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Sydney Runkle 4de4abbe29 comments 2026-01-21 15:37:53 -05:00
Sydney Runkle 834fa8932f async 2026-01-21 12:43:32 -05:00
Sydney Runkle 9458700fe9 lint 2026-01-21 09:08:21 -05:00
Sydney Runkle 122a63fc83 simplify injected args 2026-01-21 09:07:05 -05:00
Sydney Runkle d05eac67f8 simplify 2026-01-21 09:02:24 -05:00
Sydney Runkle 31b5a9c4fe dynamic tools 2026-01-21 08:41:30 -05:00
460 changed files with 16171 additions and 100774 deletions
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# Contributing to LangGraph
Hi there! Thank you for even being interested in contributing to LangGraph.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).
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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.
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).
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/).
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:
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),
* [LangChain Forum](https://forum.langchain.com/),
* [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
* [LangChain documentation with the integrated search](https://docs.langchain.com/),
* [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 +46,17 @@ 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 +77,7 @@ 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
- name: Documentation
url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
about: Report an issue related to the LangGraph documentation
- name: LangChain Forum
url: https://forum.langchain.com/
about: General community discussions and support
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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.
- PRs should not touch more than one package unless absolutely necessary.
- Do not update the `uv.lock` files or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.
## Social handles (optional)
<!-- If you'd like a shoutout on release, add your socials below -->
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LinkedIn: https://linkedin.com/in/
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
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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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# 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

+2 -2
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@@ -63,7 +63,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
try:
sys.stderr.write("\n== docker compose ps ==\n")
runner.run(
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
)
except Exception:
pass
@@ -76,7 +76,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
"logs",
"langgraph-api",
input=stdin,
verbose=True,
verbose=False,
)
)
except Exception:
+25 -75
View File
@@ -2,9 +2,6 @@ name: CLI integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
permissions:
contents: read
@@ -31,8 +28,6 @@ jobs:
workdir: libs/cli/examples/graphs_reqs_b
tag: langgraph-test-d
name: "CLI integration test"
env:
HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
defaults:
run:
working-directory: libs/cli
@@ -40,89 +35,74 @@ jobs:
- uses: actions/checkout@v6
- 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@v7
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: 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 }}
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' }}
- name: Build and test service ${{ matrix.example.name }}
if: steps.changed-files.outputs.all
working-directory: ${{ matrix.example.workdir }}
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
# Build the image for this example
langgraph build -t ${{ matrix.example.tag }}
# Prepare environment file from local or parent example directory
if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi; fi
# Run the integration test using the built tag
# Compute repo root to reference the shared script robustly
REPO_ROOT=$(git rev-parse --show-toplevel)
timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
- 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 && matrix.example.name == 'A' }}
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' }}
if: ${{ steps.changed-files.outputs.all && 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' }}
if: ${{ steps.changed-files.outputs.all && 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
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
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' }}
- name: Build and test prerelease reqs service
if: ${{ steps.changed-files.outputs.all && 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
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
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; $LANGGRAPH_VERSION"
if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
exit 1
fi
LANGCHAIN_OPENAI_VERSION=$(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; $LANGCHAIN_OPENAI_VERSION"
if [ "$LANGCHAIN_OPENAI_VERSION" != "1.0.1" ]; then
echo "LANGCHAIN_OPENAI_VERSION != 1.0.1; $LANGCHAIN_OPENAI_VERSION"
exit 1
fi
LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")
@@ -132,37 +112,7 @@ jobs:
fi
- name: Build and test prerelease reqs fail service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: ${{ steps.changed-files.outputs.all && 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
+28 -9
View File
@@ -34,25 +34,34 @@ jobs:
- uses: actions/checkout@v6
- 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@v7
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
- name: Get .mypy_cache to speed up mypy
if: steps.changed-files.outputs.all
uses: actions/cache@v5
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
path: |
${{ inputs.working-directory }}/.mypy_cache
key: mypy-lint-${{ 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
@@ -63,12 +72,22 @@ 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
- name: Get .mypy_cache_test to speed up mypy
if: steps.changed-files.outputs.all
uses: actions/cache@v5
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
path: |
${{ inputs.working-directory }}/.mypy_cache_test
key: mypy-test-${{ 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
@@ -1,85 +0,0 @@
name: sdk-py integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
DOCKERHUB_USERNAME:
required: false
DOCKERHUB_RO_TOKEN:
required: false
permissions:
contents: read
jobs:
build:
runs-on: ubuntu-latest
name: "sdk-py integration"
defaults:
run:
working-directory: libs/sdk-py
env:
HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
steps:
- uses: actions/checkout@v6
- name: Set up Python
uses: ./.github/actions/uv_setup
with:
python-version: "3.13"
cache-suffix: sdk-py-integration
working-directory: libs/sdk-py
- name: Login to Docker Hub
uses: docker/login-action@650006c6eb7dba73a995cc03b0b2d7f5ca915bee # v4
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
- name: Install dependencies
shell: bash
run: uv sync --frozen --group test --no-dev
- name: Skip if LANGSMITH_API_KEY is not available
if: env.HAS_LANGSMITH_API_KEY != 'true'
run: |
echo "LANGSMITH_API_KEY is not set (likely a fork PR). Skipping integration tests."
exit 0
- name: Bring up integration stack
if: env.HAS_LANGSMITH_API_KEY == 'true'
working-directory: libs/sdk-py/integration
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: docker compose up -d --build
- name: Wait for API healthcheck
if: env.HAS_LANGSMITH_API_KEY == 'true'
run: |
for i in $(seq 1 60); do
if curl -sf http://localhost:2024/ok >/dev/null; then
echo "API ready after ${i}s"
exit 0
fi
sleep 2
done
echo "API failed to become healthy within 120s"
docker compose -f libs/sdk-py/integration/docker-compose.yml logs api | tail -100
exit 1
- name: Run integration suite
if: env.HAS_LANGSMITH_API_KEY == 'true'
run: uv run pytest tests/integration/ -m integration
- name: Dump api logs on failure
if: failure() && env.HAS_LANGSMITH_API_KEY == 'true'
working-directory: libs/sdk-py/integration
run: docker compose logs api | tail -200
- name: Tear down stack
if: always() && env.HAS_LANGSMITH_API_KEY == 'true'
working-directory: libs/sdk-py/integration
run: docker compose down -v
+3 -3
View File
@@ -27,13 +27,13 @@ jobs:
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
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@650006c6eb7dba73a995cc03b0b2d7f5ca915bee # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
+3 -10
View File
@@ -25,13 +25,13 @@ jobs:
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
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@650006c6eb7dba73a995cc03b0b2d7f5ca915bee # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -45,13 +45,6 @@ jobs:
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: |
+6 -6
View File
@@ -25,12 +25,12 @@ jobs:
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ env.PYTHON_VERSION }}
uses: ./.github/actions/uv_setup
- name: Set up Python $${ env.PYTHON_VERSION }}
uses: astral-sh/setup-uv@v7
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 +48,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
@@ -76,13 +76,13 @@ jobs:
steps:
- uses: actions/checkout@v6
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v7
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: ${{ inputs.working-directory }}/dist/
verbose: true
+2 -2
View File
@@ -20,11 +20,11 @@ jobs:
- uses: actions/checkout@v6
- run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
working-directory: libs/langgraph
- name: Install dependencies
run: uv sync --group test
- name: Run benchmarks
+7 -11
View File
@@ -18,15 +18,15 @@ jobs:
- uses: actions/checkout@v6
- id: files
name: Get changed files
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
format: json
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
working-directory: libs/langgraph
- name: Install dependencies
run: uv sync --group test
- name: Download baseline
@@ -57,19 +57,15 @@ jobs:
echo EOF
} >> "$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: ${{ steps.compare.outputs.OUTPUT }}
uses: actions/github-script@v8
with:
script: |
const file = JSON.parse(process.env.CHANGED_FILES || "[]")[0]
core.notice(process.env.BENCHMARK_OUTPUT || "", {
const file = JSON.parse(`${{ 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,
})
+6 -26
View File
@@ -2,12 +2,10 @@
name: CI
on:
workflow_dispatch:
push:
branches:
- main
pull_request:
permissions:
contents: read
@@ -26,13 +24,11 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
python: ${{ steps.filter.outputs.python || 'true' }}
deps: ${{ steps.filter.outputs.deps || 'true' }}
sdk_py: ${{ steps.filter.outputs.sdk_py || '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: dorny/paths-filter@v3
id: filter
with:
filters: |
@@ -43,17 +39,10 @@ jobs:
- 'libs/checkpoint/**'
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/checkpoint-conformance/**'
- 'libs/prebuilt/**'
deps:
- '**/pyproject.toml'
- '**/uv.lock'
sdk_py:
- 'libs/sdk-py/**'
# The integration suite runs the local langgraph core inside the
# server (see libs/sdk-py/integration/Dockerfile), so any core
# change is now exercised end-to-end and should trigger it.
- 'libs/langgraph/**'
lint:
needs: changes
@@ -68,7 +57,7 @@ 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'
@@ -88,7 +77,6 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/sdk-py",
]
@@ -132,11 +120,11 @@ jobs:
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
with:
python-version: "3.13"
enable-cache: true
cache-suffix: "schema-check-cli"
working-directory: libs/cli
- name: Install CLI dependencies
run: |
cd libs/cli
@@ -163,13 +151,6 @@ jobs:
uses: ./.github/workflows/_integration_test.yml
secrets: inherit
sdk-py-integration-test:
needs: changes
if: needs.changes.outputs.sdk_py == 'true'
name: "sdk-py integration test"
uses: ./.github/workflows/_sdk_integration_test.yml
secrets: inherit
ci_success:
name: "CI Success"
needs:
@@ -180,7 +161,6 @@ jobs:
check-sdk-methods,
check-schema,
integration-test,
sdk-py-integration-test,
]
if: |
always()
-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
+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@v6
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
@@ -41,7 +41,6 @@ jobs:
docs
ci
deps
deps-dev
requireScope: false
ignoreLabels: |
ignore-lint-pr-title
+13 -17
View File
@@ -28,12 +28,11 @@ jobs:
- uses: actions/checkout@v6
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
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 +50,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -174,11 +173,10 @@ jobs:
# used in the real world.
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: false
working-directory: ${{ inputs.working-directory }}
enable-cache: true
- name: Import published package
shell: bash
@@ -265,20 +263,19 @@ jobs:
- uses: actions/checkout@v6
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
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@v7
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
@@ -307,20 +304,19 @@ jobs:
- uses: actions/checkout@v6
- name: Set up Python
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
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@v7
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: ${{ secrets.GITHUB_TOKEN }}
-195
View File
@@ -1,195 +0,0 @@
# Reopen PRs that were auto-closed by require_issue_link.yml when the
# contributor was not assigned to the linked issue. When a maintainer
# assigns the contributor to the issue, this workflow finds matching
# closed PRs, verifies the issue link, and reopens them.
#
# Uses the default GITHUB_TOKEN (not a PAT or app token) so that the
# reopen and label-removal events do NOT re-trigger other workflows.
# GitHub suppresses events created by the default GITHUB_TOKEN within
# workflow runs to prevent infinite loops.
name: Reopen PR on Issue Assignment
on:
issues:
types: [assigned]
permissions:
contents: read
jobs:
reopen-linked-prs:
runs-on: ubuntu-latest
permissions:
actions: write
pull-requests: write
steps:
- name: Find and reopen matching PRs
uses: actions/github-script@v9
with:
script: |
const { owner, repo } = context.repo;
const issueNumber = context.payload.issue.number;
const assignee = context.payload.assignee.login;
console.log(
`Issue #${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);
-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}`);
+4 -2
View File
@@ -19,16 +19,18 @@ jobs:
- uses: actions/checkout@v6
- name: Set up uv
uses: ./.github/actions/uv_setup
uses: astral-sh/setup-uv@v7
with:
# use minimum supported Python version
python-version: "3.10"
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@v8
with:
token: ${{ secrets.GITHUB_TOKEN }}
commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
-1
View File
@@ -100,4 +100,3 @@ dmypy.json
.turbo
.editorconfig
.scratch
.worktrees/
-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
View File
@@ -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.
-2
View File
@@ -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.
+65 -56
View File
@@ -1,81 +1,90 @@
<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://docs.langchain.com/oss/python/langgraph/overview)
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.
Create a simple workflow:
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
from langgraph.graph import START, StateGraph
from typing_extensions import TypedDict
## Why use LangGraph?
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
class State(TypedDict):
text: str
- **[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.
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
def node_a(state: State) -> dict:
return {"text": state["text"] + "a"}
## LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
def node_b(state: State) -> dict:
return {"text": state["text"] + "b"}
To improve your LLM application development, pair LangGraph with:
- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) Build agents that can plan, use subagents, and leverage file systems for complex tasks.
graph = StateGraph(State)
graph.add_node("node_a", node_a)
graph.add_node("node_b", node_b)
graph.add_edge(START, "node_a")
graph.add_edge("node_a", "node_b")
print(graph.compile().invoke({"text": ""}))
# {'text': 'ab'}
```
Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
## Core benefits
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:
- [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](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://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
## LangGraphs ecosystem
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:
- [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.
- [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 [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
- [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).
---
## Documentation
- [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
**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://docs.langchain.com/oss/javascript/langgraph/overview).
## 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://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): 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.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
## Acknowledgements
-1
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@@ -1 +0,0 @@
_site/
-142
View File
@@ -1,142 +0,0 @@
#!/usr/bin/env python3
"""
Generate HTML redirect files from redirects.json.
Usage:
python generate_redirects.py
This script reads redirects.json and generates individual HTML files
for each redirect path. Each HTML file uses meta refresh (0 delay)
which is SEO-friendly and treated similarly to 301 redirects by Google.
To add new redirects, simply edit redirects.json and re-run this script.
"""
import json
import os
from pathlib import Path
# Default fallback URL for any path not in the redirect map
DEFAULT_REDIRECT = "https://docs.langchain.com/oss/python/langgraph/overview"
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>
"""
ROOT_HTML_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting to LangGraph Documentation</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>
<h1>Documentation has moved</h1>
<p>The LangGraph documentation has moved to <a href="{url}">docs.langchain.com</a>.</p>
<p>Redirecting you now...</p>
</body>
</html>
"""
CATCHALL_404_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting to LangGraph Documentation</title>
<link rel="canonical" href="{default_url}">
<meta name="robots" content="noindex">
<script>
// Catchall redirect for any unmapped paths
window.location.replace("{default_url}");
</script>
<meta http-equiv="refresh" content="0; url={default_url}">
</head>
<body>
<h1>Documentation has moved</h1>
<p>The LangGraph documentation has moved to <a href="{default_url}">docs.langchain.com</a>.</p>
<p>Redirecting you now...</p>
</body>
</html>
"""
def generate_redirects():
script_dir = Path(__file__).parent
output_dir = script_dir / "_site"
# Load redirects
with open(script_dir / "redirects.json") as f:
redirects = json.load(f)
# Clean output directory
if output_dir.exists():
import shutil
shutil.rmtree(output_dir)
output_dir.mkdir(parents=True)
# Generate individual HTML files for each redirect
for old_path, new_url in redirects.items():
# Remove leading slash and create directory structure
path = old_path.lstrip("/")
# Check if path has a file extension (e.g., .txt, .xml)
# If so, create the file directly instead of a directory with index.html
path_obj = Path(path)
has_extension = path_obj.suffix and len(path_obj.suffix) <= 5
if not path:
html_path = output_dir / "index.html"
elif has_extension:
# For files with extensions, create the file directly
html_path = output_dir / path
else:
# For directory-style URLs, create index.html inside
html_path = output_dir / path / "index.html"
# Create parent directories
html_path.parent.mkdir(parents=True, exist_ok=True)
# Write the redirect HTML
html_path.write_text(HTML_TEMPLATE.format(url=new_url))
print(f"Created: {html_path}")
# Create root index.html
root_index = output_dir / "index.html"
if not root_index.exists():
root_index.write_text(ROOT_HTML_TEMPLATE.format(url=DEFAULT_REDIRECT))
print(f"Created: {root_index}")
# Create 404.html for catchall
catchall_404 = output_dir / "404.html"
catchall_404.write_text(CATCHALL_404_TEMPLATE.format(default_url=DEFAULT_REDIRECT))
print(f"Created: {catchall_404}")
# Copy static files (like llms.txt) that can't be redirected via HTML
static_files = ["llms.txt"]
for static_file in static_files:
src = script_dir / static_file
if src.exists():
dst = output_dir / static_file
dst.write_text(src.read_text())
print(f"Copied: {dst}")
print(f"\nGenerated {len(redirects)} redirect files in {output_dir}")
if __name__ == "__main__":
generate_redirects()
-35
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@@ -1,35 +0,0 @@
# LangGraph
LangGraph documentation has moved to docs.langchain.com.
## Overview
- [LangGraph Overview](https://docs.langchain.com/oss/python/langgraph/overview): Introduction to LangGraph, a library for building stateful, multi-actor applications with LLMs.
- [Why LangGraph?](https://docs.langchain.com/oss/python/langgraph/why-langgraph): Motivation for LangGraph and its key features.
## Core Concepts
- [Graph API](https://docs.langchain.com/oss/python/langgraph/graph-api): Learn how to define state, create nodes, and connect them with edges.
- [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming): Stream outputs from your graph for better UX.
- [Persistence](https://docs.langchain.com/oss/python/langgraph/persistence): Add memory and checkpointing to your graphs.
- [Add Memory](https://docs.langchain.com/oss/python/langgraph/add-memory): Implement short-term and long-term memory.
- [Workflows & Agents](https://docs.langchain.com/oss/python/langgraph/workflows-agents): Build agents and workflows with LangGraph.
## How-To Guides
- [Use Subgraphs](https://docs.langchain.com/oss/python/langgraph/use-subgraphs): Compose graphs using subgraphs.
- [Observability](https://docs.langchain.com/oss/python/langgraph/observability): Add tracing and debugging to your graphs.
- [Common Errors](https://docs.langchain.com/oss/python/langgraph/common-errors): Troubleshoot common LangGraph errors.
## Tutorials
- [Agentic RAG](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Build an agentic RAG system with LangGraph.
- [SQL Agent](https://docs.langchain.com/oss/python/langgraph/sql-agent): Create a SQL agent with LangGraph.
## Reference
- [API Reference](https://reference.langchain.com/python/langgraph/): Complete API documentation for LangGraph.
## LangGraph Platform
For deploying LangGraph applications in production, see the [LangSmith documentation](https://docs.langchain.com/langsmith/agent-server).
-296
View File
@@ -1,296 +0,0 @@
{
"/how-tos/stream-values": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/stream-updates": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-content": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/stream-multiple": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-tokens-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-from-final-node": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-events-from-within-tools-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/state-reducers": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-and-update-state",
"/how-tos/sequence": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-a-sequence-of-steps",
"/how-tos/branching": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-branches",
"/how-tos/recursion-limit": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-and-control-loops",
"/how-tos/visualization": "https://docs.langchain.com/oss/python/langgraph/graph-api#visualize-your-graph",
"/how-tos/input_output_schema": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-input-and-output-schemas",
"/how-tos/pass_private_state": "https://docs.langchain.com/oss/python/langgraph/graph-api#pass-private-state-between-nodes",
"/how-tos/state-model": "https://docs.langchain.com/oss/python/langgraph/graph-api#use-pydantic-models-for-graph-state",
"/how-tos/map-reduce": "https://docs.langchain.com/oss/python/langgraph/graph-api#map-reduce-and-the-send-api",
"/how-tos/command": "https://docs.langchain.com/oss/python/langgraph/graph-api#combine-control-flow-and-state-updates-with-command",
"/how-tos/configuration": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-runtime-configuration",
"/how-tos/node-retries": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-retry-policies",
"/how-tos/return-when-recursion-limit-hits": "https://docs.langchain.com/oss/python/langgraph/graph-api#impose-a-recursion-limit",
"/how-tos/async": "https://docs.langchain.com/oss/python/langgraph/graph-api#async",
"/how-tos/memory/manage-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/memory/delete-messages": "https://docs.langchain.com/oss/python/langgraph/add-memory#delete-messages",
"/how-tos/memory/add-summary-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory#summarize-messages",
"/how-tos/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/agents/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/subgraph-transform-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#different-state-schemas",
"/how-tos/subgraphs-manage-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#add-persistence",
"/how-tos/persistence_postgres": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
"/how-tos/persistence_mongodb": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
"/how-tos/persistence_redis": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
"/how-tos/subgraph-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-with-subgraphs",
"/how-tos/cross-thread-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
"/cloud/how-tos/copy_threads": "https://docs.langchain.com/langsmith/use-threads",
"/cloud/how-tos/check-thread-status": "https://docs.langchain.com/langsmith/use-threads",
"/cloud/concepts/threads": "https://docs.langchain.com/oss/python/langgraph/persistence#threads",
"/how-tos/persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/tool-calling-errors": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/how-tos/pass-config-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/how-tos/pass-run-time-values-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/how-tos/update-state-from-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/agents/tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/how-tos/agent-handoffs": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/multi-agent-network": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/multi-agent-multi-turn-convo": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/cloud/index": "https://docs.langchain.com/oss/python/langgraph/overview",
"/cloud/how-tos/index": "https://docs.langchain.com/langsmith/home",
"/cloud/concepts/api": "https://docs.langchain.com/langsmith/agent-server",
"/cloud/concepts/cloud": "https://docs.langchain.com/langsmith/cloud",
"/cloud/faq/studio": "https://docs.langchain.com/langsmith/studio",
"/cloud/how-tos/human_in_the_loop_edit_state": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
"/cloud/how-tos/human_in_the_loop_user_input": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
"/concepts/platform_architecture": "https://docs.langchain.com/langsmith/cloud#architecture",
"/cloud/how-tos/stream_values": "https://docs.langchain.com/langsmith/streaming",
"/cloud/how-tos/stream_updates": "https://docs.langchain.com/langsmith/streaming",
"/cloud/how-tos/stream_messages": "https://docs.langchain.com/langsmith/streaming",
"/cloud/how-tos/stream_events": "https://docs.langchain.com/langsmith/streaming",
"/cloud/how-tos/stream_debug": "https://docs.langchain.com/langsmith/streaming",
"/cloud/how-tos/stream_multiple": "https://docs.langchain.com/langsmith/streaming",
"/cloud/concepts/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/agents/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/create-react-agent": "https://docs.langchain.com/oss/python/langchain/agents#basic-configuration",
"/how-tos/create-react-agent-memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/create-react-agent-system-prompt": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/create-react-agent-structured-output": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
"/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
"/reference/prebuilt": "https://reference.langchain.com/python/langgraph/agents/",
"/concepts/high_level": "https://docs.langchain.com/oss/python/langgraph/overview",
"/concepts/index": "https://docs.langchain.com/oss/python/langgraph/overview",
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"/cloud/deployment/self_hosted_control_plane": "https://docs.langchain.com/langsmith/platform-setup",
"/cloud/deployment/standalone_container": "https://docs.langchain.com/langsmith/docker",
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"/cloud/how-tos/human_in_the_loop_time_travel": "https://docs.langchain.com/langsmith/human-in-the-loop-time-travel",
"/cloud/how-tos/add-human-in-the-loop": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
"/cloud/deployment/egress": "https://docs.langchain.com/langsmith/env-var",
"/cloud/how-tos/streaming": "https://docs.langchain.com/langsmith/streaming",
"/cloud/reference/api/api_ref": "https://docs.langchain.com/langsmith/server-api-ref",
"/cloud/reference/langgraph_server_changelog": "https://docs.langchain.com/langsmith/agent-server-changelog",
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"/cloud/reference/cli": "https://docs.langchain.com/langsmith/cli",
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"/troubleshooting/studio": "https://docs.langchain.com/langsmith/troubleshooting-studio",
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"/tutorials/get-started/3-add-memory": "https://docs.langchain.com/oss/python/langgraph/quickstart",
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"/tutorials/get-started/5-customize-state": "https://docs.langchain.com/oss/python/langgraph/quickstart",
"/tutorials/get-started/6-time-travel": "https://docs.langchain.com/oss/python/langgraph/quickstart",
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"/tutorials/workflows": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/tutorials/plan-and-execute/plan-and-execute": "https://docs.langchain.com/oss/python/langchain/middleware/built-in#to-do-list",
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"/concepts/agentic_concepts": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/agents/overview": "https://docs.langchain.com/oss/python/langchain/agents",
"/agents/run_agents": "https://docs.langchain.com/oss/python/langgraph/quickstart",
"/concepts/low_level": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/graph-api": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/react-agent-from-scratch": "https://docs.langchain.com/oss/python/langchain/quickstart",
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"/concepts/pregel": "https://docs.langchain.com/oss/python/langgraph/pregel",
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"/how-tos/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/concepts/persistence": "https://docs.langchain.com/oss/python/langgraph/persistence",
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"/concepts/memory": "https://docs.langchain.com/oss/python/langgraph/memory",
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"/agents/context": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/agents/models": "https://docs.langchain.com/oss/python/langgraph/overview",
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"/concepts/subgraphs": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
"/how-tos/subgraph": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
"/concepts/multi_agent": "https://docs.langchain.com/oss/python/langgraph/graph-api",
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"/concepts/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
"/agents/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
"/concepts/tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
"/how-tos/enable-tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
"/agents/evals": "https://docs.langchain.com/oss/python/langgraph/overview",
"/concepts/template_applications": "https://docs.langchain.com/oss/python/langgraph/overview",
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"/tutorials/sql/sql-agent": "https://docs.langchain.com/oss/python/langgraph/sql-agent",
"/agents/ui": "https://docs.langchain.com/oss/python/langgraph/ui",
"/how-tos/run-id-langsmith": "https://docs.langchain.com/oss/python/langgraph/observability",
"/troubleshooting/errors/index": "https://docs.langchain.com/oss/python/langgraph/common-errors",
"/troubleshooting/errors/INVALID_CHAT_HISTORY": "https://docs.langchain.com/oss/python/langgraph/INVALID_CHAT_HISTORY",
"/troubleshooting/errors/INVALID_LICENSE": "https://docs.langchain.com/oss/python/langgraph/common-errors",
"/adopters": "https://docs.langchain.com/oss/python/langgraph/case-studies",
"/concepts/faq": "https://docs.langchain.com/oss/python/langgraph/overview",
"/agents/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
"/reference/index": "https://reference.langchain.com/python/langgraph/",
"/reference/graphs": "https://reference.langchain.com/python/langgraph/graphs/",
"/reference/func": "https://reference.langchain.com/python/langgraph/func/",
"/reference/pregel": "https://reference.langchain.com/python/langgraph/pregel/",
"/reference/checkpoints": "https://reference.langchain.com/python/langgraph/checkpoints/",
"/reference/store": "https://reference.langchain.com/python/langgraph/store/",
"/reference/cache": "https://reference.langchain.com/python/langgraph/cache/",
"/reference/types": "https://reference.langchain.com/python/langgraph/types/",
"/reference/runtime": "https://reference.langchain.com/python/langgraph/runtime/",
"/reference/config": "https://reference.langchain.com/python/langgraph/config/",
"/reference/errors": "https://reference.langchain.com/python/langgraph/errors/",
"/reference/constants": "https://reference.langchain.com/python/langgraph/constants/",
"/reference/channels": "https://reference.langchain.com/python/langgraph/channels/",
"/reference/agents": "https://reference.langchain.com/python/langgraph/agents/",
"/reference/supervisor": "https://reference.langchain.com/python/langgraph/supervisor/",
"/reference/swarm": "https://reference.langchain.com/python/langgraph/swarm/",
"/reference/mcp": "https://reference.langchain.com/python/langgraph/mcp/",
"/cloud/reference/sdk/python_sdk_ref": "https://reference.langchain.com/python/langsmith/deployment/sdk/",
"/reference/remote_graph": "https://reference.langchain.com/python/langsmith/deployment/remote_graph/",
"/additional-resources/index": "https://docs.langchain.com/oss/python/langchain/overview",
"/cloud/reference/sdk/js_ts_sdk_ref": "https://reference.langchain.com/javascript/modules/langsmith.html",
"/snippets/chat_model_tabs": "https://docs.langchain.com/oss/python/langchain/overview",
"/troubleshooting/errors/GRAPH_RECURSION_LIMIT": "https://docs.langchain.com/oss/python/langgraph/GRAPH_RECURSION_LIMIT",
"/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE": "https://docs.langchain.com/oss/python/langgraph/INVALID_CONCURRENT_GRAPH_UPDATE",
"/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE": "https://docs.langchain.com/oss/python/langgraph/INVALID_GRAPH_NODE_RETURN_VALUE",
"/troubleshooting/errors/MULTIPLE_SUBGRAPHS": "https://docs.langchain.com/oss/python/langgraph/MULTIPLE_SUBGRAPHS",
"/tutorials/rag/langgraph_self_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/additional-resources": "https://docs.langchain.com/oss/python/langgraph/overview",
"/examples": "https://docs.langchain.com/oss/python/langgraph/overview",
"/guides": "https://docs.langchain.com/oss/python/langgraph/overview",
"/how-tos/autogen-integration-functional": "https://docs.langchain.com/oss/python/langgraph/overview",
"/how-tos/cross-thread-persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
"/how-tos/disable-streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/memory/semantic-search": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/multi-agent-multi-turn-convo-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/multi-agent-network-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/react-agent-from-scratch-functional": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/reference": "https://reference.langchain.com/python/langgraph/",
"/troubleshooting/errors": "https://docs.langchain.com/oss/python/langgraph/common-errors",
"/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/chatbots/information-gather-prompting": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/extraction/retries": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/langgraph-platform/local-server": "https://docs.langchain.com/langsmith/agent-server",
"/tutorials/lats/lats": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/llm-compiler/LLMCompiler": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/rag/langgraph_adaptive_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_crag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_crag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_self_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/reflection/reflection": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/reflexion/reflexion": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/rewoo/rewoo": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/self-discover/self-discover": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/tnt-llm/tnt-llm": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/tot/tot": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/usaco/usaco": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/web-navigation/web_voyager": "https://docs.langchain.com/oss/python/langgraph/overview"
}
+1 -1
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@@ -1,3 +1,3 @@
# LangGraph examples
This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.
This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.
+33
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@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "23544406",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/async.ipynb"
]
}
],
"metadata": {
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"display_name": "Python 3 (ipykernel)",
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"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "14f7ca50",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "10251c1c",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "c5fc63df",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
]
}
],
@@ -5,15 +5,7 @@
"id": "a4351a24",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "4cc9af1e",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb"
]
}
],
@@ -5,15 +5,7 @@
"id": "a9014f94",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "f47ce992",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
]
}
],
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "2b789e16",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "1f2f13ca",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "5e4c9bfe",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
]
}
],
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1d38cbab",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
@@ -41,9 +33,7 @@
"id": "e501686f-323f-4b87-8f9c-8ba89133078b",
"metadata": {},
"outputs": [],
"source": [
"! pip install -U langchain_community langchain-mistralai langchain langgraph"
]
"source": ["! pip install -U langchain_community langchain-mistralai langchain langgraph"]
},
{
"cell_type": "markdown",
@@ -61,12 +51,7 @@
"id": "982e4609-86e4-4934-828f-e03d89c20393",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
"mistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"
]
"source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"]
},
{
"cell_type": "markdown",
@@ -84,12 +69,7 @@
"id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
]
"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""]
},
{
"cell_type": "markdown",
@@ -107,42 +87,7 @@
"id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
"metadata": {},
"outputs": [],
"source": [
"# Select LLM\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_mistralai import ChatMistralAI\n",
"\n",
"mistral_model = \"mistral-large-latest\"\n",
"llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
"\n",
"# Prompt\n",
"code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
" defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
" \\n Here is the user question:\"\"\",\n",
" ),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"# Data model\n",
"class code(BaseModel):\n",
" \"\"\"Code output\"\"\"\n",
"\n",
" prefix: str = Field(description=\"Description of the problem and approach\")\n",
" imports: str = Field(description=\"Code block import statements\")\n",
" code: str = Field(description=\"Code block not including import statements\")\n",
" description = \"Schema for code solutions to questions about LCEL.\"\n",
"\n",
"\n",
"# LLM\n",
"code_gen_chain = llm.with_structured_output(code, include_raw=False)"
]
"source": ["# Select LLM\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_mistralai import ChatMistralAI\n\nmistral_model = \"mistral-large-latest\"\nllm = ChatMistralAI(model=mistral_model, temperature=0)\n\n# Prompt\ncode_gen_prompt_claude = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n \\n Here is the user question:\"\"\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n)\n\n\n# Data model\nclass code(BaseModel):\n \"\"\"Code output\"\"\"\n\n prefix: str = Field(description=\"Description of the problem and approach\")\n imports: str = Field(description=\"Code block import statements\")\n code: str = Field(description=\"Code block not including import statements\")\n description = \"Schema for code solutions to questions about LCEL.\"\n\n\n# LLM\ncode_gen_chain = llm.with_structured_output(code, include_raw=False)"]
},
{
"cell_type": "code",
@@ -150,10 +95,7 @@
"id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
"metadata": {},
"outputs": [],
"source": [
"question = \"Write a function for fibonacci.\"\n",
"messages = [(\"user\", question)]"
]
"source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
},
{
"cell_type": "code",
@@ -172,11 +114,7 @@
"output_type": "execute_result"
}
],
"source": [
"# Test\n",
"result = code_gen_chain.invoke(messages)\n",
"result"
]
"source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
},
{
"cell_type": "markdown",
@@ -192,28 +130,7 @@
"id": "183d77b8-f180-4815-b39f-8ef507ec0534",
"metadata": {},
"outputs": [],
"source": [
"from typing import Annotated, TypedDict\n",
"\n",
"from langgraph.graph.message import AnyMessage, add_messages\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" error : Binary flag for control flow to indicate whether test error was tripped\n",
" messages : With user question, error messages, reasoning\n",
" generation : Code solution\n",
" iterations : Number of tries\n",
" \"\"\"\n",
"\n",
" error: str\n",
" messages: Annotated[list[AnyMessage], add_messages]\n",
" generation: str\n",
" iterations: int"
]
"source": ["from typing import Annotated, TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n error : Binary flag for control flow to indicate whether test error was tripped\n messages : With user question, error messages, reasoning\n generation : Code solution\n iterations : Number of tries\n \"\"\"\n\n error: str\n messages: Annotated[list[AnyMessage], add_messages]\n generation: str\n iterations: int"]
},
{
"cell_type": "markdown",
@@ -229,163 +146,7 @@
"id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"### Parameters\n",
"max_iterations = 3\n",
"\n",
"\n",
"### Nodes\n",
"def generate(state: GraphState):\n",
" \"\"\"\n",
" Generate a code solution\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation\n",
" \"\"\"\n",
"\n",
" print(\"---GENERATING CODE SOLUTION---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Solution\n",
" code_solution = code_gen_chain.invoke(messages)\n",
" messages += [\n",
" (\n",
" \"assistant\",\n",
" f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
" )\n",
" ]\n",
"\n",
" # Increment\n",
" iterations = iterations + 1\n",
" return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
"\n",
"\n",
"def code_check(state: GraphState):\n",
" \"\"\"\n",
" Check code\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, error\n",
" \"\"\"\n",
"\n",
" print(\"---CHECKING CODE---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" code_solution = state[\"generation\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Get solution components\n",
" imports = code_solution.imports\n",
" code = code_solution.code\n",
"\n",
" # Check imports\n",
" try:\n",
" exec(imports)\n",
" except Exception as e:\n",
" print(\"---CODE IMPORT CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # Check execution\n",
" try:\n",
" combined_code = f\"{imports}\\n{code}\"\n",
" print(f\"CODE TO TEST: {combined_code}\")\n",
" # Use a shared scope for exec\n",
" global_scope = {}\n",
" exec(combined_code, global_scope)\n",
" except Exception as e:\n",
" print(\"---CODE BLOCK CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # No errors\n",
" print(\"---NO CODE TEST FAILURES---\")\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"no\",\n",
" }\n",
"\n",
"\n",
"### Conditional edges\n",
"\n",
"\n",
"def decide_to_finish(state: GraphState):\n",
" \"\"\"\n",
" Determines whether to finish.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Next node to call\n",
" \"\"\"\n",
" error = state[\"error\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" if error == \"no\" or iterations == max_iterations:\n",
" print(\"---DECISION: FINISH---\")\n",
" return \"end\"\n",
" else:\n",
" print(\"---DECISION: RE-TRY SOLUTION---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"### Utilities\n",
"\n",
"\n",
"def _print_event(event: dict, _printed: set, max_length=1500):\n",
" current_state = event.get(\"dialog_state\")\n",
" if current_state:\n",
" print(\"Currently in: \", current_state[-1])\n",
" message = event.get(\"messages\")\n",
" if message:\n",
" if isinstance(message, list):\n",
" message = message[-1]\n",
" if message.id not in _printed:\n",
" msg_repr = message.pretty_repr(html=True)\n",
" if len(msg_repr) > max_length:\n",
" msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
" print(msg_repr)\n",
" _printed.add(message.id)"
]
"source": ["import uuid\n\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n### Parameters\nmax_iterations = 3\n\n\n### Nodes\ndef generate(state: GraphState):\n \"\"\"\n Generate a code solution\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation\n \"\"\"\n\n print(\"---GENERATING CODE SOLUTION---\")\n\n # State\n messages = state[\"messages\"]\n iterations = state[\"iterations\"]\n\n # Solution\n code_solution = code_gen_chain.invoke(messages)\n messages += [\n (\n \"assistant\",\n f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n )\n ]\n\n # Increment\n iterations = iterations + 1\n return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n\n\ndef code_check(state: GraphState):\n \"\"\"\n Check code\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, error\n \"\"\"\n\n print(\"---CHECKING CODE---\")\n\n # State\n messages = state[\"messages\"]\n code_solution = state[\"generation\"]\n iterations = state[\"iterations\"]\n\n # Get solution components\n imports = code_solution.imports\n code = code_solution.code\n\n # Check imports\n try:\n exec(imports)\n except Exception as e:\n print(\"---CODE IMPORT CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # Check execution\n try:\n combined_code = f\"{imports}\\n{code}\"\n print(f\"CODE TO TEST: {combined_code}\")\n # Use a shared scope for exec\n global_scope = {}\n exec(combined_code, global_scope)\n except Exception as e:\n print(\"---CODE BLOCK CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # No errors\n print(\"---NO CODE TEST FAILURES---\")\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"no\",\n }\n\n\n### Conditional edges\n\n\ndef decide_to_finish(state: GraphState):\n \"\"\"\n Determines whether to finish.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Next node to call\n \"\"\"\n error = state[\"error\"]\n iterations = state[\"iterations\"]\n\n if error == \"no\" or iterations == max_iterations:\n print(\"---DECISION: FINISH---\")\n return \"end\"\n else:\n print(\"---DECISION: RE-TRY SOLUTION---\")\n return \"generate\"\n\n\n### Utilities\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n current_state = event.get(\"dialog_state\")\n if current_state:\n print(\"Currently in: \", current_state[-1])\n message = event.get(\"messages\")\n if message:\n if isinstance(message, list):\n message = message[-1]\n if message.id not in _printed:\n msg_repr = message.pretty_repr(html=True)\n if len(msg_repr) > max_length:\n msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n print(msg_repr)\n _printed.add(message.id)"]
},
{
"cell_type": "code",
@@ -393,31 +154,7 @@
"id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.memory import InMemorySaver\n",
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"builder = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"builder.add_node(\"generate\", generate) # generation solution\n",
"builder.add_node(\"check_code\", code_check) # check code\n",
"\n",
"# Build graph\n",
"builder.add_edge(START, \"generate\")\n",
"builder.add_edge(\"generate\", \"check_code\")\n",
"builder.add_conditional_edges(\n",
" \"check_code\",\n",
" decide_to_finish,\n",
" {\n",
" \"end\": END,\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"\n",
"memory = InMemorySaver()\n",
"graph = builder.compile(checkpointer=memory)"
]
"source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
},
{
"cell_type": "code",
@@ -436,15 +173,7 @@
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
},
{
"cell_type": "code",
@@ -452,23 +181,7 @@
"id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"Write a Python program that prints 'Hello, World!' to the console.\"\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -486,31 +199,7 @@
"id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
"\n",
"Requirements:\n",
"The program should define a function is_palindrome(s) that takes a string s as input.\n",
"The function should return True if the string is a palindrome and False otherwise.\n",
"Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
"\n",
"Give an example of it working on an example input word.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n\nRequirements:\nThe program should define a function is_palindrome(s) that takes a string s as input.\nThe function should return True if the string is a palindrome and False otherwise.\nIgnore spaces, punctuation, and case differences when checking for palindromes.\n\nGive an example of it working on an example input word.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -528,26 +217,7 @@
"id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
"But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
"For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Write a program that prints the numbers from 1 to 100. \nBut for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \nFor numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -565,37 +235,7 @@
"id": "2bb883df-540b-46ab-9415-fe27db68456f",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"I want to vectorize a function\n",
"\n",
" frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
" for i, val1 in enumerate(rows):\n",
" for j, val2 in enumerate(cols):\n",
" for j, val3 in enumerate(ch):\n",
" # Assuming you want to store the pair as tuples in the matrix\n",
" frame[i, j, k] = image[val1, val2, val3]\n",
"\n",
" out.write(np.array(frame))\n",
"\n",
"with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["import uuid\n\n_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"I want to vectorize a function\n\n frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n for i, val1 in enumerate(rows):\n for j, val2 in enumerate(cols):\n for j, val3 in enumerate(ch):\n # Assuming you want to store the pair as tuples in the matrix\n frame[i, j, k] = image[val1, val2, val3]\n\n out.write(np.array(frame))\n\nwith a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -613,34 +253,7 @@
"id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
"\n",
"- Allow players to take turns to input their moves.\n",
"- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
"- Determine and announce the winner or if the game ends in a draw.\n",
"\n",
"Requirements:\n",
"- Use a 2D list to represent the Tic-Tac-Toe board.\n",
"- Use functions to modularize the code.\n",
"- Validate player input.\n",
"- Check for win conditions and draw conditions after each move.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n\n- Allow players to take turns to input their moves.\n- Check for invalid moves (e.g., placing a marker on an already occupied space).\n- Determine and announce the winner or if the game ends in a draw.\n\nRequirements:\n- Use a 2D list to represent the Tic-Tac-Toe board.\n- Use functions to modularize the code.\n- Validate player input.\n- Check for win conditions and draw conditions after each move.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -658,7 +271,7 @@
"id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
"metadata": {},
"outputs": [],
"source": []
"source": [""]
}
],
"metadata": {
+33
View File
@@ -0,0 +1,33 @@
{
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@@ -0,0 +1,33 @@
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@@ -5,15 +5,7 @@
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"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
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@@ -5,15 +5,7 @@
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"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
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"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb"
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{
"cell_type": "markdown",
"id": "8f38bec5",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-run-time-values-to-tools.ipynb"
]
}
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{
"cells": [
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"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass_private_state.ipynb"
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{
"cells": [
{
"cell_type": "markdown",
"id": "d16e8b9c",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md."
]
}
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"version": "3.12.2"
}
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"nbformat": 4,
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{
"cells": [
{
"cell_type": "markdown",
"id": "78217098",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_mongodb.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"mimetype": "text/x-python",
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{
"cells": [
{
"cell_type": "markdown",
"id": "18526f23",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
]
}
],
"metadata": {
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"display_name": "Python 3 (ipykernel)",
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"version": "3.11.9"
}
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"nbformat": 4,
"nbformat_minor": 5
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+33
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@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "eee6ecdd",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_redis.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "9138f92e",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "093678ba",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb"
]
}
],
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fedd6d23",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"36fa621a-9d3d-4860-a17c-5d20e6987481.png": {
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{
"cells": [
{
"cell_type": "markdown",
"id": "39b26b09",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"3755396d-c4a8-45bd-87d4-00cb56339fe5.png": {
+3 -11
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@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "47e3b43b",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"cell_type": "markdown",
"id": "425fb020-e864-40ce-a31f-8da40c73d14b",
@@ -208,11 +200,11 @@
"output_type": "stream",
"text": [
"********************Prompt[rlm/rag-prompt]********************\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"================================\u001B[1m Human Message \u001B[0m=================================\n",
"\n",
"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
"Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
"Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
"Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
"Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
"Answer:\n"
]
}
-8
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@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "c71da2ea",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
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},
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"attachments": {
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-8
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@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ac7db067",
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"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
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"attachments": {
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-8
View File
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "b3d959ff",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"15cba0ab-a549-4909-8373-fb761e384eff.png": {
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "345488d8",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "403aeb6e",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"15cba0ab-a549-4909-8373-fb761e384eff.png": {
@@ -62,11 +54,7 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
"import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
]
},
{
@@ -76,9 +64,7 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
"import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
]
},
{
@@ -98,18 +84,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"# use pinecone movies database\n",
"\n",
"# Add to vectorDB\n",
"vectorstore = PineconeVectorStore(\n",
" embedding=OpenAIEmbeddings(),\n",
" index_name=\"sample-movies\",\n",
" text_key=\"summary\",\n",
")\n",
"retriever = vectorstore.as_retriever()"
"from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n embedding=OpenAIEmbeddings(),\n index_name=\"sample-movies\",\n text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
]
},
{
@@ -138,11 +113,7 @@
}
],
"source": [
"docs = retriever.invoke(\"James Cameron\")\n",
"for doc in docs:\n",
" print(\"# \" + doc.metadata[\"title\"])\n",
" print(doc.page_content)\n",
" print()"
"docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n print(\"# \" + doc.metadata[\"title\"])\n print(doc.page_content)\n print()"
]
},
{
@@ -202,12 +173,7 @@
}
],
"source": [
"# Test the retrieval grader\n",
"question = \"movies starring jason momoa\"\n",
"docs = retriever.invoke(question)\n",
"doc_txt = docs[0].page_content\n",
"print(doc_txt)\n",
"print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
"# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
]
},
{
@@ -235,23 +201,7 @@
}
],
"source": [
"### Generate\n",
"\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"# Prompt\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"# Chain\n",
"rag_chain = prompt | llm | StrOutputParser()\n",
"\n",
"# Run\n",
"generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
"print(generation)"
"### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
]
},
{
@@ -379,17 +329,7 @@
}
],
"source": [
"### Question Re-writer\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"\n",
"# Prompt\n",
"re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
"\n",
"question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
"print(question)\n",
"question_rewriter.invoke({\"question\": question})"
"### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
]
},
{
@@ -411,24 +351,7 @@
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from typing_extensions import TypedDict\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" question: question\n",
" generation: LLM generation\n",
" documents: list of documents\n",
" \"\"\"\n",
"\n",
" question: str\n",
" generation: str\n",
" documents: List[str]"
"from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n question: question\n generation: LLM generation\n documents: list of documents\n \"\"\"\n\n question: str\n generation: str\n documents: List[str]"
]
},
{
@@ -438,95 +361,7 @@
"metadata": {},
"outputs": [],
"source": [
"### Nodes\n",
"\n",
"\n",
"def retrieve(state):\n",
" \"\"\"\n",
" Retrieve documents\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, documents, that contains retrieved documents\n",
" \"\"\"\n",
" print(\"---RETRIEVE---\")\n",
" question = state[\"question\"]\n",
"\n",
" # Retrieval\n",
" documents = retriever.invoke(question)\n",
" return {\"documents\": documents, \"question\": question}\n",
"\n",
"\n",
"def generate(state):\n",
" \"\"\"\n",
" Generate answer\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation, that contains LLM generation\n",
" \"\"\"\n",
" print(\"---GENERATE---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # RAG generation\n",
" generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
" return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
"\n",
"\n",
"def grade_documents(state):\n",
" \"\"\"\n",
" Determines whether the retrieved documents are relevant to the question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates documents key with only filtered relevant documents\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Score each doc\n",
" filtered_docs = []\n",
" for d in documents:\n",
" score = retrieval_grader.invoke(\n",
" {\"question\": question, \"document\": d.page_content}\n",
" )\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
" filtered_docs.append(d)\n",
" else:\n",
" print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
" continue\n",
" return {\"documents\": filtered_docs, \"question\": question}\n",
"\n",
"\n",
"def transform_query(state):\n",
" \"\"\"\n",
" Transform the query to produce a better question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates question key with a re-phrased question\n",
" \"\"\"\n",
"\n",
" print(\"---TRANSFORM QUERY---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Re-write question\n",
" better_question = question_rewriter.invoke({\"question\": question})\n",
" return {\"documents\": documents, \"question\": better_question}"
"### Nodes\n\n\ndef retrieve(state):\n \"\"\"\n Retrieve documents\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, documents, that contains retrieved documents\n \"\"\"\n print(\"---RETRIEVE---\")\n question = state[\"question\"]\n\n # Retrieval\n documents = retriever.invoke(question)\n return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n \"\"\"\n Generate answer\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation, that contains LLM generation\n \"\"\"\n print(\"---GENERATE---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # RAG generation\n generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n \"\"\"\n Determines whether the retrieved documents are relevant to the question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates documents key with only filtered relevant documents\n \"\"\"\n\n print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Score each doc\n filtered_docs = []\n for d in documents:\n score = retrieval_grader.invoke(\n {\"question\": question, \"document\": d.page_content}\n )\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---GRADE: DOCUMENT RELEVANT---\")\n filtered_docs.append(d)\n else:\n print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n continue\n return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n \"\"\"\n Transform the query to produce a better question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates question key with a re-phrased question\n \"\"\"\n\n print(\"---TRANSFORM QUERY---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Re-write question\n better_question = question_rewriter.invoke({\"question\": question})\n return {\"documents\": documents, \"question\": better_question}"
]
},
{
@@ -536,74 +371,7 @@
"metadata": {},
"outputs": [],
"source": [
"### Edges\n",
"\n",
"\n",
"def decide_to_generate(state):\n",
" \"\"\"\n",
" Determines whether to generate an answer, or re-generate a question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Binary decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---ASSESS GRADED DOCUMENTS---\")\n",
" state[\"question\"]\n",
" filtered_documents = state[\"documents\"]\n",
"\n",
" if not filtered_documents:\n",
" # All documents have been filtered check_relevance\n",
" # We will re-generate a new query\n",
" print(\n",
" \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
" )\n",
" return \"transform_query\"\n",
" else:\n",
" # We have relevant documents, so generate answer\n",
" print(\"---DECISION: GENERATE---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"def grade_generation_v_documents_and_question(state):\n",
" \"\"\"\n",
" Determines whether the generation is grounded in the document and answers question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK HALLUCINATIONS---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
" generation = state[\"generation\"]\n",
"\n",
" score = hallucination_grader.invoke(\n",
" {\"documents\": documents, \"generation\": generation}\n",
" )\n",
" grade = score.binary_score\n",
"\n",
" # Check hallucination\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
" # Check question-answering\n",
" print(\"---GRADE GENERATION vs QUESTION---\")\n",
" score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
" return \"useful\"\n",
" else:\n",
" print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
" return \"not useful\"\n",
" else:\n",
" pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
" return \"not supported\""
"### Edges\n\n\ndef decide_to_generate(state):\n \"\"\"\n Determines whether to generate an answer, or re-generate a question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Binary decision for next node to call\n \"\"\"\n\n print(\"---ASSESS GRADED DOCUMENTS---\")\n state[\"question\"]\n filtered_documents = state[\"documents\"]\n\n if not filtered_documents:\n # All documents have been filtered check_relevance\n # We will re-generate a new query\n print(\n \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n )\n return \"transform_query\"\n else:\n # We have relevant documents, so generate answer\n print(\"---DECISION: GENERATE---\")\n return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n \"\"\"\n Determines whether the generation is grounded in the document and answers question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Decision for next node to call\n \"\"\"\n\n print(\"---CHECK HALLUCINATIONS---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n generation = state[\"generation\"]\n\n score = hallucination_grader.invoke(\n {\"documents\": documents, \"generation\": generation}\n )\n grade = score.binary_score\n\n # Check hallucination\n if grade == \"yes\":\n print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n # Check question-answering\n print(\"---GRADE GENERATION vs QUESTION---\")\n score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n return \"useful\"\n else:\n print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n return \"not useful\"\n else:\n pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n return \"not supported\""
]
},
{
@@ -622,42 +390,7 @@
"id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"workflow = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
"workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
"workflow.add_node(\"generate\", generate) # generate\n",
"workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
"\n",
"# Build graph\n",
"workflow.add_edge(START, \"retrieve\")\n",
"workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
"workflow.add_conditional_edges(\n",
" \"grade_documents\",\n",
" decide_to_generate,\n",
" {\n",
" \"transform_query\": \"transform_query\",\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"workflow.add_edge(\"transform_query\", \"retrieve\")\n",
"workflow.add_conditional_edges(\n",
" \"generate\",\n",
" grade_generation_v_documents_and_question,\n",
" {\n",
" \"not supported\": \"generate\",\n",
" \"useful\": END,\n",
" \"not useful\": \"transform_query\",\n",
" },\n",
")\n",
"\n",
"# Compile\n",
"app = workflow.compile()"
]
"source": ["from langgraph.graph import END, StateGraph, START\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"retrieve\", retrieve) # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents) # grade documents\nworkflow.add_node(\"generate\", generate) # generate\nworkflow.add_node(\"transform_query\", transform_query) # transform_query\n\n# Build graph\nworkflow.add_edge(START, \"retrieve\")\nworkflow.add_edge(\"retrieve\", \"grade_documents\")\nworkflow.add_conditional_edges(\n \"grade_documents\",\n decide_to_generate,\n {\n \"transform_query\": \"transform_query\",\n \"generate\": \"generate\",\n },\n)\nworkflow.add_edge(\"transform_query\", \"retrieve\")\nworkflow.add_conditional_edges(\n \"generate\",\n grade_generation_v_documents_and_question,\n {\n \"not supported\": \"generate\",\n \"useful\": END,\n \"not useful\": \"transform_query\",\n },\n)\n\n# Compile\napp = workflow.compile()"]
},
{
"cell_type": "code",
@@ -693,18 +426,7 @@
}
],
"source": [
"from pprint import pprint\n",
"\n",
"# Run\n",
"inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
"from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
]
},
{
@@ -714,15 +436,7 @@
"metadata": {},
"outputs": [],
"source": [
"inputs = {\"question\": \"Which movies are about aliens?\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
"inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
]
},
{
@@ -731,7 +445,9 @@
"id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
"metadata": {},
"outputs": [],
"source": []
"source": [
""
]
}
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"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-from-scratch.ipynb"
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@@ -5,14 +5,7 @@
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"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-structured-output.ipynb)"
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"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-structured-output.ipynb"
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+33
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@@ -0,0 +1,33 @@
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"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb"
]
}
],
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"id": "961f43ec",
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"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/rewoo/rewoo.ipynb)"
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+33
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@@ -0,0 +1,33 @@
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"version": "3.11.9"
}
},
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"nbformat_minor": 5
}
@@ -0,0 +1,33 @@
{
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{
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"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -0,0 +1,33 @@
{
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"version": "3.11.9"
}
},
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+33
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+33
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}
},
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}
},
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}

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