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William Fu-Hinthorn 8225701511 Better handle sync calls 2024-12-05 18:40:17 -08:00
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name: "\U0001F41B Bug Report"
description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
labels: ["bug"]
type: bug
description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
labels: ["02 Bug Report"]
body:
- type: markdown
attributes:
value: |
Thank you for taking the time to file a bug report.
> **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy).
For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
* [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
* [API Reference Documentation](https://reference.langchain.com/python/),
* [LangChain ChatBot](https://chat.langchain.com/)
* [GitHub search](https://github.com/langchain-ai/langgraph),
* [LangChain Forum](https://forum.langchain.com/),
value: >
Thank you for taking the time to file a bug report.
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
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:
[LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
[LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
[LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
[LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
[GitHub search](https://github.com/langchain-ai/langgraph),
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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 GitHub Discussions.
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.
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- 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).
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- 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.
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- label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
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If this bug is related to any existing issues or pull requests, please link them here.
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* e.g. #123, #456
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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
placeholder: |
from langgraph.graph import StateGraph
@@ -75,13 +47,17 @@ body:
chain = StateGraph(list)
chain.invoke('Hello!')
render: python
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If you are reporting an error, please copy and paste the full error message and
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@@ -102,18 +78,7 @@ body:
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Please share your system info with us.
Run the following command in your terminal and paste the output here:
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or if you have an existing python interpreter running:
```python
from langchain_core import sys_info
sys_info.print_sys_info()
```
python -m langchain_core.sys_info
placeholder: |
python -m langchain_core.sys_info
validations:
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blank_issues_enabled: false
version: 2.1
contact_links:
- name: 💬 LangChain Forum
url: https://forum.langchain.com/
about: General community discussions and support
- name: 📚 LangGraph Documentation
url: https://docs.langchain.com/oss/python/langgraph/overview
about: View the official LangGraph documentation
- name: 📚 API Reference Documentation
url: https://reference.langchain.com/python/langgraph/
about: View the official LangGraph API reference documentation
- name: 📚 Documentation issue
url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
about: Report an issue related to the LangGraph documentation
- name: 🤔 Question or Problem
about: Ask a question or ask about a problem in GitHub Discussions.
url: https://github.com/langchain-ai/langgraph/discussions/categories/q-a
- name: Feature Request
url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
about: Suggest a feature or an idea
- name: Show and tell
about: Show what you built with LangChain
url: https://github.com/langchain-ai/langgraph/discussions/categories/show-and-tell
- name: Slack
url: https://www.langchain.com/join-community
about: General community discussions
+19
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name: Documentation
description: Report an issue related to the LangGraph documentation.
title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
labels: [03 - Documentation]
body:
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attributes:
label: "Issue with current documentation:"
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Please make sure to leave a reference to the document/code you're
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label: "Idea or request for content:"
description: >
Please describe as clearly as possible what topics you think are missing
from the current documentation.
+9 -12
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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 LangChain 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).
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.
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Thanks for your interest in LangChain! 🚀
If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
or are a regular contributor to LangChain with previous merged merged pull requests.
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Fixes #
<!-- Replace everything above this line with a 1-2 sentence description of your change. Keep the "Fixes #xx" keyword and update the issue number. -->
Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview
> **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy).
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:
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- If there are any breaking changes, please clearly describe them.
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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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# An action for setting up poetry install with caching.
# Using a custom action since the default action does not
# take poetry install groups into account.
# Action code from:
# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
name: poetry-install-with-caching
description: Poetry install with support for caching of dependency groups.
inputs:
python-version:
description: Python version, supporting MAJOR.MINOR only
required: true
poetry-version:
description: Poetry version
required: true
cache-key:
description: Cache key to use for manual handling of caching
required: true
runs:
using: composite
steps:
- uses: actions/setup-python@v5
name: Setup python ${{ inputs.python-version }}
id: setup-python
with:
python-version: ${{ inputs.python-version }}
- uses: actions/cache@v3
id: cache-bin-poetry
name: Cache Poetry binary - Python ${{ inputs.python-version }}
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
with:
path: |
/opt/pipx/venvs/poetry
# This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
- name: Refresh shell hashtable and fixup softlinks
if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
shell: bash
env:
POETRY_VERSION: ${{ inputs.poetry-version }}
PYTHON_VERSION: ${{ inputs.python-version }}
run: |
set -eux
# Refresh the shell hashtable, to ensure correct `which` output.
hash -r
# `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
# Delete and recreate the softlinks pipx expects to have.
rm /opt/pipx/venvs/poetry/bin/python
cd /opt/pipx/venvs/poetry/bin
ln -s "$(which "python$PYTHON_VERSION")" python
chmod +x python
cd /opt/pipx_bin/
ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
chmod +x poetry
# Ensure everything got set up correctly.
/opt/pipx/venvs/poetry/bin/python --version
/opt/pipx_bin/poetry --version
- name: Install poetry
if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
shell: bash
env:
POETRY_VERSION: ${{ inputs.poetry-version }}
PYTHON_VERSION: ${{ inputs.python-version }}
# Install poetry using the python version installed by setup-python step.
run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
- name: Restore pip and poetry cached dependencies
uses: actions/cache@v3
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
with:
path: |
~/.cache/pip
~/.cache/pypoetry/virtualenvs
~/.cache/pypoetry/cache
~/.cache/pypoetry/artifacts
./.venv
key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('./poetry.lock') }}
-35
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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 }}
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@@ -1,188 +0,0 @@
version: 2
updates:
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "monthly"
groups:
minor-and-patch:
patterns:
- "*"
update-types:
- "minor"
- "patch"
major:
patterns:
- "*"
update-types:
- "major"
- package-ecosystem: "uv"
directory: "/libs/checkpoint"
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"
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Before

Width:  |  Height:  |  Size: 4.7 KiB

+3 -8
View File
@@ -1,15 +1,10 @@
import ast
import os
from itertools import filterfalse
from typing import Dict, List, Tuple
from typing import List, Tuple
ROOT_PATH = os.path.abspath(os.path.join(__file__, "..", "..", ".."))
CLIENT_PATH = os.path.join(ROOT_PATH, "libs", "sdk-py", "langgraph_sdk", "client.py")
ASYNC_TO_SYNC_METHOD_MAP: Dict[str, str] = {
"aclose": "close",
"__aenter__": "__enter__",
"__aexit__": "__exit__",
}
def get_class_methods(node: ast.ClassDef) -> List[str]:
@@ -27,7 +22,7 @@ def find_classes(tree: ast.AST) -> List[Tuple[str, List[str]]]:
def compare_sync_async_methods(sync_methods: List[str], async_methods: List[str]) -> List[str]:
sync_set = set(sync_methods)
async_set = {ASYNC_TO_SYNC_METHOD_MAP.get(async_method, async_method) for async_method in async_methods}
async_set = set(async_methods)
missing_in_sync = list(async_set - sync_set)
missing_in_async = list(sync_set - async_set)
return missing_in_sync + missing_in_async
@@ -38,7 +33,7 @@ def main():
tree = ast.parse(file.read())
classes = find_classes(tree)
def is_sync(class_spec: Tuple[str, List[str]]) -> bool:
return class_spec[0].startswith("Sync")
+84 -146
View File
@@ -1,164 +1,108 @@
import logging
import asyncio
import json
import os
import pathlib
import sys
import time
from urllib import error, request
import langgraph_cli
import langgraph_cli.config
import langgraph_cli.docker
from langgraph_cli.cli import prepare_args_and_stdin
from langgraph_cli.constants import DEFAULT_PORT
import langgraph_cli.config
from langgraph_cli.exec import Runner, subp_exec
from langgraph_cli.progress import Progress
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
from langgraph_cli.constants import DEFAULT_PORT
def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
"""Spin up API with Postgres/Redis via docker compose and wait until ready."""
logger.info("Starting test...")
def test(
config: pathlib.Path,
port: int,
tag: str,
verbose: bool,
):
with Runner() as runner, Progress(message="Pulling...") as set:
# Detect docker/compose capabilities
# check docker available
capabilities = langgraph_cli.docker.check_capabilities(runner)
# Validate config and prepare compose stdin/args using built image
# open config
config_json = langgraph_cli.config.validate_config_file(config)
args, stdin = prepare_args_and_stdin(
capabilities=capabilities,
config_path=config,
config=config_json,
docker_compose=None,
port=port,
watch=False,
debugger_port=None,
debugger_base_url=f"http://127.0.0.1:{port}",
postgres_uri=None,
api_version=None,
image=tag,
base_image=None,
)
# Compose up with wait (implies detach), similar to `langgraph up --wait`
args_up = [*args, "up", "--remove-orphans", "--wait"]
compose_cmd = ["docker", "compose"]
if capabilities.compose_type == "standalone":
compose_cmd = ["docker-compose"]
set("Starting...")
try:
runner.run(
subp_exec(
*compose_cmd,
*args_up,
input=stdin,
verbose=verbose,
)
set("Running...")
args = [
"run",
"--rm",
"-p",
f"{port}:8000",
]
if isinstance(config_json["env"], str):
args.extend(
[
"--env-file",
str(config.parent / config_json["env"]),
]
)
except Exception as e: # noqa: BLE001
# On failure, show diagnostics then ensure clean teardown
sys.stderr.write(f"docker compose up failed: {e}\n")
try:
sys.stderr.write("\n== docker compose ps ==\n")
runner.run(
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
)
except Exception:
pass
try:
sys.stderr.write("\n== docker compose logs (api) ==\n")
runner.run(
subp_exec(
*compose_cmd,
*args,
"logs",
"langgraph-api",
input=stdin,
verbose=True,
)
)
except Exception:
pass
finally:
try:
runner.run(
subp_exec(
*compose_cmd,
*args,
"down",
"-v",
"--remove-orphans",
input=stdin,
verbose=False,
)
)
finally:
raise
set("")
base_url = f"http://localhost:{port}"
ok_url = f"{base_url}/ok"
logger.info(f"Waiting for {ok_url} to respond with 200...")
deadline = time.time() + 30
last_err: Exception | None = None
while time.time() < deadline:
try:
with request.urlopen(ok_url, timeout=2) as resp:
if resp.status == 200:
sys.stdout.write(
f"""Ready!\n- API: {base_url}\n- /ok: 200 OK\n"""
)
sys.stdout.flush()
break
else:
last_err = RuntimeError(f"Unexpected status: {resp.status}")
logger.error(f"Unexpected status: {resp.status}")
except error.URLError as e:
logger.error(f"URLError: {e}")
last_err = e
except Exception as e: # noqa: BLE001
logger.error(f"Exception: {e}")
last_err = e
time.sleep(0.5)
else:
logger.error("Timeout waiting for /ok to return 200")
# Bring stack down before raising
args_down = [*args, "down", "-v", "--remove-orphans"]
try:
runner.run(
subp_exec(
*compose_cmd,
*args_down,
input=stdin,
verbose=verbose,
)
)
finally:
raise SystemExit(
f"/ok did not return 202 within timeout. Last error: {last_err}"
for k, v in config_json["env"].items():
args.extend(
[
"-e",
f"{k}={v}",
]
)
if capabilities.healthcheck_start_interval:
args.extend(
[
"--health-interval",
"5s",
"--health-retries",
"1",
"--health-start-period",
"10s",
"--health-start-interval",
"1s",
]
)
else:
args.extend(
[
"--health-interval",
"5s",
"--health-retries",
"2",
]
)
_task = None
def on_stdout(line: str):
nonlocal _task
if "GET /ok" in line or "Uvicorn running on" in line:
set("")
sys.stdout.write(
f"""Ready!
- API: http://localhost:{port}
"""
)
sys.stdout.flush()
_task.cancel()
return True
return False
async def subp_exec_task(*args, **kwargs):
nonlocal _task
_task = asyncio.create_task(subp_exec(*args, **kwargs))
await _task
# Clean up: bring compose stack down to free ports for next test
logger.info("Test succeeded. Bringing down compose stack...")
try:
args_down = [*args, "down", "-v", "--remove-orphans"]
runner.run(
subp_exec(
*compose_cmd,
*args_down,
input=stdin,
subp_exec_task(
"docker",
*args,
tag,
verbose=verbose,
on_stdout=on_stdout,
)
)
logger.info("Compose stack down. Finishing...")
except Exception:
logger.exception("Failed to bring down compose stack")
except asyncio.CancelledError:
pass
logger.info("Test finished")
if __name__ == "__main__":
import argparse
@@ -166,12 +110,6 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-t", "--tag", type=str)
parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT)
parser.add_argument("-p", "--port", default=DEFAULT_PORT)
args = parser.parse_args()
try:
test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
except BaseException:
logger.exception("Test failed")
raise
logger.info("Test execution finished")
test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
+41 -105
View File
@@ -2,12 +2,9 @@ name: CLI integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
jobs:
build:
@@ -16,123 +13,62 @@ jobs:
matrix:
python-version:
- "3.10"
- "3.14"
example:
- name: A
workdir: libs/cli/examples
tag: langgraph-test-a
- name: B
workdir: libs/cli/examples/graphs
tag: langgraph-test-b
- name: C
workdir: libs/cli/examples/graphs_reqs_a
tag: langgraph-test-c
- name: D
workdir: libs/cli/examples/graphs_reqs_b
tag: langgraph-test-d
- "3.11"
name: "CLI integration test"
env:
HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
defaults:
run:
working-directory: libs/cli
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "libs/cli/**"
- name: Set up Python ${{ matrix.python-version }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
uses: ./.github/actions/uv_setup
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
if: steps.changed-files.outputs.all
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
enable-cache: "false"
working-directory: libs/cli
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: integration-test-cli
- name: Setup env
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: cat .env.example > .env
- name: Install cli globally
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
if: steps.changed-files.outputs.all
run: pip install -e .
- name: Build service ${{ matrix.example.name }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
working-directory: ${{ matrix.example.workdir }}
- name: Build and test service A
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: |
langgraph build -t ${{ matrix.example.tag }}
- name: Test service ${{ matrix.example.name }}
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: ${{ matrix.example.workdir }}
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
# The build-arg isn't used; just testing that we accept other args
langgraph build -t langgraph-test-a --base-image "langchain/langgraph-trial"
cp .env.example .envg
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
- name: Build and test service B
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs
run: |
# Prepare environment file from local or parent example directory
if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
# Run the integration test using the built tag
REPO_ROOT=$(git rev-parse --show-toplevel)
timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
langgraph build -t langgraph-test-b --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
- name: Build and test service C
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_a
run: |
langgraph build -t langgraph-test-c --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
- name: Build and test service D
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_b
run: |
langgraph build -t langgraph-test-d --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
- name: Build JS service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: steps.changed-files.outputs.all
working-directory: libs/cli/js-examples
run: |
langgraph build -t langgraph-test-e
- name: Build JS monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/js-monorepo-example
run: |
langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
- name: Build Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/python-monorepo-example
run: |
langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
- name: Test Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/python-monorepo-example
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp apps/agent/.env.example apps/agent/.env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
- name: Build prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
run: |
langgraph build -t langgraph-test-h
- name: Test prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp ../.env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
echo "Finished starting up langgraph-test-h"
LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
if [ "$LANGGRAPH_VERSION" != "1.1.2" ]; then
echo "LANGGRAPH_VERSION != 1.1.2; $LANGGRAPH_VERSION"
exit 1
fi
LANGCHAIN_OPENAI_VERSION=$(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" != "1.0.1" ]; 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);")
if [ "$LANGCHAIN_ANTHROPIC_VERSION" != "1.0.0a5" ]; then
echo "LANGCHAIN_ANTHROPIC_VERSION != 1.0.0a5; $LANGCHAIN_ANTHROPIC_VERSION"
exit 1
fi
- name: Build and test prerelease reqs fail service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs_fail
run: |
langgraph build -t langgraph-test-i || [ $? -eq 1 ]
+50 -23
View File
@@ -8,10 +8,9 @@ on:
type: string
description: "From which folder this pipeline executes"
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
# This env var allows us to get inline annotations when ruff has complaints.
RUFF_OUTPUT_FORMAT: github
@@ -31,38 +30,57 @@ jobs:
- "3.12"
name: "lint #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "${{ inputs.working-directory }}/**"
- name: Set up Python ${{ matrix.python-version }}
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: ./.github/actions/uv_setup
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
if: steps.changed-files.outputs.all
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
cache-suffix: lint-${{ inputs.working-directory }}
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: lint-${{ inputs.working-directory }}
- name: Check Poetry File
if: steps.changed-files.outputs.all
shell: bash
working-directory: ${{ inputs.working-directory }}
run: poetry check
- name: Check lock file
if: steps.changed-files.outputs.all
shell: bash
working-directory: ${{ inputs.working-directory }}
run: poetry lock --check
- name: Install dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
# Also installs dev/lint/test/typing dependencies, to ensure we have
# type hints for as many of our libraries as possible.
# This helps catch errors that require dependencies to be spotted, for example:
# https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
#
# If you change this configuration, make sure to change the `cache-key`
# in the `poetry_setup` action above to stop using the old cache.
# It doesn't matter how you change it, any change will cause a cache-bust.
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group lint
run: poetry install --with dev
- name: Get .mypy_cache to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v3
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)) }}
key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
- name: Analysing package code with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_package > /dev/null 2>&1; then
@@ -73,22 +91,31 @@ jobs:
fi
- name: Install test dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
# Also installs dev/lint/test/typing dependencies, to ensure we have
# type hints for as many of our libraries as possible.
# This helps catch errors that require dependencies to be spotted, for example:
# https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
#
# If you change this configuration, make sure to change the `cache-key`
# in the `poetry_setup` action above to stop using the old cache.
# It doesn't matter how you change it, any change will cause a cache-bust.
working-directory: ${{ inputs.working-directory }}
run: uv sync --group lint
run: |
poetry install --with dev
- name: Get .mypy_cache_test to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v3
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)) }}
key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.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: ${{ inputs.working-directory }}
run: |
if make lint_tests > /dev/null 2>&1; then
+13 -11
View File
@@ -8,8 +8,8 @@ on:
type: string
description: "From which folder this pipeline executes"
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
jobs:
build:
@@ -17,23 +17,23 @@ jobs:
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
cache-suffix: test-${{ inputs.working-directory }}
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: test-${{ inputs.working-directory }}
- name: Login to Docker Hub
uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -42,12 +42,14 @@ jobs:
- name: Install dependencies
shell: bash
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group test --no-dev
run: |
poetry install --with dev
- name: Run tests
shell: bash
working-directory: ${{ inputs.working-directory }}
run: make test
run: |
make test
- name: Ensure the tests did not create any additional files
shell: bash
+28 -18
View File
@@ -3,8 +3,8 @@ name: test
on:
workflow_call:
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
jobs:
build:
@@ -12,26 +12,36 @@ jobs:
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
core-version:
- "latest"
ff-send-v2:
- "false"
include:
- python-version: "3.11"
core-version: ">=0.2.42,<0.3.0"
- python-version: "3.11"
core-version: "latest"
ff-send-v2: "true"
defaults:
run:
working-directory: libs/langgraph
name: "test #${{ matrix.python-version }}"
name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
cache-suffix: "test-langgraph"
working-directory: libs/langgraph
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: test-langgraph
- name: Login to Docker Hub
uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -39,18 +49,18 @@ jobs:
- name: Install dependencies
shell: bash
run: uv sync --frozen --group test --no-dev
run: |
poetry install --with dev
if [ "${{ matrix.core-version }}" != "latest" ]; then
poetry run pip install "langchain-core${{ matrix.core-version }}"
fi
- name: Run tests
shell: bash
run: make test_parallel
- name: Run strict msgpack pregel tests
if: ${{ matrix.python-version == '3.13' }}
shell: bash
env:
LANGGRAPH_STRICT_MSGPACK: "true"
run: make test TEST="tests/test_pregel.py tests/test_pregel_async.py"
LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
run: |
make test_parallel
- name: Ensure the tests did not create any additional files
shell: bash
+14 -15
View File
@@ -9,13 +9,12 @@ on:
description: "From which folder this pipeline executes"
env:
POETRY_VERSION: "1.7.1"
PYTHON_VERSION: "3.10"
permissions:
contents: read
jobs:
build:
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
outputs:
@@ -23,14 +22,14 @@ jobs:
version: ${{ steps.check-version.outputs.version }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ env.PYTHON_VERSION }}
uses: ./.github/actions/uv_setup
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ env.PYTHON_VERSION }}
cache-suffix: "release"
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: release
# We want to keep this build stage *separate* from the release stage,
# so that there's no sharing of permissions between them.
@@ -44,11 +43,11 @@ jobs:
# > from the publish job.
# https://github.com/pypa/gh-action-pypi-publish#non-goals
- name: Build project for distribution
run: uv build
run: poetry build
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
@@ -58,8 +57,8 @@ jobs:
shell: bash
working-directory: ${{ inputs.working-directory }}
run: |
echo pkg-name=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
echo version=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
publish:
needs:
@@ -74,15 +73,15 @@ jobs:
id-token: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
- name: Publish to test PyPI
uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ${{ inputs.working-directory }}/dist/
verbose: true
@@ -0,0 +1,57 @@
name: test
on:
workflow_call:
env:
POETRY_VERSION: "1.7.1"
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.11"
- "3.12"
defaults:
run:
working-directory: libs/scheduler-kafka
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: test-scheduler-kafka
- name: Login to Docker Hub
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
- name: Install dependencies
shell: bash
run: |
poetry install --with dev
- name: Run tests
shell: bash
run: |
make test
- name: Ensure the tests did not create any additional files
shell: bash
run: |
set -eu
STATUS="$(git status)"
echo "$STATUS"
# grep will exit non-zero if the target message isn't found,
# and `set -e` above will cause the step to fail.
echo "$STATUS" | grep 'nothing to commit, working tree clean'
+9 -9
View File
@@ -7,8 +7,8 @@ on:
paths:
- "libs/**"
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
jobs:
benchmark:
@@ -17,20 +17,20 @@ jobs:
run:
working-directory: libs/langgraph
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
- name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: "3.11"
cache-suffix: "bench"
working-directory: libs/langgraph
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: bench
- name: Install dependencies
run: uv sync --group test
run: poetry install --with dev
- name: Run benchmarks
run: OUTPUT=out/benchmark-baseline.json make -s benchmark
- name: Save outputs
uses: actions/cache/save@v5
uses: actions/cache/save@v4
with:
key: ${{ runner.os }}-benchmark-baseline-${{ env.SHA }}
path: |
+13 -13
View File
@@ -5,8 +5,8 @@ on:
paths:
- "libs/**"
permissions:
contents: read
env:
POETRY_VERSION: "1.7.1"
jobs:
benchmark:
@@ -15,22 +15,22 @@ jobs:
run:
working-directory: libs/langgraph
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- id: files
name: Get changed files
uses: Ana06/get-changed-files@25f79e676e7ea1868813e21465014798211fad8c # v2.3.0
uses: Ana06/get-changed-files@v2.3.0
with:
format: json
- name: Set up Python 3.11
uses: ./.github/actions/uv_setup
- name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: "3.11"
cache-suffix: "bench"
working-directory: libs/langgraph
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: bench
- name: Install dependencies
run: uv sync --group test
run: poetry install --with dev
- name: Download baseline
uses: actions/cache/restore@v5
uses: actions/cache/restore@v4
with:
key: ${{ runner.os }}-benchmark-baseline
restore-keys: |
@@ -43,7 +43,7 @@ jobs:
run: |
{
echo 'OUTPUT<<EOF'
make -s benchmark-fast
make -s benchmark
echo EOF
} >> "$GITHUB_OUTPUT"
- name: Compare benchmarks
@@ -53,11 +53,11 @@ jobs:
echo 'OUTPUT<<EOF'
mv out/benchmark-baseline.json out/main.json
mv out/benchmark.json out/changes.json
uv run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
poetry run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
echo EOF
} >> "$GITHUB_OUTPUT"
- name: Annotation
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]
+43 -98
View File
@@ -2,15 +2,9 @@
name: CI
on:
workflow_dispatch:
push:
branches:
- main
branches: [main]
pull_request:
permissions:
contents: read
# If another push to the same PR or branch happens while this workflow is still running,
# cancel the earlier run in favor of the next run.
@@ -22,34 +16,11 @@ concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
changes:
runs-on: ubuntu-latest
outputs:
python: ${{ steps.filter.outputs.python || 'true' }}
deps: ${{ steps.filter.outputs.deps || 'true' }}
steps:
- uses: actions/checkout@v6
- uses: dorny/paths-filter@fbd0ab8f3e69293af611ebaee6363fc25e6d187d # v4
if: github.event_name != 'workflow_dispatch'
id: filter
with:
filters: |
python:
- 'libs/langgraph/**'
- 'libs/sdk-py/**'
- 'libs/cli/**'
- 'libs/checkpoint/**'
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/checkpoint-conformance/**'
- 'libs/prebuilt/**'
deps:
- '**/pyproject.toml'
- '**/uv.lock'
env:
POETRY_VERSION: "1.7.1"
jobs:
lint:
needs: changes
name: cd ${{ matrix.working-directory }}
strategy:
matrix:
@@ -60,32 +31,26 @@ jobs:
"libs/cli",
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-duckdb",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/scheduler-kafka",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
uses: ./.github/workflows/_lint.yml
with:
working-directory: ${{ matrix.working-directory }}
secrets: inherit
test:
needs: changes
name: cd ${{ matrix.working-directory }}
strategy:
matrix:
working-directory:
[
working-directory: [
"libs/cli",
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/sdk-py",
"libs/checkpoint-duckdb",
"libs/checkpoint-postgres"
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
uses: ./.github/workflows/_test.yml
with:
working-directory: ${{ matrix.working-directory }}
@@ -93,80 +58,60 @@ jobs:
# NOTE: we're testing langgraph separately because it requires a different matrix
test-langgraph:
needs: changes
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
name: "cd libs/langgraph"
uses: ./.github/workflows/_test_langgraph.yml
secrets: inherit
# NOTE: we're testing scheduler-kafka separately because it requires a different matrix
test-scheduler-kafka:
name: "cd libs/scheduler-kafka"
uses: ./.github/workflows/_test_scheduler_kafka.yml
secrets: inherit
check-sdk-methods:
needs: changes
if: needs.changes.outputs.python == 'true'
name: "Check SDK methods matching"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v6
uses: actions/setup-python@v5
with:
python-version: "3.11"
python-version: '3.11'
- name: Run check_sdk_methods script
run: python .github/scripts/check_sdk_methods.py
check-schema:
needs: changes
if: needs.changes.outputs.python == 'true'
name: "Check CLI schema hasn't changed #${{ matrix.python-version }}"
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.13"
steps:
- uses: actions/checkout@v6
- name: Set up Python ${{ matrix.python-version }}
uses: ./.github/actions/uv_setup
with:
python-version: "3.13"
cache-suffix: "schema-check-cli"
working-directory: libs/cli
- name: Install CLI dependencies
run: |
cd libs/cli
uv sync
- name: Generate schema and check for changes
run: |
cd libs/cli
# Create a temporary copy of the current schema
cp schemas/schema.json schemas/schema.current.json
# Generate new schema
uv run python generate_schema.py
# Compare the new schema with the original
if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
echo "Error: Langgraph.json configuration schema has changed. Please run 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
diff schemas/schema.json schemas/schema.current.json
exit 1
fi
echo "Schema check passed - no changes detected"
integration-test:
needs: changes
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
name: CLI integration test
uses: ./.github/workflows/_integration_test.yml
secrets: inherit
lint-js:
runs-on: ubuntu-latest
strategy:
matrix:
working-directory:
- "libs/sdk-js"
defaults:
run:
working-directory: ${{ matrix.working-directory }}
steps:
- uses: actions/checkout@v3
- name: Setup Node.js (LTS)
uses: actions/setup-node@v3
with:
node-version: "20"
cache: "yarn"
cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
- name: Install dependencies
run: yarn install
- name: Run lint
run: yarn lint
- name: Build
run: yarn build
ci_success:
name: "CI Success"
needs:
[
lint,
test,
test-langgraph,
check-sdk-methods,
check-schema,
integration-test,
]
needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
if: |
always()
runs-on: ubuntu-latest
+39
View File
@@ -0,0 +1,39 @@
---
name: CI / cd . / make spell_check
on:
push:
branches: [main]
pull_request:
branches: [main]
permissions:
contents: read
jobs:
codespell:
name: (Check for spelling errors)
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Install Dependencies
run: |
pip install toml codespell jupytext
- name: Extract Ignore Words List
run: |
# Use a Python script to extract the ignore words list from pyproject.toml
python .github/workflows/extract_ignored_words_list.py
id: extract_ignore_words
- name: Codespell
uses: codespell-project/actions-codespell@v2
with:
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib'
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
# We do this to avoid spellchecking cell outputs
- name: Codespell Notebooks
run: make codespell
-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@v5
- name: Upload artifact
uses: actions/upload-pages-artifact@v4
with:
path: 'docs/_site'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v5
+132
View File
@@ -0,0 +1,132 @@
name: Deploy Docs
on:
push:
branches:
- main
pull_request:
branches:
- main
workflow_dispatch:
env:
POETRY_VERSION: "1.7.1"
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: "pages"
cancel-in-progress: false
jobs:
get-changed-files:
runs-on: ubuntu-latest
outputs:
changed-files: ${{ steps.changed-files.outputs.added_modified }}
steps:
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "docs/docs/**"
run-changed-notebooks:
needs: get-changed-files
uses: ./.github/workflows/run_notebooks.yml
secrets: inherit
with:
changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
deploy:
# needs: run-changed-notebooks
runs-on: ubuntu-latest
env:
GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: "3.12"
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: docs
- name: Install dependencies
run: |
poetry install --with test --no-root
poetry run pip install -U \
pytest \
pytest-check-links \
langsmith \
langchain \
GitPython \
"git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
- name: Lint Docs
# This step lints the docs using the existing linting set up.
# It should be very fast and should not require any external services.
run: make lint-docs
- name: Build site
run: make build-docs
env:
MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
- name: Check links in notebooks
env:
LANGCHAIN_API_KEY: test
run: |
if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
echo "Running link check on all HTML files matching notebooks in docs directory..."
poetry run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links-ignore "https://python\.langchain\.com/.*" \
--check-links-ignore "https://openai\.com/.*" \
--check-links-ignore "https://pepy\.tech/.*" \
--check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html')
else
echo "Fetching changes from origin/main..."
git fetch origin main
echo "Checking for changed notebook files..."
CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
echo "Changed files: ${CHANGED_FILES}"
if [ -n "${CHANGED_FILES}" ]; then
echo "Running link check on HTML files matching changed notebook files..."
poetry run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links ${CHANGED_FILES} \
|| ([ $? = 5 ] && exit 0 || exit $?)
else
echo "No notebook files changed."
fi
fi
- name: Configure GitHub Pages
if: github.ref == 'refs/heads/main'
uses: actions/configure-pages@v4
- name: Upload Pages Artifact
if: github.ref == 'refs/heads/main'
uses: actions/upload-pages-artifact@v3
with:
path: ./docs/site/
- name: Deploy to GitHub Pages
if: github.ref == 'refs/heads/main'
id: deployment
uses: actions/deploy-pages@v4
@@ -0,0 +1,10 @@
import toml
pyproject_toml = toml.load("libs/langgraph/pyproject.toml")
# Extract the ignore words list (adjust the key as per your TOML structure)
ignore_words_list = (
pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
)
print(f"::set-output name=ignore_words_list::{ignore_words_list}") # noqa: T201
+49
View File
@@ -0,0 +1,49 @@
name: Check Docs & Links
on:
pull_request:
branches:
- main
push:
branches:
- main
schedule:
- cron: "0 5 * * *"
workflow_dispatch:
env:
POETRY_VERSION: "1.7.1"
jobs:
markdown-link-check:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Check links in Markdown files
uses: gaurav-nelson/github-action-markdown-link-check@v1
with:
folder-path: "docs/"
check-modified-files-only: ${{ github.event_name != 'schedule' }}
file-path: "./README.md"
config-file: "./.markdown-link-check.config.json"
check-readmes-synced:
# This checks that the repo README.md is identical to the libs/langgraph/README.md
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 1
- name: Check README.md is in sync
run: |
if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
echo "README.md is out of sync with libs/langgraph/README.md"
diff -C 3 README.md libs/langgraph/README.md
exit 1
fi
-46
View File
@@ -1,46 +0,0 @@
name: PR Title Lint
permissions:
pull-requests: read
on:
pull_request:
types: [opened, edited, synchronize]
jobs:
lint-pr-title:
runs-on: ubuntu-latest
steps:
- name: Validate PR Title
uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
types: |
feat
fix
docs
style
refactor
perf
test
build
ci
chore
revert
release
scopes: |
checkpoint
checkpoint-postgres
checkpoint-sqlite
cli
langgraph
prebuilt
scheduler-kafka
sdk-py
docs
ci
deps
requireScope: false
ignoreLabels: |
ignore-lint-pr-title
+37 -52
View File
@@ -8,14 +8,13 @@ on:
type: string
default: "libs/langgraph"
permissions:
contents: read
env:
PYTHON_VERSION: "3.11"
POETRY_VERSION: "1.7.1"
jobs:
build:
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
outputs:
@@ -25,14 +24,14 @@ jobs:
tag: ${{ steps.check-version.outputs.tag }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ env.PYTHON_VERSION }}
cache-suffix: "release"
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: release
# We want to keep this build stage *separate* from the release stage,
# so that there's no sharing of permissions between them.
@@ -46,11 +45,11 @@ jobs:
# > from the publish job.
# https://github.com/pypa/gh-action-pypi-publish#non-goals
- name: Build project for distribution
run: uv build
run: poetry build
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -60,14 +59,8 @@ jobs:
shell: bash
working-directory: ${{ inputs.working-directory }}
run: |
PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
if grep -q 'dynamic.*=.*\[.*"version".*\]' pyproject.toml; then
# handle dynamic versioning
DIR_NAME=$(echo "$PKG_NAME" | tr '-' '_')
VERSION=$(grep -m 1 '^__version__' "${DIR_NAME}/__init__.py" | cut -d '"' -f 2)
else
VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
fi
PKG_NAME="$(poetry version | cut -d ' ' -f 1)"
VERSION="$(poetry version --short)"
SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
if [ -z $SHORT_PKG_NAME ]; then
TAG="$VERSION"
@@ -86,7 +79,7 @@ jobs:
outputs:
release-body: ${{ steps.generate-release-body.outputs.release-body }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
repository: langchain-ai/langgraph
path: langgraph
@@ -142,9 +135,7 @@ jobs:
needs:
- build
- release-notes
permissions:
contents: read
id-token: write
permissions: write-all
uses: ./.github/workflows/_test_release.yml
with:
working-directory: ${{ inputs.working-directory }}
@@ -157,7 +148,7 @@ jobs:
- test-pypi-publish
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
# We explicitly *don't* set up caching here. This ensures our tests are
# maximally sensitive to catching breakage.
@@ -172,12 +163,11 @@ jobs:
# - The package is published, and it breaks on the missing dependency when
# used in the real world.
- name: Set up Python
uses: ./.github/actions/uv_setup
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: false
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
- name: Import published package
shell: bash
@@ -195,21 +185,17 @@ jobs:
# - attempt install again after 5 seconds if it fails because there is
# sometimes a delay in availability on test pypi
run: |
uv run pip install \
poetry run pip install \
--extra-index-url https://test.pypi.org/simple/ \
"$PKG_NAME==$VERSION" || \
( \
sleep 5 && \
uv run pip install \
poetry run pip install \
--extra-index-url https://test.pypi.org/simple/ \
"$PKG_NAME==$VERSION" \
)
if [[ "$PKG_NAME" == *prebuilt* ]]; then
uv run pip install langgraph
fi
if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
if [[ "$PKG_NAME" == *checkpoint* ]]; then
# since checkpoint packages are namespace packages, import them with . convention
# i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
@@ -219,10 +205,10 @@ jobs:
IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
fi
uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
- name: Import test dependencies
run: uv sync --group test
run: poetry install --with dev
working-directory: ${{ inputs.working-directory }}
# Overwrite the local version of the package with the test PyPI version.
@@ -233,7 +219,7 @@ jobs:
PKG_NAME: ${{ needs.build.outputs.pkg-name }}
VERSION: ${{ needs.build.outputs.version }}
run: |
uv run pip install \
poetry run pip install \
--extra-index-url https://test.pypi.org/simple/ \
"$PKG_NAME==$VERSION"
@@ -261,22 +247,22 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ env.PYTHON_VERSION }}
cache-suffix: "release"
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: release
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
- name: Publish package distributions to PyPI
uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ${{ inputs.working-directory }}/dist/
verbose: true
@@ -302,27 +288,26 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: ./.github/actions/uv_setup
- name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ env.PYTHON_VERSION }}
cache-suffix: "release"
working-directory: ${{ inputs.working-directory }}
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: release
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
- name: Create Tag
uses: ncipollo/release-action@339a81892b84b4eeb0f6e744e4574d79d0d9b8dd # v1
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
generateReleaseNotes: false
tag: ${{needs.build.outputs.tag}}
name: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
body: ${{ needs.release-notes.outputs.release-body }}
commit: ${{ github.sha }}
+38
View File
@@ -0,0 +1,38 @@
name: JS Release
on:
workflow_dispatch:
jobs:
publish:
# Disallow publishing from branches that aren't `main`.
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
strategy:
matrix:
working-directory:
- "libs/sdk-js"
defaults:
run:
working-directory: ${{ matrix.working-directory }}
steps:
- uses: actions/checkout@v4
# JS Build
- name: Use Node.js
uses: actions/setup-node@v3
with:
node-version: "20"
cache: "yarn"
cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
- name: Install dependencies
run: yarn install
- name: Build
run: yarn build
- name: Publish package to NPM
run: |
echo "//registry.npmjs.org/:_authToken=${{ secrets.NPM_TOKEN }}" > .npmrc
npm publish
-161
View File
@@ -1,161 +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:
pull-requests: write
steps:
- name: Find and reopen matching PRs
uses: actions/github-script@v8
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}`);
}
}
-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@v8
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}): ${error.message}`,
);
}
}
}
core.setOutput('is-assigned', assignedToAny ? 'true' : 'false');
- name: Add missing-issue-link label
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
(steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
uses: actions/github-script@v8
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
const labelName = 'missing-issue-link';
// Ensure the label exists (no checkout/shared helper available)
try {
await github.rest.issues.getLabel({ owner, repo, name: labelName });
} catch (e) {
if (e.status !== 404) throw e;
try {
await github.rest.issues.createLabel({
owner, repo, name: labelName, color: 'b76e79',
});
} catch (createErr) {
if (createErr.status !== 422) throw createErr;
}
}
await github.rest.issues.addLabels({
owner, repo, issue_number: prNumber, labels: [labelName],
});
- name: Remove missing-issue-link label and reopen PR
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
steps.check-link.outputs.has-link == 'true' && steps.check-link.outputs.is-assigned == 'true'
uses: actions/github-script@v8
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
try {
await github.rest.issues.removeLabel({
owner, repo, issue_number: prNumber, name: 'missing-issue-link',
});
} catch (error) {
if (error.status !== 404) throw error;
}
// Reopen if this workflow previously closed the PR. We check the
// event payload labels (not live labels) because we already removed
// missing-issue-link above; the payload still reflects pre-step state.
const labels = context.payload.pull_request.labels.map(l => l.name);
if (context.payload.pull_request.state === 'closed' && labels.includes('missing-issue-link')) {
await github.rest.pulls.update({
owner,
repo,
pull_number: prNumber,
state: 'open',
});
console.log(`Reopened PR #${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}: ${e.message}`);
}
- name: Post comment, close PR, and fail
if: >-
env.ENFORCE_ISSUE_LINK == 'true' &&
(steps.check-link.outputs.has-link != 'true' || steps.check-link.outputs.is-assigned != 'true')
uses: actions/github-script@v8
with:
script: |
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
const hasLink = '${{ 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}: ${err.message}`);
}
}
}
const reason = !hasLink
? 'PR must reference an issue using auto-close keywords (e.g., "Fixes #123").'
: 'PR author must be assigned to the linked issue.';
core.setFailed(reason);
+78
View File
@@ -0,0 +1,78 @@
name: Run notebooks
on:
workflow_dispatch:
workflow_call:
inputs:
changed-files:
required: false
type: string
description: "JSON string of changed files"
schedule:
- cron: '0 13 * * *'
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
lib-version:
- "development"
- "latest"
steps:
- uses: actions/checkout@v4
- name: Set up Python + Poetry
uses: "./.github/actions/poetry_setup"
with:
python-version: 3.11
poetry-version: 1.7.1
cache-key: test-langgraph-notebooks
- name: Install dependencies
run: |
poetry install --with test
poetry run pip install jupyter
- name: Start services
run: make start-services
- name: Pre-download tiktoken files
run: |
poetry run python docs/_scripts/download_tiktoken.py
- name: Prepare notebooks
run: |
if [ "${{ matrix.lib-version }}" = "development" ]; then
poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
else
poetry run python docs/_scripts/prepare_notebooks_for_ci.py
fi
- name: Run notebooks
env:
# these won't actually be used because of the VCR cassettes
# but need to set them to avoid triggering getpass()
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
echo "Running all notebooks"
./docs/_scripts/execute_notebooks.sh
else
CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
if [ -n "$CHANGED_FILES" ]; then
echo "Running changed notebooks: $CHANGED_FILES"
./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
else
echo "No notebook files changed, skipping execution"
fi
fi
- name: Stop services
run: make stop-services
+29
View File
@@ -0,0 +1,29 @@
name: Check File Size
on:
push:
branches:
- main
pull_request:
branches:
- main
workflow_dispatch:
jobs:
file-size-check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
uses: tj-actions/changed-files@v44
- name: Filter by size
# TODO: roll back the web voyager hack
run: |
large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M | grep -v "web_voyager" || true)
if [ -n "$large_added_files" ]; then
echo "Large files added: $large_added_files"
echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
exit 1
fi
-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@v8
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@v8
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@v8
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@v8
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@v8
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@v8
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@v8
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@v8
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@v8
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}`);
-43
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@@ -1,43 +0,0 @@
name: UV Lock Upgrade
on:
schedule:
# run at midnight every Sunday
- cron: '0 0 * * 0'
# allow manual triggering
workflow_dispatch:
permissions:
contents: write
pull-requests: write
jobs:
upgrade-dependencies:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Set up uv
uses: ./.github/actions/uv_setup
with:
python-version: "3.10"
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@c0f553fe549906ede9cf27b5156039d195d2ece0 # v8
with:
token: ${{ secrets.GITHUB_TOKEN }}
commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
title: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
body: |
This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
branch: deps/uv-lock-upgrade
delete-branch: true
labels: |
dependencies
+83 -4
View File
@@ -6,6 +6,9 @@ __pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
@@ -51,12 +54,27 @@ coverage.xml
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
docs/docs/_build/
# PyBuilder
target/
@@ -71,9 +89,23 @@ ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.envrc
@@ -85,6 +117,16 @@ ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
@@ -96,7 +138,44 @@ dmypy.json
# macOS display setting files
.DS_Store
.vercel
.turbo
.editorconfig
.scratch
# Wandb directory
wandb/
# asdf tool versions
.tool-versions
/.ruff_cache/
*.pkl
*.bin
# integration test artifacts
data_map*
\[('_type', 'fake'), ('stop', None)]
# Replit files
*replit*
node_modules
docs/.yarn/
docs/node_modules/
docs/.docusaurus/
docs/.cache-loader/
docs/_dist
docs/api_reference/api_reference.rst
docs/api_reference/experimental_api_reference.rst
docs/api_reference/_build
docs/api_reference/*/
!docs/api_reference/_static/
!docs/api_reference/templates/
!docs/api_reference/themes/
docs/docs_skeleton/build
docs/docs_skeleton/node_modules
docs/docs_skeleton/yarn.lock
# Any new jupyter notebooks
# not intended for the repo
Untitled*.ipynb
Chinook.db
libs/langgraph/out
+4
View File
@@ -0,0 +1,4 @@
{
"aliveStatusCodes": [200, 206, 402],
"ignorePatterns": ["*dcbadge.vercel.app*"]
}
-14
View File
@@ -1,14 +0,0 @@
{
"MD013": false,
"MD024": {
"siblings_only": true
},
"MD025": false,
"MD033": false,
"MD034": false,
"MD036": false,
"MD041": false,
"MD046": {
"style": "fenced"
}
}
-57
View File
@@ -1,57 +0,0 @@
# AGENTS Instructions
This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
When you modify code in any library, run the following commands in that library's directory before creating a pull request:
- `make format` run code formatters
- `make lint` run the linter
- `make test` execute the test suite
To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
```txt
TEST=path/to/test.py make test
```
Other pytest arguments can also be supplied inside the `TEST` variable.
## Libraries
The repository contains several Python and JavaScript/TypeScript libraries.
Below is a high-level overview:
- **checkpoint** base interfaces for LangGraph checkpointers.
- **checkpoint-postgres** Postgres implementation of the checkpoint saver.
- **checkpoint-sqlite** SQLite implementation of the checkpoint saver.
- **cli** official command-line interface for LangGraph.
- **langgraph** core framework for building stateful, multi-actor agents.
- **prebuilt** high-level APIs for creating and running agents and tools.
- **sdk-js** JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** Python SDK for the LangGraph Server API.
### Dependency map
The diagram below lists downstream libraries for each production dependency as
declared in that library's `pyproject.toml` (or `package.json`).
```text
checkpoint
├── checkpoint-postgres
├── checkpoint-sqlite
├── prebuilt
└── langgraph
prebuilt
└── langgraph
sdk-py
├── langgraph
└── cli
sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
-57
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@@ -1,57 +0,0 @@
# AGENTS Instructions
This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
When you modify code in any library, run the following commands in that library's directory before creating a pull request:
- `make format` run code formatters
- `make lint` run the linter
- `make test` execute the test suite
To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
```
TEST=path/to/test.py make test
```
Other pytest arguments can also be supplied inside the `TEST` variable.
## Libraries
The repository contains several Python and JavaScript/TypeScript libraries.
Below is a high-level overview:
- **checkpoint** base interfaces for LangGraph checkpointers.
- **checkpoint-postgres** Postgres implementation of the checkpoint saver.
- **checkpoint-sqlite** SQLite implementation of the checkpoint saver.
- **cli** official command-line interface for LangGraph.
- **langgraph** core framework for building stateful, multi-actor agents.
- **prebuilt** high-level APIs for creating and running agents and tools.
- **sdk-js** JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** Python SDK for the LangGraph Server API.
### Dependency map
The diagram below lists downstream libraries for each production dependency as
declared in that library's `pyproject.toml` (or `package.json`).
```text
checkpoint
├── checkpoint-postgres
├── checkpoint-sqlite
├── prebuilt
└── langgraph
prebuilt
└── langgraph
sdk-py
├── langgraph
└── cli
sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
+293
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@@ -0,0 +1,293 @@
# Contributing to LangGraph
Thank you for being interested in contributing to LangGraph!
## General guidelines
Here are some things to keep in mind for all types of contributions:
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
- If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
- Look for duplicate PRs or issues that have already been opened before opening a new one.
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
### Bugfixes
For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
### New features
For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
## Contribute Documentation
Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
community improvements to our current documentation. Please read the resources below before getting started:
- [Documentation style guide](#documentation-style-guide)
- [Documentation setup](#setup)
## Documentation Style Guide
As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
## Philosophy
LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
[How-to guides](#how-to-guides),
[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
They should **avoid** giving
multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
To quote the Diataxis website:
> A tutorial serves the users *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
In LangGraph, these are often higher level guides that show off end-to-end use cases.
Some examples include:
- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
Here are some high-level tips on writing a good tutorial:
- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
- Be specific, not abstract and follow one path.
- No need to go deeply into alternative approaches, but its ok to reference them, ideally with a link to an appropriate how-to guide.
- Get "a point on the board" as soon as possible - something the user can run that outputs something.
- You can iterate and expand afterwards.
- Try to frequently checkpoint at given steps where the user can run code and see progress.
- Focus on results, not technical explanation.
- Crosslink heavily to appropriate conceptual/reference pages
- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
- It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
- Do not shy away from repetition.
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
should still give some background or list the scenarios where the information contained within can be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
Some examples include:
- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
Here are some high-level tips on writing a good how-to guide:
- Clearly explain what you are guiding the user through at the start
- Assume higher intent than a tutorial and show what the user needs to do to get that task done
- Assume familiarity of concepts, but explain why suggested actions are helpful
- Crosslink heavily to conceptual/reference pages
- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
### Conceptual guides
LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
To quote the Diataxis website:
> The perspective of explanation is higher and wider than that of the other types. It does not take the users eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
Some examples include:
- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
- [Tool calling](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling)
Here are some high-level tips on writing a good conceptual guide:
- Explain design decisions. Why does concept X exist and why was it designed this way?
- Use analogies and reference other concepts and alternatives
- Avoid blending in too much reference content
- You can and should reference content covered in other guides, but make sure to link to them
### References
References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
how to use something specific.
To quote the Diataxis website:
> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
Many of the reference pages in LangChain are automatically generated from code,
but here are some high-level tips on writing a good docstring:
- Be concise
- Discuss special cases and deviations from a user's expectations
- Go into detail on required inputs and outputs
- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## General guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
We generally do not merge new tutorials from outside contributors without an actue need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
## Setup
LangChain documentation consists of two components:
1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
this comprehensive resource serves as the primary user-facing documentation.
It covers a wide array of topics, including tutorials, use cases, integrations,
and more, offering extensive guidance on building with LangGraph.
The content for this documentation lives in the `/docs` directory of the monorepo.
2. In-code Documentation: This is documentation of the codebase itself, which is also
used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
We appreciate all contributions to the documentation, whether it be fixing a typo,
adding a new tutorial or example and whether it be in the main documentation or the API Reference.
### 📜 Main Documentation
The content for the main documentation is located in the `/docs` directory of the monorepo.
The documentation is written using a combination of ipython notebooks (`.ipynb` files)
and markdown (`.md` files). The notebooks are converted to markdown
and then built using [MkDocs](https://www.mkdocs.org/).
Feel free to make contributions to the main documentation! 🥰
After modifying the documentation:
1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
2. Optionally build the documentation locally to verify that the changes look good.
3. Make a pull request with the changes.
### ⚒️ Linting and Building Documentation Locally
After writing up the documentation, you may want to lint and build the documentation
locally to ensure that it looks good and is free of errors.
If you're unable to build it locally that's okay as well, as you will be able to
see a preview of the documentation on the pull request page.
From the **monorepo root**, run the following command to install the dependencies:
```bash
poetry install --with docs --no-root
```
#### Building
The code that builds the documentation is located in the `/docs` directory of the monorepo.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make clean-docs
```
You can build and preview the documentation as outlined below:
```bash
make serve-docs
```
#### Linting
The documentation is linted from the **monorepo root**. To lint it, run the following from there:
```bash
make spellcheck
```
### In-code Documentation
The in-code documentation is autogenerated from docstrings.
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
Here is an example of a well-documented function:
```python
def my_function(arg1: int, arg2: str) -> float:
"""This is a short description of the function. (It should be a single sentence.)
This is a longer description of the function. It should explain what
the function does, what the arguments are, and what the return value is.
It should wrap at 88 characters.
Examples:
This is a section for examples of how to use the function.
.. code-block:: python
my_function(1, "hello")
Args:
arg1: This is a description of arg1. We do not need to specify the type since
it is already specified in the function signature.
arg2: This is a description of arg2.
Returns:
This is a description of the return value.
"""
return 3.14
```
+32 -61
View File
@@ -1,68 +1,39 @@
# Define the directories containing projects
LIBS_DIRS := $(wildcard libs/*)
.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
# Default target
.PHONY: all
all: lint format lock test
build-typedoc:
cd libs/sdk-js && yarn install --include-dev && yarn typedoc
cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
# Add links to the monorepo
sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
# Install dependencies for all projects
.PHONY: install
install:
@echo "Creating virtual environment..."
@uv venv
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/pyproject.toml ]; then \
echo "Installing dependencies for $$dir"; \
uv pip install -e $$dir; \
fi; \
done
build-docs: build-typedoc
poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
# Lint all projects
.PHONY: lint
lint:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running lint in $$dir"; \
$(MAKE) -C $$dir lint; \
fi; \
done
serve-clean-docs: clean-docs
poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
# Format all projects
.PHONY: format
format:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running format in $$dir"; \
$(MAKE) -C $$dir format; \
fi; \
done
serve-docs: build-typedoc
poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph -w ./libs/checkpoint --dirty
# Lock all projects
.PHONY: lock
lock:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running lock in $$dir"; \
(cd $$dir && uv lock); \
fi; \
done
clean-docs:
find ./docs/docs -name "*.ipynb" -type f -delete
rm -rf docs/site
# Lock all projects and upgrade dependencies
.PHONY: lock-upgrade
lock-upgrade:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running lock-upgrade in $$dir"; \
(cd $$dir && uv lock --upgrade); \
fi; \
done
## Run format against the project documentation.
format-docs:
poetry run ruff format docs/docs
poetry run ruff check --fix docs/docs
# Test all projects
.PHONY: test
test:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running test in $$dir"; \
$(MAKE) -C $$dir test; \
fi; \
done
# Check the docs for linting violations
lint-docs:
poetry run ruff format --check docs/docs
poetry run ruff check docs/docs
codespell:
./docs/codespell_notebooks.sh .
start-services:
docker compose -f docs/test-compose.yml up -V --force-recreate --wait --remove-orphans
stop-services:
docker compose -f docs/test-compose.yml down
+222 -59
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@@ -1,82 +1,245 @@
<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>
# 🦜🕸️LangGraph
<div align="center">
<h3>Low-level orchestration framework for building stateful agents.</h3>
</div>
![Version](https://img.shields.io/pypi/v/langgraph)
[![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
<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" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
</div>
⚡ Building language agents as graphs ⚡
<br>
> [!NOTE]
> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
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.
## Overview
```bash
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
### Key Features
- **Cycles and Branching**: Implement loops and conditionals in your apps.
- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
### LangGraph Platform
LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
- **Background runs**: Runs agents asynchronously in the background
- **Support for long running agents**: Infrastructure that can handle long running processes
- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
## Installation
```shell
pip install -U langgraph
```
If you're looking to quickly build agents with LangChain's `create_agent` (built on LangGraph), check out the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
## Example
> [!NOTE]
> Looking for the 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).
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
## Why use LangGraph?
Let's take a look at a simple example of an agent that can use a search tool.
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
```shell
pip install langchain-anthropic
```
- **[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.
```shell
export ANTHROPIC_API_KEY=sk-...
```
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
## LangGraph ecosystem
```shell
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=lsv2_sk_...
```
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
```python
from typing import Annotated, Literal, TypedDict
To improve your LLM application development, pair LangGraph with:
from langchain_core.messages import HumanMessage
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) Provides integrations and composable components to streamline LLM application development.
- [LangSmith](https://www.langchain.com/langsmith) Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
---
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
messages = state['messages']
last_message = messages[-1]
# If the LLM makes a tool call, then we route to the "tools" node
if last_message.tool_calls:
return "tools"
# Otherwise, we stop (reply to the user)
return END
# Define the function that calls the model
def call_model(state: MessagesState):
messages = state['messages']
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools", 'agent')
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable.
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)
# Use the Runnable
final_state = app.invoke(
{"messages": [HumanMessage(content="what is the weather in sf")]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
```python
final_state = app.invoke(
{"messages": [HumanMessage(content="what about ny")]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
```
### Step-by-step Breakdown
1. <details>
<summary>Initialize the model and tools.</summary>
- we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
- we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
</details>
2. <details>
<summary>Initialize graph with state.</summary>
- we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
- `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
</details>
3. <details>
<summary>Define graph nodes.</summary>
There are two main nodes we need:
- The `agent` node: responsible for deciding what (if any) actions to take.
- The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
</details>
4. <details>
<summary>Define entry point and graph edges.</summary>
First, we need to set the entry point for graph execution - `agent` node.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
- Conditional edge: after the agent is called, we should either:
- a. Run tools if the agent said to take an action, OR
- b. Finish (respond to the user) if the agent did not ask to run tools
- Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
</details>
5. <details>
<summary>Compile the graph.</summary>
- When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
- We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
</details>
6. <details>
<summary>Execute the graph.</summary>
1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
2. The `"agent"` node executes, invoking the chat model.
3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
- If `AIMessage` has `tool_calls`, `"tools"` node executes
- The `"agent"` node executes again and returns `AIMessage`
5. Execution progresses to the special `END` value and outputs the final state.
And as a result, we get a list of all our chat messages as output.
</details>
## 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
* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
## Contributing
## 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.
---
## Acknowledgements
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
+2 -1
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_site/
site/
docs/cloud/reference/sdk/js_ts_sdk_ref.md
+61
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@@ -0,0 +1,61 @@
# Setup
To setup requirements for building docs you can run:
```bash
poetry install --with test
```
## Serving documentation locally
To run the documentation server locally you can run:
```bash
make serve-docs
```
## Execute notebooks
If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
```bash
python docs/_scripts/prepare_notebooks_for_ci.py
./docs/_scripts/execute_notebooks.sh
```
**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
```bash
python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
./docs/_scripts/execute_notebooks.sh
```
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
## Adding new notebooks
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
Then, run
```bash
jupyter execute <path_to_notebook>
```
Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
## Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
To delete cassettes for a notebook, you can run:
```bash
rm docs/cassettes/<notebook_name>*
```
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import tiktoken
# This will trigger the download and caching of the necessary files
for encoding in ("gpt2", "gpt-3.5"):
tiktoken.encoding_for_model(encoding)
+36
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@@ -0,0 +1,36 @@
#!/bin/bash
# Read the list of notebooks to skip from the JSON file
SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
# Function to execute a single notebook
execute_notebook() {
file="$1"
echo "Starting execution of $file"
start_time=$(date +%s)
if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Error in $file. Execution time: $execution_time seconds"
echo "Error details: $output"
exit 1
fi
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Finished $file. Execution time: $execution_time seconds"
}
export -f execute_notebook
# Check if custom notebook paths are provided
if [ $# -gt 0 ]; then
notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
else
# Find all notebooks and filter out those in the skip list
notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
fi
# Execute notebooks sequentially
for file in $notebooks; do
execute_notebook "$file"
done
@@ -0,0 +1,246 @@
import importlib
import inspect
import logging
import os
import re
from typing import List, Literal, Optional
from typing_extensions import TypedDict
import nbformat
from nbconvert.preprocessors import Preprocessor
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Base URL for all class documentation
_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
# (alias/re-exported modules, source module, class, docs namespace)
MANUAL_API_REFERENCES_LANGGRAPH = [
(
["langgraph.prebuilt"],
"langgraph.prebuilt.chat_agent_executor",
"create_react_agent",
"prebuilt",
),
(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"tools_condition",
"prebuilt",
),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"InjectedState",
"prebuilt",
),
# Graph
(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
([], "langgraph.types", "StreamMode", "types"),
(["langgraph.graph"], "langgraph.constants", "START", "constants"),
(["langgraph.graph"], "langgraph.constants", "END", "constants"),
(["langgraph.constants"], "langgraph.types", "Send", "types"),
(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
([], "langgraph.types", "RetryPolicy", "types"),
([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
]
WELL_KNOWN_LANGGRAPH_OBJECTS = {
(module_, class_): (source_module, namespace)
for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
for module_ in modules + [source_module]
}
def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
if not pkg_prefix.isidentifier():
raise ValueError(f"Invalid package prefix: {pkg_prefix}")
return re.compile(
r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
r"((?:\w+(?:,\s*)?)*" # Match zero or more words separated by a comma+optional ws
r"(?:\s*\(.*?\))?)", # Match optional parentheses block
re.DOTALL, # Match newlines as well
)
# Regular expression to match langchain import lines
_IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
_IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
def _get_full_module_name(module_path, class_name) -> Optional[str]:
"""Get full module name using inspect"""
try:
module = importlib.import_module(module_path)
class_ = getattr(module, class_name)
module = inspect.getmodule(class_)
if module is None:
# For constants, inspect.getmodule() might return None
# In this case, we'll return the original module_path
return module_path
return module.__name__
except AttributeError as e:
logger.warning(f"Could not find module for {class_name}, {e}")
return None
except ImportError as e:
logger.warning(f"Failed to load for class {class_name}, {e}")
return None
def _get_doc_title(data: str, file_name: str) -> str:
try:
return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
except IndexError:
pass
# Parse the rst-style titles
try:
return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
except IndexError:
return file_name
class ImportInformation(TypedDict):
imported: str # imported class name
source: str # module path
docs: str # URL to the documentation
title: str # Title of the document
def _get_imports(
code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
) -> List[ImportInformation]:
"""Get imports from the given code block.
Args:
code: Python code block from which to extract imports
doc_title: Title of the document
package_ecosystem: "langchain" or "langgraph". The two live in different
repositories and have separate documentation sites.
Returns:
List of import information for the given code block
"""
imports = []
if package_ecosystem == "langchain":
pattern = _IMPORT_LANGCHAIN_RE
elif package_ecosystem == "langgraph":
pattern = _IMPORT_LANGGRAPH_RE
else:
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
for import_match in pattern.finditer(code):
module = import_match.group(1)
if "pydantic_v1" in module:
continue
imports_str = (
import_match.group(2).replace("(\n", "").replace("\n)", "")
) # Handle newlines within parentheses
# remove any newline and spaces, then split by comma
imported_classes = [
imp.strip()
for imp in re.split(r",\s*", imports_str.replace("\n", ""))
if imp.strip()
]
for class_name in imported_classes:
module_path = _get_full_module_name(module, class_name)
if not module_path:
continue
if len(module_path.split(".")) < 2:
continue
if package_ecosystem == "langchain":
pkg = module_path.split(".")[0].replace("langchain_", "")
top_level_mod = module_path.split(".")[1]
url = (
_LANGCHAIN_API_REFERENCE
+ pkg
+ "/"
+ top_level_mod
+ "/"
+ module_path
+ "."
+ class_name
+ ".html"
)
elif package_ecosystem == "langgraph":
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
# Likely not documented yet
continue
source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
(module, class_name)
]
url = (
_LANGGRAPH_API_REFERENCE
+ namespace
+ "/#"
+ source_module
+ "."
+ class_name
)
else:
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
# Add the import information to our list
imports.append(
{
"imported": class_name,
"source": module,
"docs": url,
"title": doc_title,
}
)
return imports
class ImportPreprocessor(Preprocessor):
"""A preprocessor to replace imports in each Python code cell with links to their
documentation and append the import info in a comment."""
def preprocess(self, nb, resources):
self.all_imports = []
file_name = os.path.basename(resources.get("metadata", {}).get("name", ""))
_DOC_TITLE = _get_doc_title(nb.cells[0].source, file_name)
cells = []
for cell in nb.cells:
if cell.cell_type == "code":
cells.append(cell)
imports = _get_imports(
cell.source, _DOC_TITLE, "langchain"
) + _get_imports(cell.source, _DOC_TITLE, "langgraph")
if not imports:
continue
cells.append(
nbformat.v4.new_markdown_cell(
source=f"""
<div>
<b>API Reference:</b>
{' | '.join(f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports)}
</div>
"""
)
)
else:
cells.append(cell)
nb.cells = cells
return nb, resources
+126
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import os
import re
from pathlib import Path
import nbformat
from nbconvert.exporters import MarkdownExporter
from nbconvert.preprocessors import Preprocessor
from generate_api_reference_links import ImportPreprocessor
class EscapePreprocessor(Preprocessor):
def preprocess_cell(self, cell, resources, cell_index):
if cell.cell_type == "markdown":
# rewrite markdown links to html links (excluding image links)
cell.source = re.sub(
r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
r'<a href="\2">\1</a>',
cell.source,
)
# Fix image paths in <img> tags
cell.source = re.sub(
r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
)
elif cell.cell_type == "code":
# escape ``` in code
cell.source = cell.source.replace("```", r"\`\`\`")
# escape ``` in output
if "outputs" in cell:
filter_out = set()
for i, output in enumerate(cell["outputs"]):
if "text" in output:
if not output["text"].strip():
filter_out.add(i)
continue
value = output["text"].replace("```", r"\`\`\`")
# handle a funky case w/ references in text
value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
output["text"] = value
elif "data" in output:
for key, value in output["data"].items():
if isinstance(value, str):
value = value.replace("```", r"\`\`\`")
# handle a funky case w/ references in text
output["data"][key] = re.sub(
r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
)
cell["outputs"] = [
output
for i, output in enumerate(cell["outputs"])
if i not in filter_out
]
return cell, resources
class ExtractAttachmentsPreprocessor(Preprocessor):
"""
Extracts all of the outputs from the notebook file. The extracted
outputs are returned in the 'resources' dictionary.
"""
def preprocess_cell(self, cell, resources, cell_index):
"""
Apply a transformation on each cell,
Parameters
----------
cell : NotebookNode cell
Notebook cell being processed
resources : dictionary
Additional resources used in the conversion process. Allows
preprocessors to pass variables into the Jinja engine.
cell_index : int
Index of the cell being processed (see base.py)
"""
# Get files directory if it has been specified
# Make sure outputs key exists
if not isinstance(resources["outputs"], dict):
resources["outputs"] = {}
# Loop through all of the attachments in the cell
for name, attach in cell.get("attachments", {}).items():
for mime, data in attach.items():
if mime not in {
"image/png",
"image/jpeg",
"image/svg+xml",
"application/pdf",
}:
continue
# attachments are pre-rendered. Only replace markdown-formatted
# images with the following logic
attach_str = f"({name})"
if attach_str in cell.source:
data = f"(data:{mime};base64,{data})"
cell.source = cell.source.replace(attach_str, data)
return cell, resources
exporter = MarkdownExporter(
preprocessors=[
EscapePreprocessor,
ExtractAttachmentsPreprocessor,
ImportPreprocessor,
],
template_name="mdoutput",
extra_template_basedirs=[
os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
],
)
def convert_notebook(
notebook_path: Path,
) -> Path:
with open(notebook_path) as f:
nb = nbformat.read(f, as_version=4)
body, _ = exporter.from_notebook_node(nb)
return body
@@ -0,0 +1,5 @@
{
"mimetypes": {
"text/markdown": true
}
}
@@ -0,0 +1,33 @@
{% extends 'markdown/index.md.j2' %}
{%- block traceback_line -%}
```output
{{ line.rstrip() | strip_ansi }}
```
{%- endblock traceback_line -%}
{%- block stream -%}
```output
{{ output.text.rstrip() }}
```
{%- endblock stream -%}
{%- block data_text scoped -%}
```output
{{ output.data['text/plain'].rstrip() }}
```
{%- endblock data_text -%}
{%- block data_html scoped -%}
```html
{{ output.data['text/html'] | safe }}
```
{%- endblock data_html -%}
{%- block data_jpg scoped -%}
![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
{%- endblock data_jpg -%}
{%- block data_png scoped -%}
![](data:image/png;base64,{{ output.data['image/png'] }})
{%- endblock data_png -%}
+40
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@@ -0,0 +1,40 @@
import logging
from typing import Any, Dict
from mkdocs.structure.pages import Page
from mkdocs.structure.files import Files, File
from notebook_convert import convert_notebook
logger = logging.getLogger(__name__)
logging.basicConfig()
logger.setLevel(logging.INFO)
class NotebookFile(File):
def is_documentation_page(self):
return True
def on_files(files: Files, **kwargs: Dict[str, Any]):
new_files = Files([])
for file in files:
if file.src_path.endswith(".ipynb"):
new_file = NotebookFile(
path=file.src_path,
src_dir=file.src_dir,
dest_dir=file.dest_dir,
use_directory_urls=file.use_directory_urls,
)
new_files.append(new_file)
else:
new_files.append(file)
return new_files
def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
if page.file.src_path.endswith(".ipynb"):
logger.info("Processing Jupyter notebook: %s", page.file.src_path)
body = convert_notebook(page.file.abs_src_path)
return body
return markdown
+215
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@@ -0,0 +1,215 @@
"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
import logging
import os
import json
import click
import nbformat
logger = logging.getLogger(__name__)
NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
BLOCKLIST_COMMANDS = (
# skip if has WebBaseLoader to avoid caching web pages
"WebBaseLoader",
# skip if has draw_mermaid_png to avoid generating mermaid images via API
"draw_mermaid_png",
)
NOTEBOOKS_NO_CASSETTES = (
"docs/docs/how-tos/visualization.ipynb",
"docs/docs/how-tos/many-tools.ipynb"
)
NOTEBOOKS_NO_EXECUTION = [
# this uses a user provided project name for langsmith
"docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
# this uses langsmith datasets
"docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
# this uses browser APIs
"docs/docs/tutorials/web-navigation/web_voyager.ipynb",
# these RAG guides use an ollama model
"docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
"docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
"docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
# this loads a massive dataset from gcp
"docs/docs/tutorials/usaco/usaco.ipynb",
# TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
"docs/docs/how-tos/autogen-integration.ipynb",
# TODO: need to update these notebooks to make sure they are runnable in CI
"docs/docs/tutorials/storm/storm.ipynb", # issues only when running with VCR
"docs/docs/tutorials/lats/lats.ipynb", # issues only when running with VCR
"docs/docs/tutorials/rag/langgraph_crag.ipynb", # flakiness from tavily
"docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # Cannot create a consistent method resolution error from VCR
"docs/docs/how-tos/map-reduce.ipynb" # flakiness from structured output, only when running with VCR
]
def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
for cell in notebook.cells:
if cell.cell_type != "code":
continue
if "pip install" in cell.source:
# Comment out the lines in cells containing "pip install"
cell.source = "\n".join(
f"# {line}" if line.strip() else line
for line in cell.source.splitlines()
)
return notebook
def is_magic_command(code: str) -> bool:
return code.strip().startswith("%") or code.strip().startswith("!")
def is_comment(code: str) -> bool:
return code.strip().startswith("#")
def has_blocklisted_command(code: str, metadata: dict) -> bool:
if 'hide_from_vcr' in metadata:
return True
code = code.strip()
for blocklisted_pattern in BLOCKLIST_COMMANDS:
if blocklisted_pattern in code:
return True
return False
def add_vcr_to_notebook(
notebook: nbformat.NotebookNode, cassette_prefix: str
) -> nbformat.NotebookNode:
"""Inject `with vcr.cassette` into each code cell of the notebook."""
# Inject VCR context manager into each code cell
for idx, cell in enumerate(notebook.cells):
if cell.cell_type != "code":
continue
lines = cell.source.splitlines()
# skip if empty cell
if not lines:
continue
are_magic_lines = [is_magic_command(line) for line in lines]
# skip if all magic
if all(are_magic_lines):
continue
if any(are_magic_lines):
raise ValueError(
"Cannot process code cells with mixed magic and non-magic code."
)
# skip if just comments
if all(is_comment(line) or not line.strip() for line in lines):
continue
if has_blocklisted_command(cell.source, cell.metadata):
continue
cell_id = cell.get("id", idx)
cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
f" {line}" for line in lines
)
# Add import statement
vcr_import_lines = [
"import nest_asyncio",
"nest_asyncio.apply()",
"import vcr",
"import msgpack",
"import base64",
"import zlib",
"import os",
"os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
"custom_vcr = vcr.VCR()",
"",
"def compress_data(data, compression_level=9):",
" packed = msgpack.packb(data, use_bin_type=True)",
" compressed = zlib.compress(packed, level=compression_level)",
" return base64.b64encode(compressed).decode('utf-8')",
"",
"def decompress_data(compressed_string):",
" decoded = base64.b64decode(compressed_string)",
" decompressed = zlib.decompress(decoded)",
" return msgpack.unpackb(decompressed, raw=False)",
"",
"class AdvancedCompressedSerializer:",
" def serialize(self, cassette_dict):",
" return compress_data(cassette_dict)",
"",
" def deserialize(self, cassette_string):",
" return decompress_data(cassette_string)",
"",
"custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
"custom_vcr.serializer = 'advanced_compressed'",
]
import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
import_cell.pop("id", None)
notebook.cells.insert(0, import_cell)
return notebook
def process_notebooks(should_comment_install_cells: bool) -> None:
for directory in NOTEBOOK_DIRS:
for root, _, files in os.walk(directory):
for file in files:
if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
continue
notebook_path = os.path.join(root, file)
try:
notebook = nbformat.read(notebook_path, as_version=4)
if should_comment_install_cells:
notebook = comment_install_cells(notebook)
base_filename = os.path.splitext(os.path.basename(file))[0]
cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
if notebook_path not in NOTEBOOKS_NO_CASSETTES:
notebook = add_vcr_to_notebook(
notebook, cassette_prefix=cassette_prefix
)
if notebook_path in NOTEBOOKS_NO_EXECUTION:
# Add a cell at the beginning to indicate that this notebook should not be executed
warning_cell = nbformat.v4.new_markdown_cell(
source="**Warning:** This notebook is not meant to be executed automatically."
)
notebook.cells.insert(0, warning_cell)
# Add a special tag to the first code cell
if notebook.cells and notebook.cells[1].cell_type == "code":
notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
nbformat.write(notebook, notebook_path)
logger.info(f"Processed: {notebook_path}")
except Exception as e:
logger.error(f"Error processing {notebook_path}: {e}")
with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
json.dump(NOTEBOOKS_NO_EXECUTION, f)
@click.command()
@click.option(
"--comment-install-cells",
is_flag=True,
default=False,
help="Whether to comment out install cells",
)
def main(comment_install_cells):
process_notebooks(should_comment_install_cells=comment_install_cells)
logger.info("All notebooks processed successfully.")
if __name__ == "__main__":
main()
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@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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