
async def generate_nav_links_text(
output_file: str, *, replace_links: bool = False
) -> None:
"""Generate llms.txt from mkdocs.yaml."""
# Get path to mkdocs.yaml relative to this script
script_dir = os.path.dirname(os.path.abspath(__file__))
mkdocs_path = os.path.join(os.path.dirname(script_dir), "mkdocs.yml")
# Load and parse yaml
with open(mkdocs_path, "r") as f:
config = yaml.safe_load(f)
# Extract nav section
nav = config.get("nav", [])
flattened = _flatten_nav(nav)
processed_nav = await process_nav_items(flattened)
with open(output_file, "w") as f:
current_section = None
for item in processed_nav:
# Get the top-level section (first item in hierarchy)
section = item["hierarchy"][0]
if section not in {"Guides", "Examples", "Resources"}:
continue
# If we're starting a new section, add a heading
if section != current_section:
f.write(f"\n# {section}\n\n")
current_section = section
title = item["title"]
# Process URL based on replace_links flag
url = item["url"]
if replace_links:
# Remove .md extension and ensure single trailing slash
url = url.removesuffix(".md")
url = url.removesuffix(".ipynb")
url = url.rstrip("/") + "/"
url = f"https://langchain-ai.github.io/langgraph/{url}"
f.write(f"- [{title}]({url}): {item['description']}\n")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description=(
"Generate consolidated text file from markdown/notebook files for LLMs."
)
)
parser.add_argument("output_file", help="Path to output the consolidated text file")
parser.add_argument(
"--link-only",
action="store_true",
help="Only include link references in the output",
)
parser.add_argument(
"--replace-links",
action="store_true",
help="Replace markdown links with full URLs in the output",
)
args = parser.parse_args()
if args.link_only:
coro = generate_nav_links_text(
args.output_file, replace_links=args.replace_links
)
else:
coro = generate_full_llms_text(args.output_file)
asyncio.run(coro)
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JS_LINK_MAP = {
"langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
"create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
"langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
}
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import ast
import os
import re
from typing import Literal
import nbformat
from nbconvert.exporters import MarkdownExporter
from nbconvert.preprocessors import Preprocessor
def _uses_input(source: str) -> bool:
"""Parse the source code to determine if it uses the input() function."""
try:
tree = ast.parse(source)
except SyntaxError:
# If there's a syntax error, assume input() might be present to be safe.
return False
for node in ast.walk(tree):
if isinstance(node, ast.Call):
# Check if the function called is named 'input'
if isinstance(node.func, ast.Name) and node.func.id == "input":
return True
return False
def _rewrite_cell_magic(code: str) -> str:
"""Process a code block that uses cell magic.
- Lines starting with "%%capture" are ignored.
- Lines starting with "%pip" are rewritten by removing the leading "%" character.
- Any other non-empty line causes a NotImplementedError.
Args:
code (str): The original code block.
Returns:
str: The transformed code block.
Raises:
NotImplementedError: If a line doesn't start with either "%%capture" or "%pip".
"""
rewritten_lines = []
for line in code.splitlines():
stripped = line.strip()
# Skip empty lines
if not stripped:
continue
# Ignore %%capture lines
if stripped.startswith("%%capture"):
continue
# Rewrite %pip lines by dropping the '%'
elif stripped.startswith("%") or stripped.startswith("!"):
# Drop the leading '%' character and then drop all leading whitespace
stripped = stripped.lstrip("%! \t")
# Check if the line starts with "pip"
if stripped.startswith("pip"):
rewritten_lines.append(stripped)
else:
raise NotImplementedError(f"Unhandled line: {line}")
else:
raise NotImplementedError(f"Unhandled line: {line}")
return "\n".join(rewritten_lines)
class PrintCallVisitor(ast.NodeVisitor):
"""
This visitor sets self.has_print to True if it encounters a call
to a print within the global scope.
This should catch calls to print(), print_stream(), etc. (Prefixed with "print").
May have some false positives, but it's not meant to be perfect.
Temporary code for notebook conversion.
"""
def __init__(self):
self.has_print = False
self.scope_level = 0 # counter to track whether we're inside a def/lambda
def visit_FunctionDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_AsyncFunctionDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_Lambda(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_ClassDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_Call(self, node):
# Only consider calls when not inside a function definition.
if self.scope_level == 0:
if isinstance(node.func, ast.Name) and node.func.id.startswith("print"):
self.has_print = True
self.generic_visit(node)
def _has_output(source: str) -> bool:
"""Determine if the code block is expected to produce output.
Args:
source (str): The source code of the code block.
Returns:
True if the code block is expected to produce output, False otherwise.
Must meet the following conditions:
1. There is a call to a printing function (name starts with "print")
that is not inside a function definition.
2. The last top-level statement is an expression that is valid if:
- It is any expression (including calls) AND
- It is NOT a call to `display(...)`.
`display` isn't handled currently by markdown-exec
"""
try:
tree = ast.parse(source)
except SyntaxError:
return False
# Condition (1): Check for a global print-like call.
visitor = PrintCallVisitor()
visitor.visit(tree)
condition_a = visitor.has_print
# Condition (2): Check the last top-level statement.
condition_b = False
if tree.body:
last_stmt = tree.body[-1]
if isinstance(last_stmt, ast.Expr):
# If the expression is a call, ensure it's not a call to "display"
if isinstance(last_stmt.value, ast.Call):
if (
isinstance(last_stmt.value.func, ast.Name)
and last_stmt.value.func.id == "display"
):
condition_b = False # exclude display-wrapped expressions
else:
condition_b = True
else:
# Any other expression qualifies.
condition_b = True
return condition_a or condition_b
def _convert_links_in_markdown(markdown: str) -> str:
"""Convert links present in notebook markdown cells to standardized format.
We want to update markdown links code cells by linking to markdown
files rather than assuming that the link is to the finalized HTML.
This code is needed temporarily since the markdown links that are present
in ipython notebooks do not follow the same conventions as regular markdown
files in mkdocs (which should link to a .md file).
"""
# Define the regex pattern in parts for clarity:
pattern = (
r"(?<!!)" # Negative lookbehind: ensure the link is not an image (i.e., doesn't start with "!")
r"\[" # Literal '[' indicating the start of the link text.
r"(?P<text>[^\]]*)" # Named group 'text': match any characters except ']', representing the link text.
r"\]" # Literal ']' indicating the end of the link text.
r"\(" # Literal '(' indicating the start of the URL.
r"(?![^\)]*//)" # Negative lookahead: ensure that the URL does not contain '//' (skip absolute URLs).
r"(?P<url>[^)]*)" # Named group 'url': match any characters except ')', representing the URL.
r"\)" # Literal ')' indicating the end of the URL.
)
def custom_replacement(match):
"""logic will correct the link format used in ipython notebooks
Ipython notebooks were being converted directly into HTML links
instead of markdown links that retain the markdown extension.
It needs to handle the following cases:
- optional fragments (e.g., `#section`)
e.g., `[text](url/#section)` -> `[text](url.md#section)`
e.g., `[text](url#section)` -> `[text](url.md#section)`
- relative paths (e.g., `../path/to/file`) need to be denested by 1 level
"""
text = match.group("text")
url = match.group("url")
if url.startswith("../"):
# we strip the "../" from the start of the URL
# We only need to denest one level.
url = url[3:]
url = url.rstrip("/") # Strip `/` from the end of the URL
# if url has a fragment
if "#" in url:
url, fragment = url.split("#")
url = url.rstrip("/")
# Strip `/` from the end of the URL
return f"[{text}]({url}.md#{fragment})"
# Otherwise add the .md extension
return f"[{text}]({url}.md)"
return re.sub(
pattern,
custom_replacement,
markdown,
)
class HideCellTagPreprocessor(Preprocessor):
"""
Removes cells that have '# hide-cell' at the beginning of the cell content.
This allows authors to include cells in the notebook that should not
appear in the generated markdown output.
"""
def preprocess(self, nb, resources):
# Filter out cells with the '# hide-cell' comment at the beginning
nb.cells = [
cell
for cell in nb.cells
if not (cell.source.strip().startswith("# hide-cell"))
]
return nb, resources
class EscapePreprocessor(Preprocessor):
def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
super().__init__(**kwargs)
self.markdown_exec_migration = markdown_exec_migration
def preprocess_cell(self, cell, resources, cell_index):
if cell.cell_type == "markdown":
if not self.markdown_exec_migration:
# Old logic is to convert ipynb links to HTML links
cell.source = re.sub(
r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
r'<a href="\2">\1</a>',
cell.source,
)
else:
cell.source = _convert_links_in_markdown(cell.source)
# Fix image paths in <img> tags
cell.source = re.sub(
r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
)
elif cell.cell_type == "code":
# Determine if the cell has bash or cell magic
source = cell.source
is_exec = not (
source.startswith("%") or source.startswith("!") or _uses_input(source)
)
cell.metadata["exec"] = is_exec
# For markdown exec migration we'll re-write cell magic as bash commands
if source.startswith("%%"):
cell.source = _rewrite_cell_magic(source)
cell.metadata["language"] = "shell"
# Remove noqa comments
cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
# escape ``` in code
# This is needed because the markdown exporter will wrap code blocks in
# triple backticks, which will break the markdown output if the code block
# contains triple backticks.
cell.source = cell.source.replace("```", r"\`\`\`")
# escape ``` in output
if "outputs" in cell:
filter_out = set()
for i, output in enumerate(cell["outputs"]):
if "text" in output:
if not output["text"].strip():
filter_out.add(i)
continue
value = output["text"].replace("```", r"\`\`\`")
# handle a funky case w/ references in text
value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
output["text"] = value
elif "data" in output:
for key, value in output["data"].items():
if isinstance(value, str):
value = value.replace("```", r"\`\`\`")
# handle a funky case w/ references in text
output["data"][key] = re.sub(
r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
)
cell["outputs"] = [
output
for i, output in enumerate(cell["outputs"])
if i not in filter_out
]
return cell, resources
class ExtractAttachmentsPreprocessor(Preprocessor):
"""
Extracts all of the outputs from the notebook file. The extracted
outputs are returned in the 'resources' dictionary.
"""
def preprocess_cell(self, cell, resources, cell_index):
"""
Apply a transformation on each cell,
Parameters
----------
cell : NotebookNode cell
Notebook cell being processed
resources : dictionary
Additional resources used in the conversion process. Allows
preprocessors to pass variables into the Jinja engine.
cell_index : int
Index of the cell being processed (see base.py)
"""
# Get files directory if it has been specified
# Make sure outputs key exists
if not isinstance(resources["outputs"], dict):
resources["outputs"] = {}
# Loop through all of the attachments in the cell
for name, attach in cell.get("attachments", {}).items():
for mime, data in attach.items():
if mime not in {
"image/png",
"image/jpeg",
"image/svg+xml",
"application/pdf",
}:
continue
# attachments are pre-rendered. Only replace markdown-formatted
# images with the following logic
attach_str = f"({name})"
if attach_str in cell.source:
data = f"(data:{mime};base64,{data})"
cell.source = cell.source.replace(attach_str, data)
return cell, resources
exporter = MarkdownExporter(
preprocessors=[
HideCellTagPreprocessor,
EscapePreprocessor,
ExtractAttachmentsPreprocessor,
],
template_name="mdoutput",
extra_template_basedirs=[
os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
],
)
def convert_notebook(
notebook_path: str,
mode: Literal["markdown", "exec"] = "markdown",
) -> str:
with open(notebook_path) as f:
nb = nbformat.read(f, as_version=4)
nb.metadata.mode = mode
body, _ = exporter.from_notebook_node(nb)
return body
@@ -0,0 +1,5 @@
{
"mimetypes": {
"text/markdown": true
}
}
@@ -0,0 +1,50 @@
{% extends 'markdown/index.md.j2' %}
{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
```{%- if 'language' in cell.metadata -%}
{{ cell.metadata.language }}
{%- elif 'magics_language' in cell.metadata -%}
{{ cell.metadata.magics_language }}
{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
{{ nb.metadata.language_info.name }}
{%- endif %}
{{ cell.source }}
```
{% endblock input %}
{%- block traceback_line -%}
```output
{{ line.rstrip() | strip_ansi }}
```
{%- endblock traceback_line -%}
{%- block stream -%}
```output
{{ output.text.rstrip() | strip_ansi }}
```
{%- endblock stream -%}
{%- block data_text scoped -%}
```output
{{ output.data['text/plain'].rstrip() | strip_ansi }}
```
{%- endblock data_text -%}
{%- block data_html scoped -%}
```html
{{ output.data['text/html'] | safe }}
```
{%- endblock data_html -%}
{%- block data_jpg scoped -%}
<p>
<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
</p>
{%- endblock data_jpg -%}
{%- block data_png scoped -%}
<p>
<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
</p>
{%- endblock data_png -%}
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"""mkdocs hooks for adding custom logic to documentation pipeline.
Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
"""
import json
import logging
import os
import posixpath
import re
from typing import Any, Dict
from bs4 import BeautifulSoup
from mkdocs.config.defaults import MkDocsConfig
from mkdocs.structure.files import Files, File
from mkdocs.structure.pages import Page
from _scripts.generate_api_reference_links import update_markdown_with_imports
from _scripts.link_map import JS_LINK_MAP
from _scripts.notebook_convert import convert_notebook
logger = logging.getLogger(__name__)
logging.basicConfig()
logger.setLevel(logging.INFO)
DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
REDIRECT_MAP = {
# lib redirects
"how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
"how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
"how-tos/streaming-content.ipynb": "how-tos/streaming.md",
"how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
"how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
"how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
"how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
# graph-api
"how-tos/state-reducers.ipynb": "how-tos/graph-api.md#define-and-update-state",
"how-tos/sequence.ipynb": "how-tos/graph-api.md#create-a-sequence-of-steps",
"how-tos/branching.ipynb": "how-tos/graph-api.md#create-branches",
"how-tos/recursion-limit.ipynb": "how-tos/graph-api.md#create-and-control-loops",
"how-tos/visualization.ipynb": "how-tos/graph-api.md#visualize-your-graph",
"how-tos/input_output_schema.ipynb": "how-tos/graph-api.md#define-input-and-output-schemas",
"how-tos/pass_private_state.ipynb": "how-tos/graph-api.md#pass-private-state-between-nodes",
"how-tos/state-model.ipynb": "how-tos/graph-api.md#use-pydantic-models-for-graph-state",
"how-tos/map-reduce.ipynb": "how-tos/graph-api.md#map-reduce-and-the-send-api",
"how-tos/command.ipynb": "how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command",
"how-tos/configuration.ipynb": "how-tos/graph-api.md#add-runtime-configuration",
"how-tos/node-retries.ipynb": "how-tos/graph-api.md#add-retry-policies",
"how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api.md#impose-a-recursion-limit",
"how-tos/async.ipynb": "how-tos/graph-api.md#async",
# memory how-tos
"how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md",
"how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages",
"how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory/add-memory.md#summarize-messages",
"how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
"agents/memory.ipynb": "how-tos/memory/add-memory.md",
# subgraph how-tos
"how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.md#different-state-schemas",
"how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.md#add-persistence",
# persistence how-tos
"how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production",
"how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production",
"how-tos/persistence_redis.ipynb": "how-tos/memory/add-memory.md#use-in-production",
"how-tos/subgraph-persistence.ipynb": "how-tos/memory/add-memory.md#use-with-subgraphs",
"how-tos/cross-thread-persistence.ipynb": "how-tos/memory/add-memory.md#add-long-term-memory",
"cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
"cloud/how-tos/check-thread-status": "cloud/how-tos/use_threads",
"cloud/concepts/threads.md": "concepts/persistence.md#threads",
"how-tos/persistence.ipynb": "how-tos/memory/add-memory.md",
# tool calling how-tos
"how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
"how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
"how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
"how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
"agents/tools.md": "how-tos/tool-calling.md",
# multi-agent how-tos
"how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs",
"how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system",
"how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.md#multi-turn-conversation",
# cloud redirects
"cloud/index.md": "index.md",
"cloud/how-tos/index.md": "concepts/langgraph_platform",
"cloud/concepts/api.md": "concepts/langgraph_server.md",
"cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
"cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
"cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
"cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
"concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
# cloud streaming redirects
"cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
"cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
"cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
"cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
"cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
"cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
"cloud/concepts/streaming.md": "concepts/streaming.md",
"agents/streaming.md": "how-tos/streaming.md",
# prebuilt redirects
"how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
"how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
"how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
"how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
# misc
"prebuilt.md": "agents/prebuilt.md",
"reference/prebuilt.md": "reference/agents.md",
"concepts/high_level.md": "index.md",
"concepts/index.md": "index.md",
"concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
"how-tos/index.md": "index.md",
"tutorials/introduction.ipynb": "concepts/why-langgraph.md",
"agents/deployment.md": "tutorials/langgraph-platform/local-server.md",
# deployment redirects
"how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
"concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
"tutorials/deployment.md": "concepts/deployment_options.md",
# assistant redirects
"cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
"cloud/concepts/runs.md": "concepts/assistants.md#execution",
# hitl redirects
"how-tos/wait-user-input-functional.ipynb": "how-tos/use-functional-api.md",
"how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
"how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
"agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
"how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
"concepts/breakpoints.md": "concepts/human_in_the_loop.md",
"how-tos/human_in_the_loop/breakpoints.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
"cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
"how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
}
class NotebookFile(File):
def is_documentation_page(self):
return True
def on_files(files: Files, **kwargs: Dict[str, Any]):
if DISABLED:
return files
new_files = Files([])
for file in files:
if file.src_path.endswith(".ipynb"):
new_file = NotebookFile(
path=file.src_path,
src_dir=file.src_dir,
dest_dir=file.dest_dir,
use_directory_urls=file.use_directory_urls,
)
new_files.append(new_file)
else:
new_files.append(file)
return new_files
def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
"""Add the path to the code blocks."""
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
r"(?P<code>((?:.*\n)*?))" # Capture the code inside the block using named group
r"(?P=indent)```" # Match closing backticks with the same indentation
)
def replace_code_block_header(match: re.Match) -> str:
indent = match.group("indent")
language = match.group("language")
attributes = match.group("attributes").rstrip()
if 'exec="on"' not in attributes:
# Return original code block
return match.group(0)
code = match.group("code")
return f'{indent}```{language} {attributes} path="{page.file.src_path}"\n{code}{indent}```'
return code_block_pattern.sub(replace_code_block_header, markdown)
def _resolve_cross_references(md_text: str, link_map: dict[str, str]) -> str:
"""Replace [title][identifier] with [title](url) using language-specific link_map.
Args:
md_text: The markdown text to process.
link_map: mapping of identifier to URL.
Returns:
The processed markdown text with cross-references resolved.
"""
# Pattern to match [title][identifier]
pattern = re.compile(r"\[([^\]]+)\]\[([^\]]+)\]")
def replace_reference(match: re.Match) -> str:
"""Replace the matched reference with the corresponding URL."""
title, identifier = match.group(1), match.group(2)
url = link_map.get(identifier)
if url:
return f"[{title}]({url})"
else:
# Leave it unchanged if not found
return match.group(0)
return pattern.sub(replace_reference, md_text)
def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
if target_language not in {"python", "js"}:
raise ValueError("target_language must be 'python' or 'js'")
pattern = re.compile(
r"(?P<indent>[ \t]*):::(?P<language>\w+)\s*\n"
r"(?P<content>((?:.*\n)*?))" # Capture the content inside the block
r"(?P=indent):::" # Match closing with the same indentation
)
def replace_conditional_blocks(match: re.Match) -> str:
"""Keep active conditionals."""
language = match.group("language")
content = match.group("content")
if language not in {"python", "js"}:
# If the language is not supported, return the original block
return match.group(0)
if language == target_language:
return content
# If the language does not match, return an empty string
return ""
processed = pattern.sub(replace_conditional_blocks, md_text)
return processed
def _highlight_code_blocks(markdown: str) -> str:
"""Find code blocks with highlight comments and add hl_lines attribute.
Args:
markdown: The markdown content to process.
Returns:
updated Markdown code with code blocks containing highlight comments
updated to use the hl_lines attribute.
"""
# Pattern to find code blocks with highlight comments and without
# existing hl_lines for Python and JavaScript
# Pattern to find code blocks with highlight comments, handling optional indentation
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
r"(?P<code>((?:.*\n)*?))" # Capture the code inside the block using named group
r"(?P=indent)```" # Match closing backticks with the same indentation
)
def replace_highlight_comments(match: re.Match) -> str:
indent = match.group("indent")
language = match.group("language")
code_block = match.group("code")
attributes = match.group("attributes").rstrip()
# Account for a case where hl_lines is manually specified
if "hl_lines" in attributes:
# Return original code block
return match.group(0)
lines = code_block.split("\n")
highlighted_lines = []
# Skip initial empty lines
while lines and not lines[0].strip():
lines.pop(0)
lines_to_keep = []
comment_syntax = (
"# highlight-next-line"
if language in ["py", "python"]
else "// highlight-next-line"
)
for line in lines:
if comment_syntax in line:
count = len(lines_to_keep) + 1
highlighted_lines.append(str(count))
else:
lines_to_keep.append(line)
# Reconstruct the new code block
new_code_block = "\n".join(lines_to_keep)
# Construct the full code block that also includes
# the fenced code block syntax.
opening_fence = f"```{language}"
if attributes:
opening_fence += f" {attributes}"
if highlighted_lines:
opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
return (
# The indent and opening fence
f"{indent}{opening_fence}\n"
# The indent and terminating \n is already included in the code block
f"{new_code_block}"
f"{indent}```"
)
# Replace all code blocks in the markdown
markdown = code_block_pattern.sub(replace_highlight_comments, markdown)
return markdown
TARGET_LANGUAGE = os.environ.get("TARGET_LANGUAGE", "python")
if TARGET_LANGUAGE not in {"python", "js"}:
raise ValueError(f"TARGET_LANGUAGE must be 'python' or 'js', got {TARGET_LANGUAGE}")
def _on_page_markdown_with_config(
markdown: str,
page: Page,
*,
add_api_references: bool = True,
remove_base64_images: bool = False,
**kwargs: Any,
) -> str:
if DISABLED:
return markdown
if page.file.src_path.endswith(".ipynb"):
# logger.info("Processing Jupyter notebook: %s", page.file.src_path)
markdown = convert_notebook(page.file.abs_src_path)
# Append API reference links to code blocks
if add_api_references:
markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
# Apply highlight comments to code blocks
markdown = _highlight_code_blocks(markdown)
# Apply conditional rendering for code blocks
markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
if TARGET_LANGUAGE == "js":
markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
elif TARGET_LANGUAGE == "python":
# Via a dedicated plugin
pass
else:
raise ValueError(
f"Unsupported target language: {TARGET_LANGUAGE}. "
"Supported languages are 'python' and 'js'."
)
# Add file path as an attribute to code blocks that are executable.
# This file path is used to associate fixtures with the executable code
# which can be used in CI to test the docs without making network requests.
markdown = _add_path_to_code_blocks(markdown, page)
if remove_base64_images:
# Remove base64 encoded images from markdown
markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
return markdown
def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
finalized_markdown = (
_on_page_markdown_with_config(
markdown,
page,
add_api_references=True,
**kwargs,
)
)
page.meta["original_markdown"] = finalized_markdown
return finalized_markdown
# redirects
HTML_TEMPLATE = """
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting...</title>
<link rel="canonical" href="{url}">
<meta name="robots" content="noindex">
<script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
<meta http-equiv="refresh" content="0; url={url}">
</head>
<body>
Redirecting...
</body>
</html>
"""
def _write_html(site_dir, old_path, new_path):
"""Write an HTML file in the site_dir with a meta redirect to the new page"""
# Determine all relevant paths
old_path_abs = os.path.join(site_dir, old_path)
old_dir_abs = os.path.dirname(old_path_abs)
# Create parent directories if they don't exist
if not os.path.exists(old_dir_abs):
os.makedirs(old_dir_abs)
# Write the HTML redirect file in place of the old file
content = HTML_TEMPLATE.format(url=new_path)
with open(old_path_abs, "w", encoding="utf-8") as f:
f.write(content)
def _inject_gtm(html: str) -> str:
"""Inject Google Tag Manager code into the HTML.
Code to inject Google Tag Manager noscript tag immediately after <body>.
This is done via hooks rather than via a template because the MkDocs material
theme does not seem to allow placing the code immediately after the <body> tag
without modifying the template files directly.
Args:
html: The HTML content to modify.
Returns:
The modified HTML content with GTM code injected.
"""
# Code was copied from Google Tag Manager setup instructions.
gtm_code = """
<!-- Google Tag Manager (noscript) -->
<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T35S4S46"
height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
<!-- End Google Tag Manager (noscript) -->
"""
soup = BeautifulSoup(html, "html.parser")
body = soup.body
if body:
# Insert the GTM code as raw HTML at the top of <body>
body.insert(0, BeautifulSoup(gtm_code, "html.parser"))
return str(soup)
else:
return html # fallback if no <body> found
def _inject_markdown_into_html(html: str, page: Page) -> str:
"""Inject the original markdown content into the HTML page as JSON."""
original_markdown = page.meta.get("original_markdown", "")
if not original_markdown:
return html
markdown_data = {
"markdown": original_markdown,
"title": page.title or "Page Content",
"url": page.url or "",
}
# Properly escape the JSON for HTML
json_content = json.dumps(markdown_data, ensure_ascii=False)
json_content = (
json_content.replace("</", "\\u003c/")
.replace("<script", "\\u003cscript")
.replace("</script", "\\u003c/script")
)
script_content = (
f'<script id="page-markdown-content" '
f'type="application/json">{json_content}</script>'
)
# Insert before </head> if it exists, otherwise before </body>
if "</head>" not in html:
raise ValueError(
"HTML does not contain </head> tag. Cannot inject markdown content."
)
return html.replace("</head>", f"{script_content}</head>")
def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
"""Inject Google Tag Manager noscript tag immediately after <body>.
Args:
html: The HTML output of the page.
page: The page instance.
config: The MkDocs configuration object.
Returns:
modified HTML output with GTM code injected.
"""
html = _inject_markdown_into_html(html, page)
return _inject_gtm(html)
# Create HTML files for redirects after site dir has been built
def on_post_build(config):
use_directory_urls = config.get("use_directory_urls")
for page_old, page_new in REDIRECT_MAP.items():
page_old = page_old.replace(".ipynb", ".md")
page_new = page_new.replace(".ipynb", ".md")
page_new_before_hash, hash, suffix = page_new.partition("#")
old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
os.sep, "/"
)
new_html_path = File(page_new_before_hash, "", "", True).url
new_html_path = (
posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
+ hash
+ suffix
)
_write_html(config["site_dir"], old_html_path, new_html_path)
+263
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@@ -0,0 +1,263 @@
"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
import logging
import os
import json
import click
import nbformat
import re
logger = logging.getLogger(__name__)
NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
BLOCKLIST_COMMANDS = (
# skip if has WebBaseLoader to avoid caching web pages
"WebBaseLoader",
# skip if has draw_mermaid_png to avoid generating mermaid images via API
"draw_mermaid_png",
)
NOTEBOOKS_NO_CASSETTES = (
"docs/how-tos/visualization.ipynb",
)
NOTEBOOKS_NO_EXECUTION = [
# this uses a user provided project name for langsmith
"docs/tutorials/tnt-llm/tnt-llm.ipynb",
# this uses langsmith datasets
"docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
# this uses browser APIs
"docs/tutorials/web-navigation/web_voyager.ipynb",
# these RAG guides use an ollama model
"docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
"docs/tutorials/rag/langgraph_crag_local.ipynb",
"docs/tutorials/rag/langgraph_self_rag_local.ipynb",
# this loads a massive dataset from gcp
"docs/tutorials/usaco/usaco.ipynb",
# TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
"docs/how-tos/autogen-integration.ipynb",
"docs/how-tos/autogen-integration-functional.ipynb",
# TODO: need to update these notebooks to make sure they are runnable in CI
"docs/tutorials/storm/storm.ipynb", # issues only when running with VCR
"docs/tutorials/lats/lats.ipynb", # issues only when running with VCR
"docs/tutorials/rag/langgraph_crag.ipynb", # flakiness from tavily
"docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # flakiness only when running in GHA
"docs/tutorials/rag/langgraph_self_rag.ipynb", # flakiness only when running in GHA
"docs/tutorials/rag/langgraph_agentic_rag.ipynb", # flakiness only when running in GHA
"docs/how-tos/map-reduce.ipynb", # flakiness from structured output, only when running with VCR
"docs/tutorials/tot/tot.ipynb",
"docs/how-tos/visualization.ipynb",
"docs/how-tos/streaming-specific-nodes.ipynb",
"docs/tutorials/llm-compiler/LLMCompiler.ipynb",
"docs/tutorials/customer-support/customer-support.ipynb", # relies on openai embeddings, doesn't play well w/ VCR
"docs/how-tos/many-tools.ipynb", # relies on openai embeddings, doesn't play well w/ VCR
]
def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
for cell in notebook.cells:
if cell.cell_type != "code":
continue
if "pip install" in cell.source:
# Comment out the lines in cells containing "pip install"
cell.source = "\n".join(
f"# {line}" if line.strip() else line
for line in cell.source.splitlines()
)
return notebook
def is_magic_command(code: str) -> bool:
return code.strip().startswith("%") or code.strip().startswith("!")
def is_comment(code: str) -> bool:
return code.strip().startswith("#")
def has_blocklisted_command(code: str, metadata: dict) -> bool:
if 'hide_from_vcr' in metadata:
return True
code = code.strip()
for blocklisted_pattern in BLOCKLIST_COMMANDS:
if blocklisted_pattern in code:
return True
return False
MERMAID_PATTERN = re.compile(r'display\(Image\((\w+)\.get_graph\(\)\.draw_mermaid_png\(\)\)\)')
def remove_mermaid(code: str) -> str:
return MERMAID_PATTERN.sub('print()', code)
def add_vcr_to_notebook(
notebook: nbformat.NotebookNode, cassette_prefix: str
) -> nbformat.NotebookNode:
"""Inject `with vcr.cassette` into each code cell of the notebook."""
uses_langsmith = False
# Inject VCR context manager into each code cell
for idx, cell in enumerate(notebook.cells):
if cell.cell_type != "code":
continue
lines = cell.source.splitlines()
# remove the special tag for hidden cells
lines = [line for line in lines if not line.strip().startswith("# hide-cell")]
# skip if empty cell
if not lines:
continue
are_magic_lines = [is_magic_command(line) for line in lines]
# skip if all magic
if all(are_magic_lines):
continue
if any(are_magic_lines):
raise ValueError(
"Cannot process code cells with mixed magic and non-magic code."
)
# skip if just comments
if all(is_comment(line) or not line.strip() for line in lines):
continue
if has_blocklisted_command(cell.source, cell.metadata):
continue
cell_id = cell.get("id", idx)
cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
f" {line}" for line in lines
)
if any("hub.pull" in line or "from langsmith import" in line for line in lines):
uses_langsmith = True
# Add import statement
vcr_import_lines = []
if uses_langsmith:
vcr_import_lines.extend([
# patch urllib3 to handle vcr errors, see more here:
# https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
"import sys",
f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
"import _patch as patch_urllib3",
"patch_urllib3.patch_urllib3()",
])
vcr_import_lines.extend([
"import nest_asyncio",
"nest_asyncio.apply()",
"import vcr",
"import msgpack",
"import base64",
"import zlib",
"import os",
"os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
"custom_vcr = vcr.VCR()",
"",
"def compress_data(data, compression_level=9):",
" packed = msgpack.packb(data, use_bin_type=True)",
" compressed = zlib.compress(packed, level=compression_level)",
" return base64.b64encode(compressed).decode('utf-8')",
"",
"def decompress_data(compressed_string):",
" decoded = base64.b64decode(compressed_string)",
" decompressed = zlib.decompress(decoded)",
" return msgpack.unpackb(decompressed, raw=False)",
"",
"class AdvancedCompressedSerializer:",
" def serialize(self, cassette_dict):",
" return compress_data(cassette_dict)",
"",
" def deserialize(self, cassette_string):",
" return decompress_data(cassette_string)",
"",
"custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
"custom_vcr.serializer = 'advanced_compressed'",
])
import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
import_cell.pop("id", None)
notebook.cells.insert(0, import_cell)
return notebook
def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
for cell in notebook.cells:
if cell.cell_type != "code":
continue
cell.source = remove_mermaid(cell.source)
# skip the cell entirely if it contains PYPPETEER
if "PYPPETEER" in cell.source:
cell.source = ""
return notebook
def process_notebooks(should_comment_install_cells: bool) -> None:
for directory in NOTEBOOK_DIRS:
for root, _, files in os.walk(directory):
for file in files:
if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
continue
notebook_path = os.path.join(root, file)
try:
notebook = nbformat.read(notebook_path, as_version=4)
if should_comment_install_cells:
notebook = comment_install_cells(notebook)
base_filename = os.path.splitext(os.path.basename(file))[0]
cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
if notebook_path not in NOTEBOOKS_NO_CASSETTES:
notebook = add_vcr_to_notebook(
notebook, cassette_prefix=cassette_prefix
)
notebook = remove_mermaid_from_notebook(notebook)
if notebook_path in NOTEBOOKS_NO_EXECUTION:
# Add a cell at the beginning to indicate that this notebook should not be executed
warning_cell = nbformat.v4.new_markdown_cell(
source="**Warning:** This notebook is not meant to be executed automatically."
)
notebook.cells.insert(0, warning_cell)
# Add a special tag to the first code cell
if notebook.cells and notebook.cells[1].cell_type == "code":
notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
nbformat.write(notebook, notebook_path)
logger.info(f"Processed: {notebook_path}")
except Exception as e:
logger.error(f"Error processing {notebook_path}: {e}")
with open("notebooks_no_execution.json", "w") as f:
json.dump(NOTEBOOKS_NO_EXECUTION, f)
@click.command()
@click.option(
"--comment-install-cells",
is_flag=True,
default=False,
help="Whether to comment out install cells",
)
def main(comment_install_cells):
process_notebooks(should_comment_install_cells=comment_install_cells)
logger.info("All notebooks processed successfully.")
if __name__ == "__main__":
main()
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#!/usr/bin/env python
"""Create the third party page for the documentation."""
import argparse
from typing import List
from typing import TypedDict
import yaml
MARKDOWN = """\
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
# Community Agents
If youre looking for other prebuilt libraries, explore the community-built options
below. These libraries can extend LangGraph's functionality in various ways.
## 📚 Available Libraries
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
{library_list}
## ✨ Contributing Your Library
Have you built an awesome open-source library using LangGraph? We'd love to feature
your project on the official LangGraph documentation pages! 🏆
To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
**Guidelines**
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
for JavaScript/TypeScript, etc.) 📦
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
the Functional API (exposing an `entrypoint`).
- The package must include documentation (e.g., a `README.md` or docs site)
explaining how to use it.
We'll review your contribution and merge it in!
Thanks for contributing! 🚀
"""
class ResolvedPackage(TypedDict):
name: str
"""The name of the package."""
repo: str
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
weekly_downloads: int | None
"""The weekly download count of the package."""
description: str
"""A brief description of what the package does."""
def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
"""Generate the markdown content for the third party page.
Args:
resolved_packages: A list of resolved package information.
language: str
Returns:
The markdown content as a string.
"""
# Update the URL to the actual file once the initial version is merged
if language == "python":
langgraph_url = (
"https://github.com/langchain-ai/langgraph/blob/main/docs"
"/_scripts/third_party_page/packages.yml"
)
elif language == "js":
langgraph_url = (
"https://github.com/langchain-ai/langgraphjs/blob/main/docs"
"/_scripts/third_party/packages.yml"
)
else:
raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
sorted_packages = sorted(
resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
)
rows = [
"| Name | GitHub URL | Description | Weekly Downloads | Stars |",
"| --- | --- | --- | --- | --- |",
]
for package in sorted_packages:
name = f"**{package['name']}**"
repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
stars_badge = (
f"https://img.shields.io/github/stars/{package['repo']}?style=social"
)
stars = f"![GitHub stars]({stars_badge})"
downloads = package["weekly_downloads"] or "-"
row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
rows.append(row)
markdown_content = MARKDOWN.format(
library_list="\n".join(rows), langgraph_url=langgraph_url
)
return markdown_content
def main(input_file: str, output_file: str, language: str) -> None:
"""Main function to create the third party page.
Args:
input_file: Path to the input YAML file containing resolved package information.
output_file: Path to the output file for the third party page.
language: The language for which to generate the third party page.
"""
# Parse the input YAML file
with open(input_file, "r") as f:
resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
markdown_content = generate_markdown(resolved_packages, language)
# Write the markdown content to the output file
with open(output_file, "w", encoding="utf-8") as f:
f.write(markdown_content)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Create the third party page.")
parser.add_argument(
"input_file",
help="Path to the input YAML file containing resolved package information.",
)
parser.add_argument(
"output_file", help="Path to the output file for the third party page."
)
parser.add_argument(
"--language",
choices=["python", "js"],
default="python",
help="The language for which to generate the third party page. Defaults to 'python'.",
)
args = parser.parse_args()
main(args.input_file, args.output_file, args.language)
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#!/usr/bin/env python
"""Retrieve download count for a list of Python packages from PyPI."""
import argparse
from datetime import datetime
from typing import TypedDict
import pathlib
import requests
import yaml
class Package(TypedDict):
"""A TypedDict representing a package"""
name: str
"""The name of the package."""
repo: str
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
description: str
"""A brief description of what the package does."""
class ResolvedPackage(Package):
weekly_downloads: int | None
HERE = pathlib.Path(__file__).parent
PACKAGES_FILE = HERE / "packages.yml"
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
"""Retrieve the monthly download count for a list of packages from PyPIStats."""
resolved_packages: list[ResolvedPackage] = []
if fake:
# To avoid making network requests during testing, return fake download counts
for package in packages:
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"weekly_downloads": -12345,
"description": package["description"],
}
)
return resolved_packages
for package in packages:
# First check if package exists on PyPI
pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
try:
pypi_response = requests.get(pypi_url)
pypi_response.raise_for_status()
except requests.exceptions.HTTPError:
raise AssertionError(f"Package {package['name']} does not exist on PyPI")
# Get first release date
pypi_data = pypi_response.json()
releases = pypi_data["releases"]
first_release_date = None
for version_releases in releases.values():
if version_releases: # Some versions may be empty lists
upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
if first_release_date is None or upload_time < first_release_date:
first_release_date = upload_time
if first_release_date is None:
raise AssertionError(f"Package {package['name']} has no releases yet")
# If package was published in last 48 hours, skip download stats
if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
url = f"https://pypistats.org/api/packages/{package['name']}/overall"
response = requests.get(url)
response.raise_for_status()
data = response.json()
sorted_data = sorted(
data["data"],
key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
reverse=True,
)
# Sum the last 7 days of downloads
num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
else:
num_downloads = None
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"weekly_downloads": num_downloads,
"description": package["description"],
}
)
return resolved_packages
def main(output_file: str, fake: bool) -> None:
"""Main function to generate package download information.
Args:
output_file: Path to the output YAML file.
"""
resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
if not output_file.endswith(".yml"):
raise ValueError("Output file must have a .yml extension")
with open(output_file, "w") as f:
f.write("# This file is auto-generated. Do not edit.\n")
yaml.dump(resolved_packages, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Generate package download information."
)
parser.add_argument(
"output_file",
help=(
"Path to the output YAML file. Example: python generate_downloads.py "
"downloads.yml"
),
)
parser.add_argument(
"--fake",
default=False,
action="store_true",
help=(
"Generate fake download counts for testing purposes. "
"This option will not make any network requests."
),
)
args = parser.parse_args()
main(args.output_file, args.fake)
@@ -0,0 +1,41 @@
#A list of third-party packages to surface on the third-party page.
packages:
- name: "trustcall"
repo: "hinthornw/trustcall"
description: "Tenacious tool calling built on LangGraph."
- name: "breeze-agent"
repo: "andrestorres123/breeze-agent"
description: "A streamlined research system built inspired on STORM and built on LangGraph."
- name: "langgraph-supervisor"
repo: "langchain-ai/langgraph-supervisor-py"
description: "Build supervisor multi-agent systems with LangGraph."
- name: "langmem"
repo: "langchain-ai/langmem"
description: "Build agents that learn and adapt from interactions over time."
- name: "langchain-mcp-adapters"
repo: "langchain-ai/langchain-mcp-adapters"
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
- name: "open-deep-research"
repo: "langchain-ai/open_deep_research"
description: "Open source assistant for iterative web research and report writing."
- name: "langgraph-swarm"
repo: "langchain-ai/langgraph-swarm-py"
description: "Build swarm-style multi-agent systems using LangGraph."
- name: "delve-taxonomy-generator"
repo: "andrestorres123/delve"
description: "A taxonomy generator for unstructured data"
- name: "nodeology"
repo: "xyin-anl/Nodeology"
description: "Enable researcher to build scientific workflows easily with simplified interface."
- name: "langgraph-bigtool"
repo: "langchain-ai/langgraph-bigtool"
description: "Build LangGraph agents with large numbers of tools."
- name: "ai-data-science-team"
repo: "business-science/ai-data-science-team"
description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
- name: "langgraph-reflection"
repo: "langchain-ai/langgraph-reflection"
description: "LangGraph agent that runs a reflection step."
- name: "langgraph-codeact"
repo: "langchain-ai/langgraph-codeact"
description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
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@@ -0,0 +1 @@
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