pydantic-ai-harness/pydantic_ai_harness/context

Context

Note

Import this capability from its submodule -- there is no top-level pydantic_ai_harness re-export:

from pydantic_ai_harness.context import RepoContext

The API may change between releases. Where practical, breaking changes ship with a deprecation warning.

Discover and load a repo's accumulated coding-assistant context engineering (CE).

Source

The problem

A repo accumulates CE for whatever coding assistant worked in it: instruction files (CLAUDE.md/AGENTS.md) scattered across the tree, and assets under .claude/.agents/.codex/.grok (skills, sub-agents, hooks). An agent that loads only the top-level instruction file misses the ancestor context and has no idea the rest of the setup exists, so it can neither honor it nor translate it.

The solution

RepoContext bundles three strategies, each independently toggleable.

from pathlib import Path

from pydantic_ai import Agent
from pydantic_ai_harness.context import RepoContext

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[RepoContext(workspace_dir=Path('.'), home_dir=Path.home())],
)

1. Walk-up instruction autoload (on by default)

Loads CLAUDE.md/AGENTS.md from workspace_dir and every ancestor up to home_dir (inclusive). Precedence is ancestor-first, workspace-last: broadest context first, most specific last. Files are deduped by resolved real path and by content hash, so a symlinked AGENTS.md -> CLAUDE.md or two ancestors sharing identical content load once.

When home_dir is None (the default), only workspace_dir is scanned -- no walk-up. Pass home_dir=Path.home() to walk up to your home directory.

2. Asset inventory (on by default)

Exposes one tool, inventory_agent_context(), that reports where the repo's CE assets live -- the .claude/.agents/.codex/.grok roots and, within each, the skills/ (SKILL.md), agents/ (.md), and settings.json (hooks) it contains. It returns a structured AgentContextInventory; it locates assets and does not parse them, leaving translation to the orchestrator.

Rename the tool with inventory_tool_name, or scope which roots it scans with asset_roots.

3. Nested-on-traversal (off by default)

When the model lists or reads a directory, surface that directory's CLAUDE.md/AGENTS.md. This couples to the host's list/read tools, so it is opt-in and configurable:

from pathlib import Path

from pydantic_ai import Agent
from pydantic_ai_harness import FileSystem
from pydantic_ai_harness.context import RepoContext

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[
        FileSystem(root_dir='.'),
        RepoContext(
            workspace_dir=Path('.'),
            nested_traversal=True,
            traversal_tool_names=frozenset({'list_directory', 'read_file'}),  # the FileSystem tool names to hook
            traversal_path_arg='path',                                   # the path arg key
            nested_inject='pointer',                                     # or 'contents'
        )
    ],
)

nested_inject='pointer' (default) appends a one-line note pointing at the file; 'contents' inlines the file body. Each directory is surfaced at most once per run.

Cache cost

Injecting file contents into the system prompt costs prompt-cache stability: a changed prefix re-bills the whole cached region. RepoContext keeps the two cache-relevant paths separate:

• Strategy 1 reads its files once at run start and injects them as static system instructions, so the cached prefix stays byte-identical across turns.
• Strategy 3 is volatile (it depends on which directory was just touched), so its note is appended to the tool result in the message tail -- never to the system prompt -- and cannot invalidate the cached prefix.

Configuration

RepoContext(
    workspace_dir,                  # Path -- the deepest dir the agent works in (required)
    home_dir=None,                  # Path | None -- shallowest dir to stop walk-up at, inclusive
    filenames=('CLAUDE.md', 'AGENTS.md'),
    autoload_instructions=True,     # Strategy 1
    expose_inventory_tool=True,     # Strategy 2
    inventory_tool_name='inventory_agent_context',
    nested_traversal=False,         # Strategy 3
    nested_inject='pointer',        # 'pointer' | 'contents'
    traversal_tool_names=frozenset({'list_directory', 'read_file'}),
    traversal_path_arg='path',
    asset_roots=('.claude', '.agents', '.codex', '.grok'),
)

Scope

RepoContext locates and loads CE; it does not parse skill/sub-agent frontmatter or hook bodies, and it does not rewrite or translate assets. Strategy 1 reads its files once per run, so mid-run edits to those files are not reloaded.

Further reading

• Pydantic AI capabilities
• Pydantic AI hooks