Compare commits

..
Author SHA1 Message Date
Arjun Natarajan 91bca2ea1d first pass at data storage docs 2025-06-23 20:22:33 -04:00
lc-arjunandGitHub bd206c2fcd docs: fix assistants links (#5172)
* docs: fix assistants links

* fix another page
2025-06-23 13:48:20 -07:00
lc-arjunandGitHub 14ec895046 docs: Fix LGP sdk typo (#5170)
docs: fix typo in sdk docs
2025-06-23 10:15:15 -07:00
hari-dhanushkodiandGitHub fb66736ccb add more docs for lgp deployment metrics (#5168) 2025-06-23 07:11:57 -07:00
Nuno Campos c3544024b9 If FuturesDict callback has been GCed, don't call it 2025-06-17 14:06:54 -07:00
b0d1234737 docs: missing lgp docs (#5130)
* chore: add docs for lgp deployment monitoring (#5104)

* docs: studio evals (#5129)

* docs: studio evals

* docs: added studio evals images (#5076)

* docs: added studio evals images

* Update docs/docs/cloud/how-tos/studio/run_evals.md

Co-authored-by: lc-arjun <arjun@langchain.dev>

* Update docs/docs/cloud/how-tos/studio/run_evals.md

Co-authored-by: lc-arjun <arjun@langchain.dev>

* Update docs/docs/cloud/how-tos/studio/run_evals.md

Co-authored-by: lc-arjun <arjun@langchain.dev>

* docs: updated studio evals

* Update docs/docs/cloud/how-tos/studio/run_evals.md

Co-authored-by: lc-arjun <arjun@langchain.dev>

* docs: removed images

---------

Co-authored-by: lc-arjun <arjun@langchain.dev>

* final changes

* i think its this

---------

Co-authored-by: Marco Perini <perinim.98@gmail.com>

---------

Co-authored-by: hari-dhanushkodi <hari@langchain.dev>
Co-authored-by: Marco Perini <perinim.98@gmail.com>
2025-06-17 13:10:27 -07:00
Lauren Hirata Singh 53e1a238db Remove cookie consent 2025-06-16 18:42:56 -04:00
langchain-infraandGitHub fcdeafd0d1 docs: fix langgraph docs (#5065)
docs: fix config section
2025-06-11 13:23:16 -04:00
langchain-infraandGitHub 28c529feb2 docs: add mount prefix environment variable (#5061) 2025-06-11 11:21:53 -04:00
infra 91ebc8d3ed docs: add mount prefix environment variable 2025-06-11 11:19:40 -04:00
Eugene YurtsevandGitHub 6e08f4c12e v0: port GTM to v0 (#5056)
This was lost when the v0 branch was cut out and docs started being deployed from v0
2025-06-11 10:21:29 -04:00
Nuno CamposandGitHub f2dc0653f1 docs: list CipherProtocol in API (#5048) 2025-06-10 14:28:39 -07:00
Nuno CamposandNuno Campos 67177a5610 docs: list CipherProtocol in API 2025-06-10 14:25:26 -07:00
Asamu DavidandGitHub b1b238c7ea add docs for image_distro cli option (#4981) 2025-06-06 16:32:21 +01:00
David Asamu 598796ef86 add docs for image_distro cli option 2025-06-06 16:26:25 +01:00
Sydney RunkleandGitHub 3c7981201e docs: remove usage of StateGraph(dict) (#4967)
docs: remove references to `StateGraph(dict)` (#4964)

remove StateGraph(dict)
2025-06-04 21:30:39 -04:00
Sydney RunkleandGitHub 109c0dfb93 docs: deploy from v0 branch for now (#4960) (#4961)
only deploy docs on v0
2025-06-04 13:30:34 -04:00
Nuno Campos d7c364c5bb Port step_timeout/GraphBubbleUp fix to v0
See fix and tests in original PR https://github.com/langchain-ai/langgraph/pull/4950
2025-06-03 17:23:45 -07:00
Nuno Campos 746142fb07 One more 2025-06-02 16:16:11 -07:00
Nuno Campos b9c9c32c31 Allow releases from v0 2025-06-02 16:12:54 -07:00
Nuno Campos c89fe4c45d 0.4.8 2025-06-02 16:11:09 -07:00
Nuno CamposandGitHub b0e28851a6 v0: Fix Command(graph=PARENT) when used together w checkpointer=True (#4920) 2025-06-02 16:10:00 -07:00
Nuno Campos 48fb91deda Fix Command(graph=PARENT) when used together w checkpointer=True 2025-06-02 16:02:35 -07:00
1110 changed files with 152417 additions and 84330 deletions
+29 -52
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@@ -1,60 +1,44 @@
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.
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),
- type: checkboxes
id: checks
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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.
required: true
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- 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.
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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 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
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from langgraph.graph import StateGraph
@@ -63,13 +47,17 @@ body:
chain = StateGraph(list)
chain.invoke('Hello!')
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Exception + full stack trace
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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
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+12 -12
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blank_issues_enabled: false
version: 2.1
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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/
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
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about: Ask a question or ask about a problem in GitHub Discussions.
url: https://github.com/langchain-ai/langgraph/discussions/categories/q-a
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url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
about: Suggest a feature or an idea
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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
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+19
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labels: [03 - Documentation]
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label: "Issue with current documentation:"
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Please describe as clearly as possible what topics you think are missing
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+8 -12
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name: 🔒 Privileged
description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
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
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Thanks for your interest in LangGraph! 🚀
If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository
or are a regular contributor to LangGraph with previous merged merged pull requests.
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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- label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
required: true
- type: textarea
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Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.
-31
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@@ -1,31 +0,0 @@
Thank you for contributing to LangGraph! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres, checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace with
- **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, you must include:
1. A test for the integration, preferably unit tests that do not rely on network access,
2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
-111
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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)
+34 -102
View File
@@ -2,12 +2,6 @@ name: CLI integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
permissions:
contents: read
jobs:
build:
@@ -16,124 +10,62 @@ 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@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: astral-sh/setup-uv@v7
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: "cli-integration-test"
ignore-nothing-to-cache: true
- name: Setup env
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: cat .env.example > .env
- name: Install cli globally
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
if: steps.changed-files.outputs.all
run: pip install -e .
- name: Build service ${{ matrix.example.name }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
working-directory: ${{ matrix.example.workdir }}
- name: Build and test service A
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples
run: |
langgraph build -t ${{ matrix.example.tag }}
- name: Test service ${{ matrix.example.name }}
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: ${{ matrix.example.workdir }}
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
# The build-arg isn't used; just testing that we accept other args
langgraph build -t langgraph-test-a --base-image "langchain/langgraph-trial"
cp .env.example .envg
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
- name: Build and test service B
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs
run: |
# Prepare environment file from local or parent example directory
if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
# Run the integration test using the built tag
REPO_ROOT=$(git rev-parse --show-toplevel)
timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
langgraph build -t langgraph-test-b --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
- name: Build and test service C
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_a
run: |
langgraph build -t langgraph-test-c --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
- name: Build and test service D
if: steps.changed-files.outputs.all
working-directory: libs/cli/examples/graphs_reqs_b
run: |
langgraph build -t langgraph-test-d --base-image "langchain/langgraph-trial"
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
- name: Build JS service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: steps.changed-files.outputs.all
working-directory: libs/cli/js-examples
run: |
langgraph build -t langgraph-test-e
- name: Build JS monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/js-monorepo-example
run: |
langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
- name: Build Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/python-monorepo-example
run: |
langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
- name: Test Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/python-monorepo-example
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp apps/agent/.env.example apps/agent/.env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
- name: Build prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
run: |
langgraph build -t langgraph-test-h
- name: Test prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp ../.env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
echo "Finished starting up langgraph-test-h"
LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
if [ "$LANGGRAPH_VERSION" != "1.1.2" ]; 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 || [ $? -eq 1 ]
+13 -17
View File
@@ -8,9 +8,6 @@ on:
type: string
description: "From which folder this pipeline executes"
permissions:
contents: read
env:
# This env var allows us to get inline annotations when ruff has complaints.
RUFF_OUTPUT_FORMAT: github
@@ -31,29 +28,28 @@ jobs:
- "3.12"
name: "lint #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@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: astral-sh/setup-uv@v7
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: lint-${{ inputs.working-directory }}
- name: Install dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group lint
run: uv sync --frozen --group dev
- name: Get .mypy_cache to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v3
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
@@ -62,7 +58,7 @@ jobs:
key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing package code with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_package > /dev/null 2>&1; then
@@ -73,13 +69,13 @@ jobs:
fi
- name: Install test dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --group lint
run: uv sync --group dev
- name: Get .mypy_cache_test to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
uses: actions/cache@v5
if: steps.changed-files.outputs.all
uses: actions/cache@v3
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
with:
@@ -88,7 +84,7 @@ jobs:
key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing tests with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_tests > /dev/null 2>&1; then
+5 -8
View File
@@ -8,32 +8,29 @@ on:
type: string
description: "From which folder this pipeline executes"
permissions:
contents: read
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: test-${{ inputs.working-directory }}
- name: Login to Docker Hub
uses: docker/login-action@v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -42,7 +39,7 @@ jobs:
- name: Install dependencies
shell: bash
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group test --no-dev
run: uv sync --frozen --group dev
- name: Run tests
shell: bash
+5 -15
View File
@@ -3,35 +3,32 @@ name: test
on:
workflow_call:
permissions:
contents: read
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
- "3.14"
defaults:
run:
working-directory: libs/langgraph
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: "test-langgraph"
- name: Login to Docker Hub
uses: docker/login-action@v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -39,19 +36,12 @@ jobs:
- name: Install dependencies
shell: bash
run: uv sync --frozen --group test --no-dev
run: uv sync --frozen --group dev
- name: Run tests
shell: bash
run: make test_parallel
- name: Run strict msgpack pregel tests
if: ${{ matrix.python-version == '3.13' }}
shell: bash
env:
LANGGRAPH_STRICT_MSGPACK: "true"
run: make test TEST="tests/test_pregel.py tests/test_pregel_async.py"
- name: Ensure the tests did not create any additional files
shell: bash
run: |
+6 -8
View File
@@ -11,11 +11,9 @@ on:
env:
PYTHON_VERSION: "3.10"
permissions:
contents: read
jobs:
build:
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v0'
runs-on: ubuntu-latest
outputs:
@@ -23,10 +21,10 @@ jobs:
version: ${{ steps.check-version.outputs.version }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python $${ env.PYTHON_VERSION }}
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
@@ -48,7 +46,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
@@ -74,9 +72,9 @@ 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: ${{ inputs.working-directory }}/dist/
@@ -0,0 +1,52 @@
name: test
on:
workflow_call:
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.11"
- "3.12"
defaults:
run:
working-directory: libs/scheduler-kafka
name: "test #${{ matrix.python-version }}"
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python-version }}
enable-cache: true
cache-suffix: "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: ${{ secrets.DOCKERHUB_RO_TOKEN }}
- name: Install dependencies
shell: bash
run: uv sync --frozen --group dev
- name: Run tests
shell: bash
run: make test
- name: Ensure the tests did not create any additional files
shell: bash
run: |
set -eu
STATUS="$(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'
+4 -7
View File
@@ -7,9 +7,6 @@ on:
paths:
- "libs/**"
permissions:
contents: read
jobs:
benchmark:
runs-on: ubuntu-latest
@@ -17,20 +14,20 @@ 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: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
- name: Install dependencies
run: uv sync --group test
run: uv sync --group dev
- name: Run benchmarks
run: OUTPUT=out/benchmark-baseline.json make -s benchmark
- name: Save outputs
uses: actions/cache/save@v5
uses: actions/cache/save@v4
with:
key: ${{ runner.os }}-benchmark-baseline-${{ env.SHA }}
path: |
+5 -8
View File
@@ -5,9 +5,6 @@ on:
paths:
- "libs/**"
permissions:
contents: read
jobs:
benchmark:
runs-on: ubuntu-latest
@@ -15,22 +12,22 @@ 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@v2.3.0
with:
format: json
- name: Set up Python 3.11
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "bench"
- name: Install dependencies
run: uv sync --group test
run: uv sync --group dev
- name: Download baseline
uses: actions/cache/restore@v5
uses: actions/cache/restore@v4
with:
key: ${{ runner.os }}-benchmark-baseline
restore-keys: |
@@ -57,7 +54,7 @@ jobs:
echo EOF
} >> "$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]
+80 -29
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.
@@ -26,12 +20,11 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
python: ${{ steps.filter.outputs.python || 'true' }}
deps: ${{ steps.filter.outputs.deps || 'true' }}
python: ${{ steps.filter.outputs.python }}
sdk-js: ${{ steps.filter.outputs.sdk-js }}
steps:
- uses: actions/checkout@v6
- uses: dorny/paths-filter@v4
if: github.event_name != 'workflow_dispatch'
- uses: actions/checkout@v4
- uses: dorny/paths-filter@v3
id: filter
with:
filters: |
@@ -42,11 +35,10 @@ jobs:
- 'libs/checkpoint/**'
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/checkpoint-conformance/**'
- 'libs/scheduler-kafka/**'
- 'libs/prebuilt/**'
deps:
- '**/pyproject.toml'
- '**/uv.lock'
sdk-js:
- 'libs/sdk-js/**'
lint:
needs: changes
@@ -61,10 +53,10 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/scheduler-kafka",
"libs/prebuilt",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
if: needs.changes.outputs.python == 'true'
uses: ./.github/workflows/_lint.yml
with:
working-directory: ${{ matrix.working-directory }}
@@ -81,11 +73,9 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/sdk-py",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
if: needs.changes.outputs.python == 'true'
uses: ./.github/workflows/_test.yml
with:
working-directory: ${{ matrix.working-directory }}
@@ -94,20 +84,28 @@ 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'
if: needs.changes.outputs.python == '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:
needs: changes
if: needs.changes.outputs.python == 'true'
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"
- name: Run check_sdk_methods script
@@ -121,13 +119,13 @@ jobs:
strategy:
matrix:
python-version:
- "3.13"
- "3.11"
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: "3.13"
python-version: "3.11"
enable-cache: true
cache-suffix: "schema-check-cli"
- name: Install CLI dependencies
@@ -151,21 +149,74 @@ jobs:
integration-test:
needs: changes
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
if: needs.changes.outputs.python == 'true'
name: CLI integration test
uses: ./.github/workflows/_integration_test.yml
secrets: inherit
lint-js:
needs: changes
if: needs.changes.outputs.sdk-js == 'true'
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
test-js:
needs: changes
if: needs.changes.outputs.sdk-js == 'true'
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 tests
run: yarn test
ci_success:
name: "CI Success"
needs:
[
lint,
lint-js,
test,
test-langgraph,
test-scheduler-kafka,
check-sdk-methods,
check-schema,
integration-test,
test-js,
]
if: |
always()
+43
View File
@@ -0,0 +1,43 @@
---
name: CI / cd . / make spell_check
on:
push:
branches: [main]
pull_request:
branches: [main]
permissions:
contents: read
defaults:
run:
working-directory: docs
jobs:
codespell:
name: (Check for spelling errors)
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Install Dependencies
run: |
pip install toml codespell==2.3.0 jupytext
- name: Extract Ignore Words List
run: |
# Use a Python script to extract the ignore words list from pyproject.toml
python ../.github/workflows/extract_ignored_words_list.py
id: extract_ignore_words
- name: Codespell
uses: codespell-project/actions-codespell@v2
with:
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
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@v4
+161
View File
@@ -0,0 +1,161 @@
name: Deploy Docs
on:
push:
branches:
- main
- v0
pull_request:
branches:
- main
- v0
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: "pages"
cancel-in-progress: false
defaults:
run:
working-directory: docs
jobs:
get-changed-files:
runs-on: ubuntu-latest
outputs:
changed-files: ${{ steps.changed-files.outputs.added_modified }}
steps:
- uses: actions/checkout@v4
- name: Get changed files
id: changed-files
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "docs/docs/**"
# TODO: Uncomment this to run on PRs
# 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
timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
env:
GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: astral-sh/setup-uv@v6
with:
python-version: "3.12"
enable-cache: true
cache-suffix: "docs"
- name: Install dependencies
run: |
yarn
uv sync --all-groups
# we run this installation only for internal PRs
# as GITHUB_TOKEN is not available for PRs from outside contributors
if [ -n "${GITHUB_TOKEN}" ]; then
uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
fi
- name: Run unit tests
# Run unit tests on the docs build pipeline
run: make tests
- name: Lint Docs
# This step lints the docs using the existing linting set up.
# It should be very fast and should not require any external services.
run: make lint-docs
- name: Build llms-text
run: make llms-text
- name: Build site
run: |
# If this is v0 branch, then we want to download stats. we do this
# with the env variable DOWNLOAD_STATS=true
if [ "${{ github.ref }}" == "refs/heads/v0" ]; then
DOWNLOAD_STATS=true make build-docs
else
make build-docs
fi
env:
MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
- name: Check links in notebooks
env:
LANGCHAIN_API_KEY: test
if: github.event_name == 'schedule'
run: |
if [ "${{ github.event_name }}" == "schedule" ]; then
echo "Running link check on all HTML files matching notebooks in docs directory..."
uv run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "https://academy\.langchain\.com/.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://twitter.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "http://localhost:2024.*" \
--check-links-ignore "http://127.0.0.1:.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links-ignore "https://python\.langchain\.com/.*" \
--check-links-ignore "https://openai\.com/.*" \
--check-links-ignore "https://www\.uber\.com/.*" \
--check-links-ignore "https://pepy\.tech/.*" \
--check-links-ignore "docs/docs/static/wordmark_*" \
--check-links $(find site -name "index.html" | grep -v 'storm/index.html')
else
echo "Fetching changes from origin/main..."
git fetch origin main
echo "Checking for changed notebook files..."
CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
echo "Changed files: ${CHANGED_FILES}"
if [ -n "${CHANGED_FILES}" ]; then
echo "Running link check on HTML files matching changed notebook files..."
uv run pytest -v \
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
--check-links-ignore "https://academy\.langchain\.com/.*" \
--check-links-ignore "http://localhost:8123/.*" \
--check-links-ignore "http://localhost:2024.*" \
--check-links-ignore "http://127.0.0.1:.*" \
--check-links-ignore "https://x.com/.*" \
--check-links-ignore "https://twitter.com/.*" \
--check-links-ignore "https://github\.com/.*" \
--check-links-ignore "/.*\.(ipynb|html)$" \
--check-links-ignore "docs/docs/static/wordmark_*" \
--check-links ${CHANGED_FILES} \
|| ([ $? = 5 ] && exit 0 || exit $?)
else
echo "No notebook files changed."
fi
fi
- name: Configure GitHub Pages
if: github.ref == 'refs/heads/v0'
uses: actions/configure-pages@v5
- name: Upload Pages Artifact
# if: github.ref == 'refs/heads/main'
uses: actions/upload-pages-artifact@v3
with:
path: ./docs/site/
- name: Deploy to GitHub Pages
if: github.ref == 'refs/heads/v0'
id: deployment
uses: actions/deploy-pages@v4
@@ -0,0 +1,10 @@
import toml
pyproject_toml = toml.load("pyproject.toml")
# Extract the ignore words list (adjust the key as per your TOML structure)
ignore_words_list = (
pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
)
print(f"::set-output name=ignore_words_list::{ignore_words_list}") # noqa: T201
+46
View File
@@ -0,0 +1,46 @@
name: Check Docs & Links
on:
pull_request:
branches:
- main
push:
branches:
- main
schedule:
- cron: "0 5 * * *"
workflow_dispatch:
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@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
+16 -27
View File
@@ -8,14 +8,12 @@ on:
type: string
default: "libs/langgraph"
permissions:
contents: read
env:
PYTHON_VERSION: "3.11"
jobs:
build:
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v0'
runs-on: ubuntu-latest
outputs:
@@ -25,10 +23,10 @@ jobs:
tag: ${{ steps.check-version.outputs.tag }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
@@ -50,7 +48,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -61,13 +59,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
run: |
PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
if grep -q 'dynamic.*=.*\[.*"version".*\]' pyproject.toml; then
# handle dynamic versioning
DIR_NAME=$(echo "$PKG_NAME" | tr '-' '_')
VERSION=$(grep -m 1 '^__version__' "${DIR_NAME}/__init__.py" | cut -d '"' -f 2)
else
VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
fi
VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
if [ -z $SHORT_PKG_NAME ]; then
TAG="$VERSION"
@@ -86,7 +78,7 @@ 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 +134,7 @@ 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 +147,7 @@ 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.
@@ -173,7 +163,7 @@ jobs:
# used in the real world.
- name: Set up Python
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
@@ -221,7 +211,7 @@ jobs:
uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
- name: Import test dependencies
run: uv sync --group test
run: uv sync --group dev
working-directory: ${{ inputs.working-directory }}
# Overwrite the local version of the package with the test PyPI version.
@@ -260,16 +250,16 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -301,16 +291,16 @@ jobs:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set up Python
uses: astral-sh/setup-uv@v7
uses: astral-sh/setup-uv@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
enable-cache: true
cache-suffix: "release"
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -322,6 +312,5 @@ jobs:
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
+82
View File
@@ -0,0 +1,82 @@
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 * * *"
defaults:
run:
working-directory: docs
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
lib-version:
- "development"
- "latest"
steps:
- uses: actions/checkout@v4
- name: Set up Python + Poetry
uses: astral-sh/setup-uv@v6
with:
python-version: "3.11"
enable-cache: true
cache-suffix: "test-langgraph-notebooks"
- name: Install dependencies
run: |
uv sync --group test
uv run pip install jupyter
- name: Start services
run: make start-services
- name: Pre-download tiktoken files
run: |
uv run python _scripts/download_tiktoken.py
- name: Prepare notebooks
run: |
if [ "${{ matrix.lib-version }}" = "development" ]; then
uv run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
else
uv run python _scripts/prepare_notebooks_for_ci.py
fi
- name: Run notebooks
env:
# these won't actually be used because of the VCR cassettes
# but need to set them to avoid triggering getpass()
OPENAI_API_KEY: "very-secret-key"
ANTHROPIC_API_KEY: "very-secret-key"
TAVILY_API_KEY: "very-secret-key"
LANGSMITH_API_KEY: "very-secret-key"
NOMIC_API_KEY: "very-secret-key"
COHERE_API_KEY: "very-secret-key"
FIREWORKS_API_KEY: "very-secret-key"
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
echo "Running all notebooks"
./_scripts/execute_notebooks.sh
else
CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
if [ -n "$CHANGED_FILES" ]; then
echo "Running changed notebooks: $CHANGED_FILES"
./_scripts/execute_notebooks.sh $CHANGED_FILES
else
echo "No notebook files changed, skipping execution"
fi
fi
- name: Stop services
run: make stop-services
-45
View File
@@ -1,45 +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: astral-sh/setup-uv@v7
with:
# use minimum supported Python version
python-version: "3.10"
enable-cache: true
cache-suffix: "uv-lock-upgrade"
- name: Run uv lock --upgrade in all Python packages
run: make lock-upgrade
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.GITHUB_TOKEN }}
commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
title: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
body: |
This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
branch: deps/uv-lock-upgrade
delete-branch: true
labels: |
dependencies
+82 -1
View File
@@ -6,6 +6,9 @@ __pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
@@ -51,12 +54,27 @@ coverage.xml
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
docs/docs/_build/
# PyBuilder
target/
@@ -71,9 +89,23 @@ ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.envrc
@@ -85,6 +117,16 @@ ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
@@ -96,7 +138,46 @@ dmypy.json
# macOS display setting files
.DS_Store
# Wandb directory
wandb/
# asdf tool versions
.tool-versions
/.ruff_cache/
*.pkl
*.bin
# integration test artifacts
data_map*
\[('_type', 'fake'), ('stop', None)]
# Replit files
*replit*
node_modules
docs/.yarn/
docs/node_modules/
docs/.docusaurus/
docs/.cache-loader/
docs/_dist
docs/api_reference/api_reference.rst
docs/api_reference/experimental_api_reference.rst
docs/api_reference/_build
docs/api_reference/*/
!docs/api_reference/_static/
!docs/api_reference/templates/
!docs/api_reference/themes/
docs/docs_skeleton/build
docs/docs_skeleton/node_modules
docs/docs_skeleton/yarn.lock
# Any new jupyter notebooks
# not intended for the repo
Untitled*.ipynb
Chinook.db
.vercel
.turbo
.editorconfig
.scratch
+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
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:
```
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.
+294
View File
@@ -0,0 +1,294 @@
# 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 actual need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
## Setup
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:
<!-- TODO -->
```bash
poetry install --with docs --no-root
```
#### Building
The code that builds the documentation is located in the `/docs` directory of the monorepo.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make clean-docs
```
You can build and preview the documentation as outlined below:
```bash
make serve-docs
```
#### Linting
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
```
-68
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@@ -1,68 +0,0 @@
# Define the directories containing projects
LIBS_DIRS := $(wildcard libs/*)
# Default target
.PHONY: all
all: lint format lock test
# 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 $$dir; \
fi; \
done
# Lint all projects
.PHONY: lint
lint:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running lint in $$dir"; \
$(MAKE) -C $$dir lint; \
fi; \
done
# Format all projects
.PHONY: format
format:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running format in $$dir"; \
$(MAKE) -C $$dir format; \
fi; \
done
# Lock all projects
.PHONY: lock
lock:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running lock in $$dir"; \
(cd $$dir && uv lock); \
fi; \
done
# 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 $$dir && uv lock --upgrade); \
fi; \
done
# Test all projects
.PHONY: test
test:
@for dir in $(LIBS_DIRS); do \
if [ -f $$dir/Makefile ]; then \
echo "Running test in $$dir"; \
$(MAKE) -C $$dir test; \
fi; \
done
+67 -65
View File
@@ -1,81 +1,83 @@
<div align="center">
<a href="https://www.langchain.com/langgraph">
<picture>
<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
</picture>
</a>
</div>
<div align="center">
<h3>Low-level orchestration framework for building stateful agents.</h3>
</div>
<div align="center">
<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
<a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
<a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
<a href="https://x.com/langchain" 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>
<picture class="github-only">
<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
</picture>
<div>
<br>
</div>
[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
[![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)
Trusted by companies shaping the future of agents including Klarna, Replit, Elastic, and more LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
```bash
## Get started
Install LangGraph:
```
pip install -U langgraph
```
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).
Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
```python
# pip install -qU "langchain[anthropic]" to call the model
from langgraph.prebuilt import create_react_agent
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_weather],
prompt="You are a helpful assistant"
)
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
```
For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
## Core benefits
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
## LangGraphs ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
- [LangChain](https://python.langchain.com/docs/introduction/) Provides integrations and composable components to streamline LLM application development.
> [!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).
## Why use LangGraph?
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
- **[Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- **[Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- **[Debugging with LangSmith](https://www.langchain.com/langsmith)** — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- **[Production-ready deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
## LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- [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).
---
## Documentation
- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) Comprehensive documentation, including conceptual overviews and guides
- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) API reference docs for LangGraph packages
- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) Get started building with LangGraph
- [Chat LangChain](https://chat.langchain.com/) Chat with the LangChain documentation and get answers to your questions
**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
## Additional resources
- **[Guides](https://docs.langchain.com/oss/python/learn)** Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** Learn the basics of LangGraph in our free, structured course.
- **[Case studies](https://www.langchain.com/built-with-langgraph)** Hear how industry leaders use LangGraph to ship AI applications at scale.
- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) Learn how to contribute to LangChain projects and find good first issues.
- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) Our community guidelines and standards for participation.
---
- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
## Acknowledgements
+4 -1
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@@ -1 +1,4 @@
_site/
site/
docs/cloud/reference/sdk/js_ts_sdk_ref.md
.vercel
+72
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@@ -0,0 +1,72 @@
.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
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/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
build-prebuilt:
# Use to create an update to date prebuilt page.
# Looks up download stats for each of the prebuilt packages and
# generates the final prebuilt page.
@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
set -x; \
uv run python -m _scripts.third_party_page.get_download_stats stats.yml; \
set +x; \
else \
set -x; \
uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
set +x; \
fi
uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
build-docs: build-typedoc build-prebuilt
uv run python -m mkdocs build --clean -f mkdocs.yml --strict
llms-text:
uv run python -m _scripts.generate_llms_text docs/llms-full.txt
install-vercel-deps:
curl -sL "https://astral.sh/uv/install.sh" | bash -s
export PATH="${HOME}/.cargo/bin:${PATH}"
uv venv --python 3.11
uv sync --all-groups
tests:
# Run unit tests
uv run pytest tests/unit_tests
vercel-build-docs: install-vercel-deps
make build-docs
serve-clean-docs: clean-docs
uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
serve-docs: build-typedoc
uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph -w ../libs/checkpoint -w ../libs/sdk-py --dirty
clean-docs:
find ./docs -name "*.ipynb" -type f -delete
rm -rf site
## Run format against the project documentation.
format-docs:
uv run ruff format docs
uv run ruff check --fix docs
# Check the docs for linting violations
lint-docs:
uv run ruff format --check docs
uv run ruff check docs
codespell:
./codespell_notebooks.sh .
start-services:
docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
stop-services:
docker compose -f test-compose.yml down
+61
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# Setup
To setup requirements for building docs you can run:
```bash
uv sync --group test
```
## Serving documentation locally
To run the documentation server locally you can run:
```bash
make serve-docs
```
This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
## Execute notebooks
If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
```bash
python _scripts/prepare_notebooks_for_ci.py
./_scripts/execute_notebooks.sh
```
**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
```bash
python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
./_scripts/execute_notebooks.sh
```
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
## Adding new notebooks
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
Then, run
```bash
jupyter execute <path_to_notebook>
```
Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
## Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
To delete cassettes for a notebook, you can run:
```bash
rm cassettes/<notebook_name>*
```
+93
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import functools
from urllib3 import __version__ as urllib3version # type: ignore[import-untyped]
from urllib3 import connection # type: ignore[import-untyped]
def _ensure_str(s, encoding="utf-8", errors="strict") -> str:
if isinstance(s, str):
return s
if isinstance(s, bytes):
return s.decode(encoding, errors)
return str(s)
# Copied from https://github.com/urllib3/urllib3/blob/1c994dfc8c5d5ecaee8ed3eb585d4785f5febf6e/src/urllib3/connection.py#L231
def request(self, method, url, body=None, headers=None):
"""Make the request.
This function is based on the urllib3 request method, with modifications
to handle potential issues when using vcrpy in concurrent workloads.
Args:
self: The HTTPConnection instance.
method (str): The HTTP method (e.g., 'GET', 'POST').
url (str): The URL for the request.
body (Optional[Any]): The body of the request.
headers (Optional[dict]): Headers to send with the request.
Returns:
The result of calling the parent request method.
"""
# Update the inner socket's timeout value to send the request.
# This only triggers if the connection is reused.
if getattr(self, "sock", None) is not None:
self.sock.settimeout(self.timeout)
if headers is None:
headers = {}
else:
# Avoid modifying the headers passed into .request()
headers = headers.copy()
if "user-agent" not in (_ensure_str(k.lower()) for k in headers):
headers["User-Agent"] = connection._get_default_user_agent()
# The above is all the same ^^^
# The following is different:
return self._parent_request(method, url, body=body, headers=headers)
_PATCHED = False
def patch_urllib3():
"""Patch the request method of urllib3 to avoid type errors when using vcrpy.
In concurrent workloads (such as the tracing background queue), the
connection pool can get in a state where an HTTPConnection is created
before vcrpy patches the HTTPConnection class. In urllib3 >= 2.0 this isn't
a problem since they use the proper super().request(...) syntax, but in older
versions, super(HTTPConnection, self).request is used, resulting in a TypeError
since self is no longer a subclass of "HTTPConnection" (which at this point
is vcr.stubs.VCRConnection).
This method patches the class to fix the super() syntax to avoid mixed inheritance.
In the case of the LangSmith tracing logic, it doesn't really matter since we always
exclude cache checks for calls to LangSmith.
The patch is only applied for urllib3 versions older than 2.0.
"""
global _PATCHED
if _PATCHED:
return
from packaging import version
if version.parse(urllib3version) >= version.parse("2.0"):
_PATCHED = True
return
# Lookup the parent class and its request method
parent_class = connection.HTTPConnection.__bases__[0]
parent_request = parent_class.request
def new_request(self, *args, **kwargs):
"""Handle parent request.
This method binds the parent's request method to self and then
calls our modified request function.
"""
self._parent_request = functools.partial(parent_request, self)
return request(self, *args, **kwargs)
connection.HTTPConnection.request = new_request
_PATCHED = True
+157
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@@ -0,0 +1,157 @@
"""Add typescript translation to a given markdown file."""
import argparse
import re
import requests
from langchain_anthropic import ChatAnthropic
URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
response = requests.get(URL)
response.raise_for_status()
reference_snippets = response.text
model = ChatAnthropic(model="claude-3-5-sonnet-latest")
def _get_tqdm():
try:
from tqdm import tqdm
except ImportError:
# If not available return a simple identity function
def tqdm(iterable, *args, **kwargs):
return iterable
return tqdm
_tqdm = _get_tqdm()
opening_pattern = re.compile(r"^\s*```python(?:\s+.*)?\s*$")
closing_pattern = re.compile(r"^\s*```\s*$")
def extract_python_snippets(markdown: str) -> list[str]:
"""
Extract all python code blocks (including their fence lines) from the markdown content.
A python block is defined as any block that starts with a line containing an opening fence
with '```python' (optionally with extra parameters) and ends with a closing fence '```'.
"""
snippets = []
inside_block = False
current_snippet = []
for line in markdown.splitlines(keepends=True):
if not inside_block:
if opening_pattern.match(line):
inside_block = True
current_snippet = [line]
else:
current_snippet.append(line)
if closing_pattern.match(line):
inside_block = False
snippets.append("".join(current_snippet))
current_snippet = []
return snippets
def translate_snippet(python_snippet: str) -> str:
"""Translate a python code block into a TypeScript code block using Langchain.
The response is expected to be a properly fenced TypeScript code block (i.e.
starting with ```typescript and ending with ```).
"""
ai_message = model.invoke(
[
{
"role": "system",
"content": (
f"You have access to the following up-to-date example TypeScript code "
f"snippets that show examples of building with langgraph "
f"and langchain:\n\n{reference_snippets}\n\n"
"Use this context to translate the following Python code to equivalent "
"TypeScript. Ensure that your output is a valid fenced TypeScript "
"code block (i.e. starts with ```typescript and ends with ```)."
),
},
{
"role": "user",
"content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
},
]
)
# Use a regular expression to search for a TypeScript code block in the response.
pattern = r"```typescript\s*(.*?)\s*```"
match = re.search(pattern, ai_message.content, re.DOTALL)
if match:
# Reconstruct the code block with proper fences.
typescript_code = match.group(1).strip()
return f"```typescript\n{typescript_code}\n```"
else:
raise ValueError("No TypeScript code block found in the model's response.")
def insert_translations_into_markdown(
markdown: str, typescript_snippets: list[str]
) -> str:
"""Walks through the original markdown content and, after each
Python snippet block, inserts the corresponding translated TypeScript snippet.
It assumes that the ordering of the Python snippets
(from extract_python_snippets) matches the order they appear in the markdown.
"""
output_lines = []
lines = markdown.splitlines(keepends=True)
inside_block = False
snippet_index = 0
for line in lines:
output_lines.append(line)
if not inside_block and opening_pattern.match(line):
# We've encountered the start of a python code block.
inside_block = True
elif inside_block:
if closing_pattern.match(line):
# End of a python snippet block.
inside_block = False
if snippet_index < len(typescript_snippets):
# Insert an extra newline for clarity, then the translated TypeScript snippet.
output_lines.append("\n")
output_lines.append(typescript_snippets[snippet_index])
output_lines.append("\n")
snippet_index += 1
return "".join(output_lines)
def main(file_path: str) -> None:
# Read the markdown file.
with open(file_path, "r") as f:
markdown_content = f.read()
# 1. Extract all Python snippets.
python_snippets = extract_python_snippets(markdown_content)[:1]
# 2. Translate each Python snippet to TypeScript.
typescript_snippets = []
# Replace with .batch() for faster translation
for python_snippet in _tqdm(python_snippets):
ts_snippet = translate_snippet(python_snippet)
typescript_snippets.append(ts_snippet)
# 3. Insert the TypeScript translations after their respective Python snippets.
updated_markdown = insert_translations_into_markdown(
markdown_content, typescript_snippets
)
# Overwrite the original markdown file with the updated content.
with open(file_path, "w") as f:
f.write(updated_markdown)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
)
parser.add_argument("file_path", type=str, help="Path to the markdown file.")
args = parser.parse_args()
main(args.file_path)
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import tiktoken
# This will trigger the download and caching of the necessary files
for encoding in ("gpt2", "gpt-3.5"):
tiktoken.encoding_for_model(encoding)
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#!/bin/bash
# Read the list of notebooks to skip from the JSON file
SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
# Function to execute a single notebook
execute_notebook() {
file="$1"
echo "Starting execution of $file"
start_time=$(date +%s)
if ! output=$(time uv run jupyter execute "$file" 2>&1); then
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Error in $file. Execution time: $execution_time seconds"
echo "Error details: $output"
exit 1
fi
end_time=$(date +%s)
execution_time=$((end_time - start_time))
echo "Finished $file. Execution time: $execution_time seconds"
}
export -f execute_notebook
# Check if custom notebook paths are provided
if [ $# -gt 0 ]; then
notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
else
# Find all notebooks and filter out those in the skip list
notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
fi
# Execute notebooks sequentially
for file in $notebooks; do
execute_notebook "$file"
done
@@ -0,0 +1,284 @@
import ast
import importlib
import logging
import re
from functools import lru_cache
from typing import List, Optional
from typing_extensions import TypedDict
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Base URL for all class documentation
_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
# (alias/re-exported modules, source module, class, docs namespace)
MANUAL_API_REFERENCES_LANGGRAPH = [
(
["langgraph.prebuilt"],
"langgraph.prebuilt.chat_agent_executor",
"create_react_agent",
"prebuilt",
),
(
[],
"langgraph.prebuilt.chat_agent_executor",
"AgentState",
"prebuilt",
),
(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"tools_condition",
"prebuilt",
),
(
["langgraph.prebuilt"],
"langgraph.prebuilt.tool_node",
"InjectedState",
"prebuilt",
),
# Graph
(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
(["langgraph.graph"], "langgraph.constants", "START", "constants"),
(["langgraph.graph"], "langgraph.constants", "END", "constants"),
(["langgraph.constants"], "langgraph.types", "Send", "types"),
(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
(["langgraph.constants"], "langgraph.types", "interrupt", "types"),
(["langgraph.constants"], "langgraph.types", "Command", "types"),
(["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
(["langgraph.config"], "langgraph.config", "get_store", "config"),
(["langgraph.func"], "langgraph.func", "entrypoint", "func"),
(["langgraph.func"], "langgraph.func", "task", "func"),
(["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
(["langgraph.types"], "langgraph.types", "StreamMode", "types"),
(["langgraph.types"], "langgraph.types", "StreamWriter", "types"),
([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
# other prebuilts
(["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
(["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
(["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
]
WELL_KNOWN_LANGGRAPH_OBJECTS = {
(module_, class_): (source_module, namespace)
for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
for module_ in modules + [source_module]
}
@lru_cache(maxsize=10_000)
def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
"""Get full module name using inspect, with LRU cache to memoize results."""
try:
module = importlib.import_module(module_path)
symbol = getattr(module, class_name)
# First check the __module__ attribute on the symbol.
mod_name = getattr(symbol, "__module__", None)
# If __module__ is not set or comes from typing,
# assume the definition is in module_path.
if mod_name is None or mod_name.startswith("typing"):
return module_path
return mod_name
except AttributeError as e:
logger.warning(f"API Reference: Could not find module for {class_name}, {e}")
return None
except ImportError as e:
logger.warning(f"API Reference: Failed to load for class {class_name}, {e}")
return None
class ImportInformation(TypedDict):
imported: str # The name of the class that was imported.
source: str # The full module path from which the class was imported.
docs: str # The URL pointing to the class's documentation.
path: str # The path of the file where the markdown content originated.
def get_imports(code: str, path: str) -> List[ImportInformation]:
"""Retrieve all import references from the given code for specified ecosystems.
Args:
code: The source code from which to extract import references.
path: The path of the file where the markdown content originated.
Returns:
A list of import information for each import found.
"""
# Parse the code into an AST.
try:
tree = ast.parse(code)
except SyntaxError:
return []
found_imports = []
# Walk through the AST and process ImportFrom nodes.
for node in ast.walk(tree):
if isinstance(node, ast.ImportFrom):
# node.module is the source module.
if node.module is None:
continue
for alias in node.names:
if not (
node.module.startswith("langchain")
or node.module.startswith("langgraph")
):
continue
found_imports.append(
{
"source": node.module,
# alias.name is the original name even if an alias exists.
"imported": alias.name,
}
)
imports: list[ImportInformation] = []
for found_import in found_imports:
module = found_import["source"]
if module.startswith("langchain_mcp_adapters"):
package_ecosystem = "langgraph"
elif module.startswith("langchain"):
# Handles things like `langchain` or `langchain_anthropic`
package_ecosystem = "langchain"
elif module.startswith("langgraph"):
package_ecosystem = "langgraph"
else:
continue
class_name = found_import["imported"]
module_path = _get_full_module_name(module, class_name)
if not module_path:
continue
if len(module_path.split(".")) < 2:
continue
if package_ecosystem == "langchain":
pkg = module_path.split(".")[0].replace("langchain_", "")
top_level_mod = module_path.split(".")[1]
url = (
_LANGCHAIN_API_REFERENCE
+ pkg
+ "/"
+ top_level_mod
+ "/"
+ module_path
+ "."
+ class_name
+ ".html"
)
elif package_ecosystem == "langgraph":
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
# Likely not documented yet
continue
source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
(module, class_name)
]
url = (
_LANGGRAPH_API_REFERENCE
+ namespace
+ "/#"
+ source_module
+ "."
+ class_name
)
else:
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
# Add the import information to our list
imports.append(
{
"imported": class_name,
"source": module,
"docs": url,
"path": path,
}
)
return imports
def update_markdown_with_imports(markdown: str, path: str) -> str:
"""Update markdown to include API reference links for imports in Python code blocks.
This function scans the markdown content for Python code blocks, extracts any
imports, and appends links to their API documentation.
Args:
markdown: The markdown content to process.
path: The path of the file where the markdown content originated.
Returns:
Updated markdown with API reference links prepended to Python code blocks.
Example:
Given a markdown with a Python code block:
```python
from langchain.nlp import TextGenerator
```
This function will append an API reference link to the `TextGenerator` class
from the `langchain.nlp` module if it's recognized.
"""
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```",
re.DOTALL,
)
def replace_code_block(match: re.Match) -> str:
"""Replace the matched code block with additional API reference links if imports are found.
Args:
match (re.Match): The regex match object containing the code block.
Returns:
str: The modified code block with API reference links prepended if applicable.
"""
indent = match.group("indent")
code_block = match.group("code")
# Retrieve import information from the code block
imports = get_imports(code_block, "__unused__")
original_code_block = match.group(0)
# If no imports are found, return the original code block
if not imports:
return original_code_block
# Generate API reference links for each import
api_links = " | ".join(
f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
)
# Return the code block with prepended API reference links
return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
# Apply the replace_code_block function to all matches in the markdown
updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
return updated_markdown
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"""Experimental script to generate consolidated llms text from the docs."""
import asyncio
import glob
import os
from typing import TypedDict, List, Optional
import pydantic
import re
from pydantic import BaseModel, Field
from langchain_core.rate_limiters import InMemoryRateLimiter
import yaml
from langchain.chat_models import init_chat_model
from mkdocs.structure.files import File
from mkdocs.structure.pages import Page
from yaml import SafeLoader
from _scripts.notebook_hooks import _on_page_markdown_with_config
HERE = os.path.dirname(os.path.abspath(__file__))
# Get source directory (parent of HERE / docs)
SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
async def convert_ipynb_to_md(file_path: str) -> Optional[str]:
"""Process a file (markdown or notebook) to markdown format.
Args:
file_path: Path to the file to process
Returns:
Processed markdown content if successful, None otherwise
"""
rel_path = os.path.relpath(file_path, SOURCE_DIR)
# Create File and Page objects to match mkdocs structure
file_obj = File(
path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
)
page = Page(
title="",
file=file_obj,
config={},
)
try:
# Read raw content
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
# Convert to markdown without logic to resolve API references
processed_content = _on_page_markdown_with_config(
content, page, add_api_references=False, remove_base64_images=True
)
# Remove self-closing img tags <img ... />
processed_content = re.sub(r"<img[^>]*/>", "", processed_content)
# Remove img tags with content <img ...>...</img>
processed_content = re.sub(
r"<img[^>]*>.*?</img>", "", processed_content, flags=re.DOTALL
)
return processed_content
except Exception as e:
print(f"Error processing file {file_path}: {e}")
return None
async def generate_full_llms_text(output_file: str) -> None:
"""Generate a consolidated text file from markdown/notebook files for LLM training.
Args:
output_file: Path to output the consolidated text file
"""
# Collect all markdown and notebook files
all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
)
all_files.extend(
glob.glob(os.path.join(SOURCE_DIR, "concepts/*.ipynb"), recursive=True)
)
all_content = []
# Process files concurrently
tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
results = await asyncio.gather(*tasks)
# Combine results with file paths
for file_path, processed_content in zip(all_files, results):
if processed_content:
rel_path = os.path.relpath(file_path, SOURCE_DIR)
# Add file name
all_content.append(f"---\n{rel_path}\n---")
# Add content
all_content.append(processed_content)
# Write consolidated output
with open(output_file, "w", encoding="utf-8") as f:
f.write("\n\n".join(all_content))
def no_op_constructor(*args):
"""No-op"""
SafeLoader.add_multi_constructor(
"tag:yaml.org,2002:python/name",
no_op_constructor,
)
class NavItem(TypedDict):
title: str
url: str
hierarchy: tuple[str, ...]
description: str
def _flatten_nav(
nav: list[dict[str, str | list] | str], path: tuple[str, ...] = ()
) -> list[NavItem]:
flat: List[NavItem] = []
for item in nav:
if isinstance(item, dict):
for title, node in item.items():
new_path = path + (title,)
if isinstance(node, str):
# Leaf page
flat.append(
{
"title": title,
"url": node,
"hierarchy": new_path,
"description": "",
}
)
elif isinstance(node, list):
# Dive in, carrying along the updated path
flat.extend(_flatten_nav(node, new_path))
else:
raise TypeError(
f"Unexpected node type {type(node)} under {title!r}"
)
elif isinstance(item, str):
# Bare string entry → use itself as title, and as URL
new_path = path + (item,)
flat.append(
{"title": item, "url": item, "hierarchy": new_path, "description": ""}
)
else:
raise TypeError(f"Unexpected item type {type(item)} in nav")
return flat
class PageInfo(BaseModel):
title: str = Field(description="The title of the page")
description: str = Field(
description="A short description of the page no longer than 3 sentences "
"explaining the kind of content that can be found in the page."
)
async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
"""Open the contents of each nav item and come up with a better title and description."""
rate_limiter = InMemoryRateLimiter(requests_per_second=10)
model = init_chat_model("gpt-4o-mini", temperature=0.0, rate_limiter=rate_limiter)
model = model.with_structured_output(PageInfo)
async def process_single_item(item: NavItem) -> NavItem:
path = item["url"]
file_path = os.path.join(SOURCE_DIR, path)
# Process the file content (handles both markdown and notebooks)
if path.endswith(".ipynb"):
content = await convert_ipynb_to_md(file_path)
else:
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
if not content:
return item
# Generate a better title and description
response = await model.ainvoke(
[
{
"role": "system",
"content": "You are a technical documentation writer. "
"You are given a markdown page of documentation. "
"Please come up with an appropriate title and "
"description for the page. The description should "
"be a short summary of the page content that is "
"no longer than 3 sentences.",
},
{
"role": "user",
"content": "The markdown page is as follows:\n\n" + content,
},
]
)
return {
"title": response.title,
"url": item["url"],
"hierarchy": item["hierarchy"],
"description": response.description,
}
# Remove any items that start with http:// or https:// looking only for
# local file at this stages.
nav_items = [
item for item in nav_items if not item["url"].startswith(("http://", "https://"))
]
# Process items in parallel
tasks = [process_single_item(item) for item in nav_items]
new_nav_items = await asyncio.gather(*tasks)
return new_nav_items
async def generate_nav_links_text(
output_file: str, *, replace_links: bool = False
) -> None:
"""Generate llms.txt from mkdocs.yaml."""
# Get path to mkdocs.yaml relative to this script
script_dir = os.path.dirname(os.path.abspath(__file__))
mkdocs_path = os.path.join(os.path.dirname(script_dir), "mkdocs.yml")
# Load and parse yaml
with open(mkdocs_path, "r") as f:
config = yaml.safe_load(f)
# Extract nav section
nav = config.get("nav", [])
flattened = _flatten_nav(nav)
processed_nav = await process_nav_items(flattened)
with open(output_file, "w") as f:
current_section = None
for item in processed_nav:
# Get the top-level section (first item in hierarchy)
section = item["hierarchy"][0]
if section not in {"Guides", "Examples", "Resources"}:
continue
# If we're starting a new section, add a heading
if section != current_section:
f.write(f"\n# {section}\n\n")
current_section = section
title = item["title"]
# Process URL based on replace_links flag
url = item["url"]
if replace_links:
# Remove .md extension and ensure single trailing slash
url = url.removesuffix(".md")
url = url.removesuffix(".ipynb")
url = url.rstrip("/") + "/"
url = f"https://langchain-ai.github.io/langgraph/{url}"
f.write(f"- [{title}]({url}): {item['description']}\n")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description=(
"Generate consolidated text file from markdown/notebook files for LLMs."
)
)
parser.add_argument("output_file", help="Path to output the consolidated text file")
parser.add_argument(
"--link-only",
action="store_true",
help="Only include link references in the output",
)
parser.add_argument(
"--replace-links",
action="store_true",
help="Replace markdown links with full URLs in the output",
)
args = parser.parse_args()
if args.link_only:
coro = generate_nav_links_text(
args.output_file, replace_links=args.replace_links
)
else:
coro = generate_full_llms_text(args.output_file)
asyncio.run(coro)
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import ast
import os
import re
from typing import Literal
import nbformat
from nbconvert.exporters import MarkdownExporter
from nbconvert.preprocessors import Preprocessor
def _uses_input(source: str) -> bool:
"""Parse the source code to determine if it uses the input() function."""
try:
tree = ast.parse(source)
except SyntaxError:
# If there's a syntax error, assume input() might be present to be safe.
return False
for node in ast.walk(tree):
if isinstance(node, ast.Call):
# Check if the function called is named 'input'
if isinstance(node.func, ast.Name) and node.func.id == "input":
return True
return False
def _rewrite_cell_magic(code: str) -> str:
"""Process a code block that uses cell magic.
- Lines starting with "%%capture" are ignored.
- Lines starting with "%pip" are rewritten by removing the leading "%" character.
- Any other non-empty line causes a NotImplementedError.
Args:
code (str): The original code block.
Returns:
str: The transformed code block.
Raises:
NotImplementedError: If a line doesn't start with either "%%capture" or "%pip".
"""
rewritten_lines = []
for line in code.splitlines():
stripped = line.strip()
# Skip empty lines
if not stripped:
continue
# Ignore %%capture lines
if stripped.startswith("%%capture"):
continue
# Rewrite %pip lines by dropping the '%'
elif stripped.startswith("%") or stripped.startswith("!"):
# Drop the leading '%' character and then drop all leading whitespace
stripped = stripped.lstrip("%! \t")
# Check if the line starts with "pip"
if stripped.startswith("pip"):
rewritten_lines.append(stripped)
else:
raise NotImplementedError(f"Unhandled line: {line}")
else:
raise NotImplementedError(f"Unhandled line: {line}")
return "\n".join(rewritten_lines)
class PrintCallVisitor(ast.NodeVisitor):
"""
This visitor sets self.has_print to True if it encounters a call
to a print within the global scope.
This should catch calls to print(), print_stream(), etc. (Prefixed with "print").
May have some false positives, but it's not meant to be perfect.
Temporary code for notebook conversion.
"""
def __init__(self):
self.has_print = False
self.scope_level = 0 # counter to track whether we're inside a def/lambda
def visit_FunctionDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_AsyncFunctionDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_Lambda(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_ClassDef(self, node):
self.scope_level += 1
self.generic_visit(node)
self.scope_level -= 1
def visit_Call(self, node):
# Only consider calls when not inside a function definition.
if self.scope_level == 0:
if isinstance(node.func, ast.Name) and node.func.id.startswith("print"):
self.has_print = True
self.generic_visit(node)
def _has_output(source: str) -> bool:
"""Determine if the code block is expected to produce output.
Args:
source (str): The source code of the code block.
Returns:
True if the code block is expected to produce output, False otherwise.
Must meet the following conditions:
1. There is a call to a printing function (name starts with "print")
that is not inside a function definition.
2. The last top-level statement is an expression that is valid if:
- It is any expression (including calls) AND
- It is NOT a call to `display(...)`.
`display` isn't handled currently by markdown-exec
"""
try:
tree = ast.parse(source)
except SyntaxError:
return False
# Condition (1): Check for a global print-like call.
visitor = PrintCallVisitor()
visitor.visit(tree)
condition_a = visitor.has_print
# Condition (2): Check the last top-level statement.
condition_b = False
if tree.body:
last_stmt = tree.body[-1]
if isinstance(last_stmt, ast.Expr):
# If the expression is a call, ensure it's not a call to "display"
if isinstance(last_stmt.value, ast.Call):
if (
isinstance(last_stmt.value.func, ast.Name)
and last_stmt.value.func.id == "display"
):
condition_b = False # exclude display-wrapped expressions
else:
condition_b = True
else:
# Any other expression qualifies.
condition_b = True
return condition_a or condition_b
def _convert_links_in_markdown(markdown: str) -> str:
"""Convert links present in notebook markdown cells to standardized format.
We want to update markdown links code cells by linking to markdown
files rather than assuming that the link is to the finalized HTML.
This code is needed temporarily since the markdown links that are present
in ipython notebooks do not follow the same conventions as regular markdown
files in mkdocs (which should link to a .md file).
"""
# Define the regex pattern in parts for clarity:
pattern = (
r"(?<!!)" # Negative lookbehind: ensure the link is not an image (i.e., doesn't start with "!")
r"\[" # Literal '[' indicating the start of the link text.
r"(?P<text>[^\]]*)" # Named group 'text': match any characters except ']', representing the link text.
r"\]" # Literal ']' indicating the end of the link text.
r"\(" # Literal '(' indicating the start of the URL.
r"(?![^\)]*//)" # Negative lookahead: ensure that the URL does not contain '//' (skip absolute URLs).
r"(?P<url>[^)]*)" # Named group 'url': match any characters except ')', representing the URL.
r"\)" # Literal ')' indicating the end of the URL.
)
def custom_replacement(match):
"""logic will correct the link format used in ipython notebooks
Ipython notebooks were being converted directly into HTML links
instead of markdown links that retain the markdown extension.
It needs to handle the following cases:
- optional fragments (e.g., `#section`)
e.g., `[text](url/#section)` -> `[text](url.md#section)`
e.g., `[text](url#section)` -> `[text](url.md#section)`
- relative paths (e.g., `../path/to/file`) need to be denested by 1 level
"""
text = match.group("text")
url = match.group("url")
if url.startswith("../"):
# we strip the "../" from the start of the URL
# We only need to denest one level.
url = url[3:]
url = url.rstrip("/") # Strip `/` from the end of the URL
# if url has a fragment
if "#" in url:
url, fragment = url.split("#")
url = url.rstrip("/")
# Strip `/` from the end of the URL
return f"[{text}]({url}.md#{fragment})"
# Otherwise add the .md extension
return f"[{text}]({url}.md)"
return re.sub(
pattern,
custom_replacement,
markdown,
)
class HideCellTagPreprocessor(Preprocessor):
"""
Removes cells that have '# hide-cell' at the beginning of the cell content.
This allows authors to include cells in the notebook that should not
appear in the generated markdown output.
"""
def preprocess(self, nb, resources):
# Filter out cells with the '# hide-cell' comment at the beginning
nb.cells = [
cell
for cell in nb.cells
if not (cell.source.strip().startswith("# hide-cell"))
]
return nb, resources
class EscapePreprocessor(Preprocessor):
def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
super().__init__(**kwargs)
self.markdown_exec_migration = markdown_exec_migration
def preprocess_cell(self, cell, resources, cell_index):
if cell.cell_type == "markdown":
if not self.markdown_exec_migration:
# Old logic is to convert ipynb links to HTML links
cell.source = re.sub(
r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
r'<a href="\2">\1</a>',
cell.source,
)
else:
cell.source = _convert_links_in_markdown(cell.source)
# Fix image paths in <img> tags
cell.source = re.sub(
r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
)
elif cell.cell_type == "code":
# Determine if the cell has bash or cell magic
source = cell.source
is_exec = not (
source.startswith("%") or source.startswith("!") or _uses_input(source)
)
cell.metadata["exec"] = is_exec
# For markdown exec migration we'll re-write cell magic as bash commands
if source.startswith("%%"):
cell.source = _rewrite_cell_magic(source)
cell.metadata["language"] = "shell"
# Remove noqa comments
cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
# escape ``` in code
# This is needed because the markdown exporter will wrap code blocks in
# triple backticks, which will break the markdown output if the code block
# contains triple backticks.
cell.source = cell.source.replace("```", r"\`\`\`")
# escape ``` in output
if "outputs" in cell:
filter_out = set()
for i, output in enumerate(cell["outputs"]):
if "text" in output:
if not output["text"].strip():
filter_out.add(i)
continue
value = output["text"].replace("```", r"\`\`\`")
# handle a funky case w/ references in text
value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
output["text"] = value
elif "data" in output:
for key, value in output["data"].items():
if isinstance(value, str):
value = value.replace("```", r"\`\`\`")
# handle a funky case w/ references in text
output["data"][key] = re.sub(
r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
)
cell["outputs"] = [
output
for i, output in enumerate(cell["outputs"])
if i not in filter_out
]
return cell, resources
class ExtractAttachmentsPreprocessor(Preprocessor):
"""
Extracts all of the outputs from the notebook file. The extracted
outputs are returned in the 'resources' dictionary.
"""
def preprocess_cell(self, cell, resources, cell_index):
"""
Apply a transformation on each cell,
Parameters
----------
cell : NotebookNode cell
Notebook cell being processed
resources : dictionary
Additional resources used in the conversion process. Allows
preprocessors to pass variables into the Jinja engine.
cell_index : int
Index of the cell being processed (see base.py)
"""
# Get files directory if it has been specified
# Make sure outputs key exists
if not isinstance(resources["outputs"], dict):
resources["outputs"] = {}
# Loop through all of the attachments in the cell
for name, attach in cell.get("attachments", {}).items():
for mime, data in attach.items():
if mime not in {
"image/png",
"image/jpeg",
"image/svg+xml",
"application/pdf",
}:
continue
# attachments are pre-rendered. Only replace markdown-formatted
# images with the following logic
attach_str = f"({name})"
if attach_str in cell.source:
data = f"(data:{mime};base64,{data})"
cell.source = cell.source.replace(attach_str, data)
return cell, resources
exporter = MarkdownExporter(
preprocessors=[
HideCellTagPreprocessor,
EscapePreprocessor,
ExtractAttachmentsPreprocessor,
],
template_name="mdoutput",
extra_template_basedirs=[
os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
],
)
def convert_notebook(
notebook_path: str,
mode: Literal["markdown", "exec"] = "markdown",
) -> str:
with open(notebook_path) as f:
nb = nbformat.read(f, as_version=4)
nb.metadata.mode = mode
body, _ = exporter.from_notebook_node(nb)
return body
@@ -0,0 +1,5 @@
{
"mimetypes": {
"text/markdown": true
}
}
@@ -0,0 +1,50 @@
{% extends 'markdown/index.md.j2' %}
{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
```{%- if 'language' in cell.metadata -%}
{{ cell.metadata.language }}
{%- elif 'magics_language' in cell.metadata -%}
{{ cell.metadata.magics_language }}
{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
{{ nb.metadata.language_info.name }}
{%- endif %}
{{ cell.source }}
```
{% endblock input %}
{%- block traceback_line -%}
```output
{{ line.rstrip() | strip_ansi }}
```
{%- endblock traceback_line -%}
{%- block stream -%}
```output
{{ output.text.rstrip() | strip_ansi }}
```
{%- endblock stream -%}
{%- block data_text scoped -%}
```output
{{ output.data['text/plain'].rstrip() | strip_ansi }}
```
{%- endblock data_text -%}
{%- block data_html scoped -%}
```html
{{ output.data['text/html'] | safe }}
```
{%- endblock data_html -%}
{%- block data_jpg scoped -%}
<p>
<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
</p>
{%- endblock data_jpg -%}
{%- block data_png scoped -%}
<p>
<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
</p>
{%- endblock data_png -%}
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@@ -0,0 +1,379 @@
"""mkdocs hooks for adding custom logic to documentation pipeline.
Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
"""
import logging
import os
import posixpath
import re
from typing import Any, Dict
from bs4 import BeautifulSoup
from mkdocs.config.defaults import MkDocsConfig
from mkdocs.structure.files import Files, File
from mkdocs.structure.pages import Page
from _scripts.generate_api_reference_links import update_markdown_with_imports
from _scripts.notebook_convert import convert_notebook
logger = logging.getLogger(__name__)
logging.basicConfig()
logger.setLevel(logging.INFO)
DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
REDIRECT_MAP = {
# lib redirects
"how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
"how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
"how-tos/streaming-content.ipynb": "how-tos/streaming.md",
"how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
"how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
"how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
"how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
# graph-api
"how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
"how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
"how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
"how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
"how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
"how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
"how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
"how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
"how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
"how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
"how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
"how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
"how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
"how-tos/async.ipynb": "how-tos/graph-api/#async",
# memory how-tos
"how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
"how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
"how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
# subgraph how-tos
"how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
"how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
# persistence how-tos
"how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
"how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
"how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
"how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
"how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
"cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
# tool calling how-tos
"how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
"how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
"how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
"how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
# multi-agent how-tos
"how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
"how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
"how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
# cloud redirects
"cloud/index.md": "index.md",
"cloud/how-tos/index.md": "concepts/langgraph_platform",
"cloud/concepts/api.md": "concepts/langgraph_server.md",
"cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
"cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
"cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
"cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
"concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
# cloud streaming redirects
"cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
"cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
"cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
"cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
"cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
"cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
# prebuit redirects
"how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
"how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
"how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
"how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
"how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
# Time-travel
"how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.ipynb",
# breakpoints
"how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
# misc
"prebuilt.md": "agents/prebuilt.md",
"reference/prebuilt.md": "reference/agents.md",
"concepts/high_level.md": "index.md",
"concepts/index.md": "index.md",
"concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
"how-tos/index.md": "index.md",
"tutorials/introduction.ipynb": "concepts/why-langgraph.md",
# deployment redirects
"how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
"concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
# assistant redirects
"cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
}
class NotebookFile(File):
def is_documentation_page(self):
return True
def on_files(files: Files, **kwargs: Dict[str, Any]):
if DISABLED:
return files
new_files = Files([])
for file in files:
if file.src_path.endswith(".ipynb"):
new_file = NotebookFile(
path=file.src_path,
src_dir=file.src_dir,
dest_dir=file.dest_dir,
use_directory_urls=file.use_directory_urls,
)
new_files.append(new_file)
else:
new_files.append(file)
return new_files
def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
"""Add the path to the code blocks."""
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
r"(?P<code>((?:.*\n)*?))" # Capture the code inside the block using named group
r"(?P=indent)```" # Match closing backticks with the same indentation
)
def replace_code_block_header(match: re.Match) -> str:
indent = match.group("indent")
language = match.group("language")
attributes = match.group("attributes").rstrip()
if 'exec="on"' not in attributes:
# Return original code block
return match.group(0)
code = match.group("code")
return f'{indent}```{language} {attributes} path="{page.file.src_path}"\n{code}{indent}```'
return code_block_pattern.sub(replace_code_block_header, markdown)
def _highlight_code_blocks(markdown: str) -> str:
"""Find code blocks with highlight comments and add hl_lines attribute.
Args:
markdown: The markdown content to process.
Returns:
updated Markdown code with code blocks containing highlight comments
updated to use the hl_lines attribute.
"""
# Pattern to find code blocks with highlight comments and without
# existing hl_lines for Python and JavaScript
# Pattern to find code blocks with highlight comments, handling optional indentation
code_block_pattern = re.compile(
r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
r"(?P<code>((?:.*\n)*?))" # Capture the code inside the block using named group
r"(?P=indent)```" # Match closing backticks with the same indentation
)
def replace_highlight_comments(match: re.Match) -> str:
indent = match.group("indent")
language = match.group("language")
code_block = match.group("code")
attributes = match.group("attributes").rstrip()
# Account for a case where hl_lines is manually specified
if "hl_lines" in attributes:
# Return original code block
return match.group(0)
lines = code_block.split("\n")
highlighted_lines = []
# Skip initial empty lines
while lines and not lines[0].strip():
lines.pop(0)
lines_to_keep = []
comment_syntax = (
"# highlight-next-line"
if language in ["py", "python"]
else "// highlight-next-line"
)
for line in lines:
if comment_syntax in line:
count = len(lines_to_keep) + 1
highlighted_lines.append(str(count))
else:
lines_to_keep.append(line)
# Reconstruct the new code block
new_code_block = "\n".join(lines_to_keep)
# Construct the full code block that also includes
# the fenced code block syntax.
opening_fence = f"```{language}"
if attributes:
opening_fence += f" {attributes}"
if highlighted_lines:
opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
return (
# The indent and opening fence
f"{indent}{opening_fence}\n"
# The indent and terminating \n is already included in the code block
f"{new_code_block}"
f"{indent}```"
)
# Replace all code blocks in the markdown
markdown = code_block_pattern.sub(replace_highlight_comments, markdown)
return markdown
def _on_page_markdown_with_config(
markdown: str,
page: Page,
*,
add_api_references: bool = True,
remove_base64_images: bool = False,
**kwargs: Any,
) -> str:
if DISABLED:
return markdown
if page.file.src_path.endswith(".ipynb"):
# logger.info("Processing Jupyter notebook: %s", page.file.src_path)
markdown = convert_notebook(page.file.abs_src_path)
# Append API reference links to code blocks
if add_api_references:
markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
# Apply highlight comments to code blocks
markdown = _highlight_code_blocks(markdown)
# Add file path as an attribute to code blocks that are executable.
# This file path is used to associate fixtures with the executable code
# which can be used in CI to test the docs without making network requests.
markdown = _add_path_to_code_blocks(markdown, page)
if remove_base64_images:
# Remove base64 encoded images from markdown
markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
return markdown
def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
return _on_page_markdown_with_config(
markdown,
page,
add_api_references=True,
**kwargs,
)
# redirects
HTML_TEMPLATE = """
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting...</title>
<link rel="canonical" href="{url}">
<meta name="robots" content="noindex">
<script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
<meta http-equiv="refresh" content="0; url={url}">
</head>
<body>
Redirecting...
</body>
</html>
"""
def _write_html(site_dir, old_path, new_path):
"""Write an HTML file in the site_dir with a meta redirect to the new page"""
# Determine all relevant paths
old_path_abs = os.path.join(site_dir, old_path)
old_dir_abs = os.path.dirname(old_path_abs)
# Create parent directories if they don't exist
if not os.path.exists(old_dir_abs):
os.makedirs(old_dir_abs)
# Write the HTML redirect file in place of the old file
content = HTML_TEMPLATE.format(url=new_path)
with open(old_path_abs, "w", encoding="utf-8") as f:
f.write(content)
def _inject_gtm(html: str) -> str:
"""Inject Google Tag Manager code into the HTML.
Code to inject Google Tag Manager noscript tag immediately after <body>.
This is done via hooks rather than via a template because the MkDocs material
theme does not seem to allow placing the code immediately after the <body> tag
without modifying the template files directly.
Args:
html: The HTML content to modify.
Returns:
The modified HTML content with GTM code injected.
"""
# Code was copied from Google Tag Manager setup instructions.
gtm_code = """
<!-- Google Tag Manager (noscript) -->
<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T35S4S46"
height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
<!-- End Google Tag Manager (noscript) -->
"""
soup = BeautifulSoup(html, "html.parser")
body = soup.body
if body:
# Insert the GTM code as raw HTML at the top of <body>
body.insert(0, BeautifulSoup(gtm_code, "html.parser"))
return str(soup)
else:
return html # fallback if no <body> found
def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
"""Inject Google Tag Manager noscript tag immediately after <body>.
Args:
output: The HTML output of the page.
page: The page instance.
config: The MkDocs configuration object.
Returns:
modified HTML output with GTM code injected.
"""
return _inject_gtm(output)
# Create HTML files for redirects after site dir has been built
def on_post_build(config):
use_directory_urls = config.get("use_directory_urls")
for page_old, page_new in REDIRECT_MAP.items():
page_old = page_old.replace(".ipynb", ".md")
page_new = page_new.replace(".ipynb", ".md")
page_new_before_hash, hash, suffix = page_new.partition("#")
old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
os.sep, "/"
)
new_html_path = File(page_new_before_hash, "", "", True).url
new_html_path = (
posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
+ hash
+ suffix
)
_write_html(config["site_dir"], old_html_path, new_html_path)
+263
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@@ -0,0 +1,263 @@
"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
import logging
import os
import json
import click
import nbformat
import re
logger = logging.getLogger(__name__)
NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
BLOCKLIST_COMMANDS = (
# skip if has WebBaseLoader to avoid caching web pages
"WebBaseLoader",
# skip if has draw_mermaid_png to avoid generating mermaid images via API
"draw_mermaid_png",
)
NOTEBOOKS_NO_CASSETTES = (
"docs/how-tos/visualization.ipynb",
)
NOTEBOOKS_NO_EXECUTION = [
# this uses a user provided project name for langsmith
"docs/tutorials/tnt-llm/tnt-llm.ipynb",
# this uses langsmith datasets
"docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
# this uses browser APIs
"docs/tutorials/web-navigation/web_voyager.ipynb",
# these RAG guides use an ollama model
"docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
"docs/tutorials/rag/langgraph_crag_local.ipynb",
"docs/tutorials/rag/langgraph_self_rag_local.ipynb",
# this loads a massive dataset from gcp
"docs/tutorials/usaco/usaco.ipynb",
# TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
"docs/how-tos/autogen-integration.ipynb",
"docs/how-tos/autogen-integration-functional.ipynb",
# TODO: need to update these notebooks to make sure they are runnable in CI
"docs/tutorials/storm/storm.ipynb", # issues only when running with VCR
"docs/tutorials/lats/lats.ipynb", # issues only when running with VCR
"docs/tutorials/rag/langgraph_crag.ipynb", # flakiness from tavily
"docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # flakiness only when running in GHA
"docs/tutorials/rag/langgraph_self_rag.ipynb", # flakiness only when running in GHA
"docs/tutorials/rag/langgraph_agentic_rag.ipynb", # flakiness only when running in GHA
"docs/how-tos/map-reduce.ipynb", # flakiness from structured output, only when running with VCR
"docs/tutorials/tot/tot.ipynb",
"docs/how-tos/visualization.ipynb",
"docs/how-tos/streaming-specific-nodes.ipynb",
"docs/tutorials/llm-compiler/LLMCompiler.ipynb",
"docs/tutorials/customer-support/customer-support.ipynb", # relies on openai embeddings, doesn't play well w/ VCR
"docs/how-tos/many-tools.ipynb", # relies on openai embeddings, doesn't play well w/ VCR
]
def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
for cell in notebook.cells:
if cell.cell_type != "code":
continue
if "pip install" in cell.source:
# Comment out the lines in cells containing "pip install"
cell.source = "\n".join(
f"# {line}" if line.strip() else line
for line in cell.source.splitlines()
)
return notebook
def is_magic_command(code: str) -> bool:
return code.strip().startswith("%") or code.strip().startswith("!")
def is_comment(code: str) -> bool:
return code.strip().startswith("#")
def has_blocklisted_command(code: str, metadata: dict) -> bool:
if 'hide_from_vcr' in metadata:
return True
code = code.strip()
for blocklisted_pattern in BLOCKLIST_COMMANDS:
if blocklisted_pattern in code:
return True
return False
MERMAID_PATTERN = re.compile(r'display\(Image\((\w+)\.get_graph\(\)\.draw_mermaid_png\(\)\)\)')
def remove_mermaid(code: str) -> str:
return MERMAID_PATTERN.sub('print()', code)
def add_vcr_to_notebook(
notebook: nbformat.NotebookNode, cassette_prefix: str
) -> nbformat.NotebookNode:
"""Inject `with vcr.cassette` into each code cell of the notebook."""
uses_langsmith = False
# Inject VCR context manager into each code cell
for idx, cell in enumerate(notebook.cells):
if cell.cell_type != "code":
continue
lines = cell.source.splitlines()
# remove the special tag for hidden cells
lines = [line for line in lines if not line.strip().startswith("# hide-cell")]
# skip if empty cell
if not lines:
continue
are_magic_lines = [is_magic_command(line) for line in lines]
# skip if all magic
if all(are_magic_lines):
continue
if any(are_magic_lines):
raise ValueError(
"Cannot process code cells with mixed magic and non-magic code."
)
# skip if just comments
if all(is_comment(line) or not line.strip() for line in lines):
continue
if has_blocklisted_command(cell.source, cell.metadata):
continue
cell_id = cell.get("id", idx)
cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
f" {line}" for line in lines
)
if any("hub.pull" in line or "from langsmith import" in line for line in lines):
uses_langsmith = True
# Add import statement
vcr_import_lines = []
if uses_langsmith:
vcr_import_lines.extend([
# patch urllib3 to handle vcr errors, see more here:
# https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
"import sys",
f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
"import _patch as patch_urllib3",
"patch_urllib3.patch_urllib3()",
])
vcr_import_lines.extend([
"import nest_asyncio",
"nest_asyncio.apply()",
"import vcr",
"import msgpack",
"import base64",
"import zlib",
"import os",
"os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
"custom_vcr = vcr.VCR()",
"",
"def compress_data(data, compression_level=9):",
" packed = msgpack.packb(data, use_bin_type=True)",
" compressed = zlib.compress(packed, level=compression_level)",
" return base64.b64encode(compressed).decode('utf-8')",
"",
"def decompress_data(compressed_string):",
" decoded = base64.b64decode(compressed_string)",
" decompressed = zlib.decompress(decoded)",
" return msgpack.unpackb(decompressed, raw=False)",
"",
"class AdvancedCompressedSerializer:",
" def serialize(self, cassette_dict):",
" return compress_data(cassette_dict)",
"",
" def deserialize(self, cassette_string):",
" return decompress_data(cassette_string)",
"",
"custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
"custom_vcr.serializer = 'advanced_compressed'",
])
import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
import_cell.pop("id", None)
notebook.cells.insert(0, import_cell)
return notebook
def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
for cell in notebook.cells:
if cell.cell_type != "code":
continue
cell.source = remove_mermaid(cell.source)
# skip the cell entirely if it contains PYPPETEER
if "PYPPETEER" in cell.source:
cell.source = ""
return notebook
def process_notebooks(should_comment_install_cells: bool) -> None:
for directory in NOTEBOOK_DIRS:
for root, _, files in os.walk(directory):
for file in files:
if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
continue
notebook_path = os.path.join(root, file)
try:
notebook = nbformat.read(notebook_path, as_version=4)
if should_comment_install_cells:
notebook = comment_install_cells(notebook)
base_filename = os.path.splitext(os.path.basename(file))[0]
cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
if notebook_path not in NOTEBOOKS_NO_CASSETTES:
notebook = add_vcr_to_notebook(
notebook, cassette_prefix=cassette_prefix
)
notebook = remove_mermaid_from_notebook(notebook)
if notebook_path in NOTEBOOKS_NO_EXECUTION:
# Add a cell at the beginning to indicate that this notebook should not be executed
warning_cell = nbformat.v4.new_markdown_cell(
source="**Warning:** This notebook is not meant to be executed automatically."
)
notebook.cells.insert(0, warning_cell)
# Add a special tag to the first code cell
if notebook.cells and notebook.cells[1].cell_type == "code":
notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
nbformat.write(notebook, notebook_path)
logger.info(f"Processed: {notebook_path}")
except Exception as e:
logger.error(f"Error processing {notebook_path}: {e}")
with open("notebooks_no_execution.json", "w") as f:
json.dump(NOTEBOOKS_NO_EXECUTION, f)
@click.command()
@click.option(
"--comment-install-cells",
is_flag=True,
default=False,
help="Whether to comment out install cells",
)
def main(comment_install_cells):
process_notebooks(should_comment_install_cells=comment_install_cells)
logger.info("All notebooks processed successfully.")
if __name__ == "__main__":
main()
+138
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@@ -0,0 +1,138 @@
#!/usr/bin/env python
"""Create the third party page for the documentation."""
import argparse
from typing import List
from typing import TypedDict
import yaml
MARKDOWN = """\
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
# Community Agents
If youre looking for other prebuilt libraries, explore the community-built options
below. These libraries can extend LangGraph's functionality in various ways.
## 📚 Available Libraries
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
{library_list}
## ✨ Contributing Your Library
Have you built an awesome open-source library using LangGraph? We'd love to feature
your project on the official LangGraph documentation pages! 🏆
To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
**Guidelines**
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
for JavaScript/TypeScript, etc.) 📦
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
the Functional API (exposing an `entrypoint`).
- The package must include documentation (e.g., a `README.md` or docs site)
explaining how to use it.
We'll review your contribution and merge it in!
Thanks for contributing! 🚀
"""
class ResolvedPackage(TypedDict):
name: str
"""The name of the package."""
repo: str
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
weekly_downloads: int | None
"""The weekly download count of the package."""
description: str
"""A brief description of what the package does."""
def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
"""Generate the markdown content for the third party page.
Args:
resolved_packages: A list of resolved package information.
language: str
Returns:
The markdown content as a string.
"""
# Update the URL to the actual file once the initial version is merged
if language == "python":
langgraph_url = (
"https://github.com/langchain-ai/langgraph/blob/main/docs"
"/_scripts/third_party_page/packages.yml"
)
elif language == "js":
langgraph_url = (
"https://github.com/langchain-ai/langgraphjs/blob/main/docs"
"/_scripts/third_party/packages.yml"
)
else:
raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
sorted_packages = sorted(
resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
)
rows = [
"| Name | GitHub URL | Description | Weekly Downloads | Stars |",
"| --- | --- | --- | --- | --- |",
]
for package in sorted_packages:
name = f"**{package['name']}**"
repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
stars_badge = (
f"https://img.shields.io/github/stars/{package['repo']}?style=social"
)
stars = f"![GitHub stars]({stars_badge})"
downloads = package["weekly_downloads"] or "-"
row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
rows.append(row)
markdown_content = MARKDOWN.format(
library_list="\n".join(rows), langgraph_url=langgraph_url
)
return markdown_content
def main(input_file: str, output_file: str, language: str) -> None:
"""Main function to create the third party page.
Args:
input_file: Path to the input YAML file containing resolved package information.
output_file: Path to the output file for the third party page.
language: The language for which to generate the third party page.
"""
# Parse the input YAML file
with open(input_file, "r") as f:
resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
markdown_content = generate_markdown(resolved_packages, language)
# Write the markdown content to the output file
with open(output_file, "w", encoding="utf-8") as f:
f.write(markdown_content)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Create the third party page.")
parser.add_argument(
"input_file",
help="Path to the input YAML file containing resolved package information.",
)
parser.add_argument(
"output_file", help="Path to the output file for the third party page."
)
parser.add_argument(
"--language",
choices=["python", "js"],
default="python",
help="The language for which to generate the third party page. Defaults to 'python'.",
)
args = parser.parse_args()
main(args.input_file, args.output_file, args.language)
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#!/usr/bin/env python
"""Retrieve download count for a list of Python packages from PyPI."""
import argparse
from datetime import datetime
from typing import TypedDict
import pathlib
import requests
import yaml
class Package(TypedDict):
"""A TypedDict representing a package"""
name: str
"""The name of the package."""
repo: str
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
description: str
"""A brief description of what the package does."""
class ResolvedPackage(Package):
weekly_downloads: int | None
HERE = pathlib.Path(__file__).parent
PACKAGES_FILE = HERE / "packages.yml"
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
"""Retrieve the monthly download count for a list of packages from PyPIStats."""
resolved_packages: list[ResolvedPackage] = []
if fake:
# To avoid making network requests during testing, return fake download counts
for package in packages:
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"weekly_downloads": -12345,
"description": package["description"],
}
)
return resolved_packages
for package in packages:
# First check if package exists on PyPI
pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
try:
pypi_response = requests.get(pypi_url)
pypi_response.raise_for_status()
except requests.exceptions.HTTPError:
raise AssertionError(f"Package {package['name']} does not exist on PyPI")
# Get first release date
pypi_data = pypi_response.json()
releases = pypi_data["releases"]
first_release_date = None
for version_releases in releases.values():
if version_releases: # Some versions may be empty lists
upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
if first_release_date is None or upload_time < first_release_date:
first_release_date = upload_time
if first_release_date is None:
raise AssertionError(f"Package {package['name']} has no releases yet")
# If package was published in last 48 hours, skip download stats
if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
url = f"https://pypistats.org/api/packages/{package['name']}/overall"
response = requests.get(url)
response.raise_for_status()
data = response.json()
sorted_data = sorted(
data["data"],
key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
reverse=True,
)
# Sum the last 7 days of downloads
num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
else:
num_downloads = None
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"weekly_downloads": num_downloads,
"description": package["description"],
}
)
return resolved_packages
def main(output_file: str, fake: bool) -> None:
"""Main function to generate package download information.
Args:
output_file: Path to the output YAML file.
"""
resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
if not output_file.endswith(".yml"):
raise ValueError("Output file must have a .yml extension")
with open(output_file, "w") as f:
f.write("# This file is auto-generated. Do not edit.\n")
yaml.dump(resolved_packages, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Generate package download information."
)
parser.add_argument(
"output_file",
help=(
"Path to the output YAML file. Example: python generate_downloads.py "
"downloads.yml"
),
)
parser.add_argument(
"--fake",
default=False,
action="store_true",
help=(
"Generate fake download counts for testing purposes. "
"This option will not make any network requests."
),
)
args = parser.parse_args()
main(args.output_file, args.fake)
@@ -0,0 +1,41 @@
#A list of third-party packages to surface on the third-party page.
packages:
- name: "trustcall"
repo: "hinthornw/trustcall"
description: "Tenacious tool calling built on LangGraph."
- name: "breeze-agent"
repo: "andrestorres123/breeze-agent"
description: "A streamlined research system built inspired on STORM and built on LangGraph."
- name: "langgraph-supervisor"
repo: "langchain-ai/langgraph-supervisor-py"
description: "Build supervisor multi-agent systems with LangGraph."
- name: "langmem"
repo: "langchain-ai/langmem"
description: "Build agents that learn and adapt from interactions over time."
- name: "langchain-mcp-adapters"
repo: "langchain-ai/langchain-mcp-adapters"
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
- name: "open-deep-research"
repo: "langchain-ai/open_deep_research"
description: "Open source assistant for iterative web research and report writing."
- name: "langgraph-swarm"
repo: "langchain-ai/langgraph-swarm-py"
description: "Build swarm-style multi-agent systems using LangGraph."
- name: "delve-taxonomy-generator"
repo: "andrestorres123/delve"
description: "A taxonomy generator for unstructured data"
- name: "nodeology"
repo: "xyin-anl/Nodeology"
description: "Enable researcher to build scientific workflows easily with simplified interface."
- name: "langgraph-bigtool"
repo: "langchain-ai/langgraph-bigtool"
description: "Build LangGraph agents with large numbers of tools."
- name: "ai-data-science-team"
repo: "business-science/ai-data-science-team"
description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
- name: "langgraph-reflection"
repo: "langchain-ai/langgraph-reflection"
description: "LangGraph agent that runs a reflection step."
- name: "langgraph-codeact"
repo: "langchain-ai/langgraph-codeact"
description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
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