feat(docs): update third party script

This commit is contained in:
Hunter Lovell
2025-07-27 01:41:31 -07:00
parent 11077affd6
commit a8ea374b83
5 changed files with 289 additions and 142 deletions
+1 -1
View File
@@ -19,7 +19,7 @@ build-prebuilt:
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
uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md
build-docs: build-typedoc build-prebuilt
TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
@@ -15,9 +15,10 @@ If youre looking for other prebuilt libraries, explore the community-built op
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}
:::python
{python_library_list}
## ✨ Contributing Your Library
@@ -28,16 +29,39 @@ To share your project, simply open a Pull Request adding an entry for your packa
**Guidelines**
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
for JavaScript/TypeScript, etc.) 📦
- Your repo must be distributed as an installable package on PyPI 📦
- 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! 🚀
:::
:::js
{js_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 on npm 📦
- 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! 🚀
:::
"""
@@ -46,36 +70,18 @@ class ResolvedPackage(TypedDict):
"""The name of the package."""
repo: str
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
monorepo_path: str | None
"""Optional: The path to the package in the monorepo. Must be relative to the root of the monorepo."""
language: str
"""The language of the package. (either 'python' or 'js')"""
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.
def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
"""Generate the package table for the third party page.
"""
# 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
)
@@ -85,7 +91,15 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
]
for package in sorted_packages:
name = f"**{package['name']}**"
repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
monorepo_path = package.get("monorepo_path", "")
if monorepo_path:
monorepo_path = monorepo_path[1:] if monorepo_path.startswith('/') else monorepo_path
repo_url_suffix = f"/tree/main/{monorepo_path}"
else:
repo_url_suffix = ""
repo_url = f"https://github.com/{package['repo']}{repo_url_suffix}"
stars_badge = (
f"https://img.shields.io/github/stars/{package['repo']}?style=social"
)
@@ -93,13 +107,39 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
downloads = package["weekly_downloads"] or "-"
row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
rows.append(row)
return "\n".join(rows)
def generate_markdown(resolved_packages: List[ResolvedPackage]) -> str:
"""Generate the markdown content for the third party page.
Args:
resolved_packages: A list of resolved package information.
Returns:
The markdown content as a string.
"""
# Update the URL to the actual file once the initial version is merged
langgraph_url = (
"https://github.com/langchain-ai/langgraph/blob/main/docs"
"/_scripts/third_party_page/packages.yml"
)
python_library_list = generate_package_table(
[p for p in resolved_packages if p["language"] == "python"]
)
js_library_list = generate_package_table(
[p for p in resolved_packages if p["language"] == "js"]
)
markdown_content = MARKDOWN.format(
library_list="\n".join(rows), langgraph_url=langgraph_url
python_library_list=python_library_list,
js_library_list=js_library_list,
langgraph_url=langgraph_url,
)
return markdown_content
def main(input_file: str, output_file: str, language: str) -> None:
def main(input_file: str, output_file: str) -> None:
"""Main function to create the third party page.
Args:
@@ -111,7 +151,7 @@ def main(input_file: str, output_file: str, language: str) -> None:
with open(input_file, "r") as f:
resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
markdown_content = generate_markdown(resolved_packages, language)
markdown_content = generate_markdown(resolved_packages)
# Write the markdown content to the output file
with open(output_file, "w", encoding="utf-8") as f:
@@ -127,12 +167,6 @@ if __name__ == "__main__":
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)
main(args.input_file, args.output_file)
@@ -11,101 +11,146 @@ 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]."""
monorepo_path: str | None
"""The path to the package in the monorepo. Only used for JS packages."""
description: str
"""A brief description of what the package does."""
class ResolvedPackage(Package):
weekly_downloads: int | None
"""The weekly download count of the package."""
language: str
"""The language of the package. (either 'python' or 'js')"""
HERE = pathlib.Path(__file__).parent
PACKAGES_FILE = HERE / "packages.yml"
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())["packages"]
def _get_pypi_downloads(package: Package) -> int:
"""Retrieve the weekly download count for a package from PyPIStats."""
def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
"""Retrieve the monthly download count for a list of packages from PyPIStats."""
# 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
return sum(entry["downloads"] for entry in sorted_data[:7])
else:
return None
def _get_npm_downloads(package: Package) -> int:
"""Retrieve the weekly download count for a package on the npm registry."""
# Check if package exists on the npm registry
npm_url = f"https://registry.npmjs.org/{package['name']}"
try:
npm_response = requests.get(npm_url)
npm_response.raise_for_status()
except requests.exceptions.HTTPError:
raise AssertionError(f"Package {package['name']} does not exist on npm registry")
npm_data = npm_response.json()
# Retrieve the first publish date using the 'created' timestamp from the 'time' field.
created_str = npm_data.get("time", {}).get("created")
if created_str is None:
raise AssertionError(f"Package {package['name']} has no creation time in registry data")
# Remove the trailing 'Z' if present and parse the ISO format timestamp
first_publish_date = datetime.fromisoformat(created_str.rstrip("Z"))
# If package was published more than 48 hours ago, fetch download stats.
if (datetime.now() - first_publish_date).total_seconds() >= 48 * 3600:
stats_url = f"https://api.npmjs.org/downloads/point/last-week/{package['name']}"
stats_response = requests.get(stats_url)
stats_response.raise_for_status()
stats_data = stats_response.json()
return stats_data.get("downloads", None)
else:
return None
def _get_weekly_downloads(packages: dict[str, list[Package]], fake: bool) -> list[ResolvedPackage]:
"""Retrieve the weekly download count for a dictionary of python or js packages."""
resolved_packages: list[ResolvedPackage] = []
if fake:
# To avoid making network requests during testing, return fake download counts
for package in packages:
for language, package_list in packages.items():
for package in package_list:
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"monorepo_path": package.get("monorepo_path", None),
"language": language,
"description": package["description"],
"weekly_downloads": -12345,
}
)
return resolved_packages
for language, package_list in packages.items():
for package in package_list:
if language == "python":
num_downloads = _get_pypi_downloads(package)
elif language == "js":
num_downloads = _get_npm_downloads(package)
else:
num_downloads = None
resolved_packages.append(
{
"name": package["name"],
"repo": package["repo"],
"weekly_downloads": -12345,
"monorepo_path": package.get("monorepo_path", None),
"language": language,
"description": package["description"],
"weekly_downloads": num_downloads,
}
)
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.
fake: If True, use fake download counts for testing purposes.
"""
resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
+56 -39
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@@ -1,41 +1,58 @@
#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."
python:
- 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."
js:
- name: "@langchain/mcp-adapters"
repo: "langchain-ai/langchainjs"
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
- name: "@langchain/langgraph-supervisor"
repo: "langchain-ai/langgraphjs"
monorepo_path: "libs/langgraph-supervisor"
description: "Build supervisor multi-agent systems with LangGraph"
- name: "@langchain/langgraph-swarm"
repo: "langchain-ai/langgraphjs"
monorepo_path: "libs/langgraph-swarm"
description: "Build multi-agent swarms with LangGraph"
- name: "@langchain/langgraph-cua"
repo: "langchain-ai/langgraphjs"
monorepo_path: "libs/langgraph-cua"
description: "Build computer use agents with LangGraph"
+51
View File
@@ -1,58 +1,109 @@
# This file is auto-generated. Do not edit.
- description: Tenacious tool calling built on LangGraph.
language: python
monorepo_path: null
name: trustcall
repo: hinthornw/trustcall
weekly_downloads: -12345
- description: A streamlined research system built inspired on STORM and built on
LangGraph.
language: python
monorepo_path: null
name: breeze-agent
repo: andrestorres123/breeze-agent
weekly_downloads: -12345
- description: Build supervisor multi-agent systems with LangGraph.
language: python
monorepo_path: null
name: langgraph-supervisor
repo: langchain-ai/langgraph-supervisor-py
weekly_downloads: -12345
- description: Build agents that learn and adapt from interactions over time.
language: python
monorepo_path: null
name: langmem
repo: langchain-ai/langmem
weekly_downloads: -12345
- description: Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph
agents.
language: python
monorepo_path: null
name: langchain-mcp-adapters
repo: langchain-ai/langchain-mcp-adapters
weekly_downloads: -12345
- description: Open source assistant for iterative web research and report writing.
language: python
monorepo_path: null
name: open-deep-research
repo: langchain-ai/open_deep_research
weekly_downloads: -12345
- description: Build swarm-style multi-agent systems using LangGraph.
language: python
monorepo_path: null
name: langgraph-swarm
repo: langchain-ai/langgraph-swarm-py
weekly_downloads: -12345
- description: A taxonomy generator for unstructured data
language: python
monorepo_path: null
name: delve-taxonomy-generator
repo: andrestorres123/delve
weekly_downloads: -12345
- description: Enable researcher to build scientific workflows easily with simplified
interface.
language: python
monorepo_path: null
name: nodeology
repo: xyin-anl/Nodeology
weekly_downloads: -12345
- description: Build LangGraph agents with large numbers of tools.
language: python
monorepo_path: null
name: langgraph-bigtool
repo: langchain-ai/langgraph-bigtool
weekly_downloads: -12345
- description: An AI-powered data science team of agents to help you perform common
data science tasks 10X faster.
language: python
monorepo_path: null
name: ai-data-science-team
repo: business-science/ai-data-science-team
weekly_downloads: -12345
- description: LangGraph agent that runs a reflection step.
language: python
monorepo_path: null
name: langgraph-reflection
repo: langchain-ai/langgraph-reflection
weekly_downloads: -12345
- description: LangGraph implementation of CodeAct agent that generates and executes
code instead of tool calling.
language: python
monorepo_path: null
name: langgraph-codeact
repo: langchain-ai/langgraph-codeact
weekly_downloads: -12345
- description: Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph
agents.
language: js
monorepo_path: null
name: '@langchain/mcp-adapters'
repo: langchain-ai/langchainjs
weekly_downloads: -12345
- description: Build supervisor multi-agent systems with LangGraph
language: js
monorepo_path: libs/langgraph-supervisor
name: '@langchain/langgraph-supervisor'
repo: langchain-ai/langgraphjs
weekly_downloads: -12345
- description: Build multi-agent swarms with LangGraph
language: js
monorepo_path: libs/langgraph-swarm
name: '@langchain/langgraph-swarm'
repo: langchain-ai/langgraphjs
weekly_downloads: -12345
- description: Build computer use agents with LangGraph
language: js
monorepo_path: libs/langgraph-cua
name: '@langchain/langgraph-cua'
repo: langchain-ai/langgraphjs
weekly_downloads: -12345