Github 工具包

Github 工具包包含使 LLM 代理能够与 github 存储库进行交互的工具。该工具是 PyGitHub 库的包装器。

有关 GithubToolkit 所有功能和配置的详细文档，请参阅 API 参考。

设置

总的来说，我们将：

安装 pygithub 库
创建一个 Github 应用
设置您的环境变量
使用 toolkit.get_tools() 将工具传递给您的代理

要启用对各个工具的自动跟踪，请设置您的 LangSmith API 密钥：

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

1. 安装依赖

此集成在 langchain-community 中实现。我们还需要 pygithub 依赖：

%pip install --upgrade --quiet  pygithub langchain-community

2. 创建 Github App

请按照此处的说明创建并注册一个 Github App。请确保您的 App 具有以下仓库权限：

Commit statuses (read only)
Contents (read and write)
Issues (read and write)
Metadata (read only)
Pull requests (read and write)

App 注册完成后，您必须授权您的 App 访问您希望它操作的每一个仓库。使用此处的 github.com 应用设置进行操作。

3. 设置环境变量

在初始化您的 agent 之前，需要设置以下环境变量：

GITHUB_APP_ID - 您 App 的通用设置中找到的一个六位数字。
GITHUB_APP_PRIVATE_KEY - 您 App 私有密钥 .pem 文件的位置，或者将该文件的全部内容作为字符串。
GITHUB_REPOSITORY - 您希望您的机器人操作的 Github 仓库名称。必须遵循 {username}/{repo-name} 的格式。请确保您已首先将 App 添加到该仓库！
Optional: GITHUB_BRANCH - 机器人将进行提交的分支。默认为 repo.default_branch。
Optional: GITHUB_BASE_BRANCH - 您仓库的基础分支，PR 将基于此分支创建。默认为 repo.default_branch。

import getpass
import os

for env_var in [
    "GITHUB_APP_ID",
    "GITHUB_APP_PRIVATE_KEY",
    "GITHUB_REPOSITORY",
]:
    if not os.getenv(env_var):
        os.environ[env_var] = getpass.getpass()

实例化

现在我们可以实例化我们的工具包了：

from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit
from langchain_community.utilities.github import GitHubAPIWrapper

github = GitHubAPIWrapper()
toolkit = GitHubToolkit.from_github_api_wrapper(github)

API Reference:GitHubToolkit | GitHubAPIWrapper

工具

查看可用工具：

tools = toolkit.get_tools()

for tool in tools:
    print(tool.name)

Get Issues
Get Issue
Comment on Issue
List open pull requests (PRs)
Get Pull Request
Overview of files included in PR
Create Pull Request
List Pull Requests' Files
Create File
Read File
Update File
Delete File
Overview of existing files in Main branch
Overview of files in current working branch
List branches in this repository
Set active branch
Create a new branch
Get files from a directory
Search issues and pull requests
Search code
Create review request

这些工具的用途如下：

下面将详细解释每个步骤。

Get Issues - 从仓库中获取 issues。
Get Issue - 获取特定 issue 的详细信息。
Comment on Issue - 在特定 issue 上发布评论。
Create Pull Request - 从机器人的工作分支创建到基础分支的 pull request。
Create File - 在仓库中创建新文件。
Read File - 从仓库中读取文件。
Update File - 更新仓库中的文件。
Delete File - 从仓库中删除文件。

包含 release 工具

默认情况下，工具包不包含 release 相关工具。您可以通过在初始化工具包时设置 include_release_tools=True 来包含它们：

toolkit = GitHubToolkit.from_github_api_wrapper(github, include_release_tools=True)

将 include_release_tools 设置为 True 将包含以下工具：

Get Latest Release - 从仓库获取最新的发布版本。
Get Releases - 从仓库获取最新的 5 个发布版本。
Get Release - 通过标签名称从仓库获取特定的发布版本，例如 v1.0.0。

在 Agent 中使用

我们需要一个 LLM 或聊天模型：

Select chat model:

pip install -qU "langchain[google-genai]"

import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
  os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-2.0-flash", model_provider="google_genai")

使用工具子集初始化代理：

from langgraph.prebuilt import create_react_agent

tools = [tool for tool in toolkit.get_tools() if tool.name == "Get Issue"]
assert len(tools) == 1
tools[0].name = "get_issue"

agent_executor = create_react_agent(llm, tools)

API Reference:create_react_agent

并向其发出查询：

example_query = "What is the title of issue 24888?"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

================================[1m Human Message [0m=================================

What is the title of issue 24888?
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)
 Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi
  Args:
    issue_number: 24888
=================================[1m Tool Message [0m=================================
Name: get_issue

{"number": 24888, "title": "Standardize KV-Store Docs", "body": "To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\r\n\r\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\r\n\r\nRelated to broader issues #21983 and #22005.\r\n\r\n## Docstrings\r\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\r\n\r\nTo build a preview of the API docs for the package you're working on run (from root of repo):\r\n\r\n\`\`\`shell\r\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\r\n\`\`\`\r\n\r\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\r\n\r\n## Doc pages\r\nEach KV-store [docs page](https://python.langchain.com/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\r\n\r\nHere is an example: https://python.langchain.com/docs/integrations/stores/in_memory/\r\n\r\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\r\n\r\n\`\`\`shell\r\npoetry run pip install -e libs/cli\r\npoetry run langchain-cli integration create-doc --name \"foo-bar\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\r\n\`\`\`\r\n\r\nwhere `--name` is the integration package name without the \"langchain-\" prefix and `--name-class` is the class name without the \"ByteStore\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\r\n\r\nTo build a preview of the docs you can run (from root):\r\n\r\n\`\`\`shell\r\nmake docs_clean\r\nmake docs_build\r\ncd docs/build/output-new\r\nyarn\r\nyarn start\r\n\`\`\`\r\n\r\n## Appendix\r\nExpected sections for the KV-store class docstring.\r\n\r\n\`\`\`python\r\n    \"\"\"__ModuleName__ completion KV-store integration.\r\n\r\n    # TODO: Replace with relevant packages, env vars.\r\n    Setup:\r\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\r\n\r\n        .. code-block:: bash\r\n\r\n            pip install -U __package_name__\r\n            export __MODULE_NAME___API_KEY=\"your-api-key\"\r\n\r\n    # TODO: Populate with relevant params.\r\n    Key init args \u2014 client params:\r\n        api_key: Optional[str]\r\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\r\n\r\n    See full list of supported init args and their descriptions in the params section.\r\n\r\n    # TODO: Replace with relevant init params.\r\n    Instantiate:\r\n        .. code-block:: python\r\n\r\n            from __module_name__ import __ModuleName__ByteStore\r\n\r\n            kv_store = __ModuleName__ByteStore(\r\n                # api_key=\"...\",\r\n                # other params...\r\n            )\r\n\r\n    Set keys:\r\n        .. code-block:: python\r\n\r\n            kv_pairs = [\r\n                [\"key1\", \"value1\"],\r\n                [\"key2\", \"value2\"],\r\n            ]\r\n\r\n            kv_store.mset(kv_pairs)\r\n\r\n        .. code-block:: python\r\n\r\n    Get keys:\r\n        .. code-block:: python\r\n\r\n            kv_store.mget([\"key1\", \"key2\"])\r\n\r\n        .. code-block:: python\r\n\r\n            # TODO: Example output.\r\n\r\n    Delete keys:\r\n        ..code-block:: python\r\n\r\n            kv_store.mdelete([\"key1\", \"key2\"])\r\n\r\n        ..code-block:: python\r\n    \"\"\"  # noqa: E501\r\n\`\`\`", "comments": "[]", "opened_by": "jacoblee93"}
==================================[1m Ai Message [0m==================================

The title of issue 24888 is "Standardize KV-Store Docs".

API 参考

关于 GithubToolkit 所有功能和配置的详细文档，请前往 API 参考。

Tool conceptual guide
Tool how-to guides

设置​

安装​

1. 安装依赖​

2. 创建 Github App​

3. 设置环境变量​

实例化​

工具​

包含 release 工具​

在 Agent 中使用​

API 参考​

Related​

设置

安装