GitHub

Let an agent read and change GitHub repositories, issues, pull requests, and other resources. GitHub gives the agent the tools in GitHub’s default tool groups, including tools that make changes; toolsets picks other groups. The credential you connect with decides what those tools can reach.

While Pydantic AI Harness is on 0.x releases, the API may change between minor releases; when it does, deprecation warnings and release-note migration guidance tell you (or your agent) exactly how to upgrade. See the version policy.

Install and connect

pip

Terminal

pip install "pydantic-ai-harness[github]" "pydantic-ai-slim[openai]"

uv

Terminal

uv add "pydantic-ai-harness[github]" "pydantic-ai-slim[openai]"

Set GITHUB_TOKEN to a GitHub personal access token, or pass auth= a token. See the provider setup.

from pydantic_ai import Agent
from pydantic_ai_harness.github import GitHub

agent = Agent('openai:gpt-5.6-sol', capabilities=[GitHub()])
result = agent.run_sync('Summarize the resources I can access')
print(result.output)

Per-user credentials

auth decides which GitHub account each run uses:

auth	Account used
Not set, None, or ''	GITHUB_TOKEN. If that is not set either, creating the agent raises an error.
A token	That token, for every run.
A function	Called at the start of each run. The token it returns is used for that run. If it returns None or '', that run has no GitHub tools. A function never uses GITHUB_TOKEN, and must not return 'oauth'.

A fixed token or GITHUB_TOKEN suits a script or an agent on your own machine, where every run is the same account.

In an app where each user connects their own GitHub account, one agent serves all of them, so the token cannot be fixed when the agent is created. Pass a function that reads the current user’s token from the run’s deps:

from dataclasses import dataclass

from pydantic_ai import Agent, RunContext
from pydantic_ai_harness.github import GitHub


@dataclass
class Deps:
    github_token: str | None


def github_token(ctx: RunContext[Deps]) -> str | None:
    return ctx.deps.github_token


agent = Agent('openai:gpt-5.6-sol', deps_type=Deps, capabilities=[GitHub(auth=github_token)])

Each run connects as its own user, so concurrent runs never share an account. read_only, toolsets, and url still apply to every run.

Your app gets each user’s token, stores it, and refreshes it. For example, a “Connect GitHub” button that signs them in through your GitHub App with OAuth and saves the user access token to their account, or a settings page where each user pastes their own personal access token. Before each run, load it (this can be async) and put it in the deps; the function only reads it.

When users differ in more than their credential, such as a user whose organization is on a GitHub Enterprise Cloud data-residency endpoint, build the whole capability for each run with a dynamic capability:

from dataclasses import dataclass

from pydantic_ai import Agent, RunContext
from pydantic_ai.capabilities import DynamicCapability
from pydantic_ai_harness.github import GITHUB_MCP_URL, GitHub


@dataclass
class Deps:
    github_token: str | None
    github_mcp_url: str | None = None


def github(ctx: RunContext[Deps]) -> GitHub[Deps] | None:
    if not ctx.deps.github_token:
        return None
    return GitHub(auth=ctx.deps.github_token, url=ctx.deps.github_mcp_url or GITHUB_MCP_URL)


agent = Agent('openai:gpt-5.6-sol', deps_type=Deps, capabilities=[DynamicCapability(github, id='github')])

With durable execution such as Temporal, read the credential from the run’s deps rather than from a global, since the function may run in another process. The capability’s id defaults to github, so defer_loading=True works without one. To add more than one GitHub to an agent, give each a distinct id and wrap them in PrefixTools, since their tool names are the same; two that share an id but differ raise an error.

Provider settings

toolsets=['repos', 'issues', 'actions'] picks which of GitHub’s tool groups the server offers. Leave it unset to get the server’s default groups. read_only=True asks the server for its read-only mode. Set url to use a GitHub Enterprise Cloud endpoint.

The capability does not limit which repositories the agent can reach. Set that with the token’s or GitHub App’s permissions. To send other headers, or to use custom authentication, pass a configured client.

Tool selection and approval

To filter tools or require approval in your application, wrap the toolset with the existing toolset wrappers. For example, this asks for approval before every tool call:

from pydantic_ai import Agent
from pydantic_ai.messages import DeferredToolRequests
from pydantic_ai_harness.github import GitHub

capability = GitHub()
agent = Agent(
    'openai:gpt-5.6-sol',
    toolsets=[capability.get_toolset().approval_required()],
    output_type=[str, DeferredToolRequests],
)

Handle the approval requests with the deferred tools workflow. To cap the size of tool output, add Tool Output Limits.

Connection customization

Use auth in almost every case. Pass client only when you need control of the connection itself: your own FastMCP client or transport, for example one with a different authentication scheme, a proxy, or MCP handlers. The client then owns the URL and authentication, so passing client together with auth, url, or toolsets raises an error. With a client, read_only=True keeps only the tools the server marks as read-only, instead of asking the server for read-only mode. include_instructions=False stops the server’s instructions from reaching the model.

A client is one connection shared by every run; see Per-user credentials to connect each user separately. To use two connections whose tool names overlap, give them distinct ids and add PrefixTools.

Source