autocontext 0.11.0

autocontext control plane for iterative strategy evolution.

autocontext Python package

This package is the Python control plane for autocontext: scenario runs, solve, simulations, investigations, MCP/HTTP surfaces, persistent knowledge, training-data export, and local training hooks.

Use it when you want the full harness in Python, a CLI installed with uv/pip, or the MCP/HTTP server that coding agents can call.

Install

pip install autocontext
# or, for an isolated CLI tool:
uv tool install autocontext

Optional extras:

pip install 'autocontext[browser]'          # Chrome/CDP capture
pip install 'autocontext[primeintellect]'   # PrimeIntellect sandbox backend
pip install 'autocontext[mcp]'              # MCP server dependencies

The CLI entrypoint is autoctx. Provider env vars are listed in the repo-level .env.example.

Run from a checkout

cd autocontext
uv venv
source .venv/bin/activate
uv sync --group dev

AUTOCONTEXT_AGENT_PROVIDER=deterministic \
uv run autoctx solve "improve customer-support replies for billing disputes" --iterations 3

Use a real provider by changing AUTOCONTEXT_AGENT_PROVIDER and setting its credential:

AUTOCONTEXT_AGENT_PROVIDER=anthropic \
ANTHROPIC_API_KEY=... \
uv run autoctx solve "improve customer-support replies for billing disputes" --iterations 3

Pi and local CLI providers avoid API-key plumbing when those tools are already authenticated:

AUTOCONTEXT_AGENT_PROVIDER=pi AUTOCONTEXT_PI_COMMAND=pi uv run autoctx solve "..." --iterations 3
AUTOCONTEXT_AGENT_PROVIDER=claude-cli AUTOCONTEXT_CLAUDE_MODEL=sonnet uv run autoctx solve "..." --iterations 3
AUTOCONTEXT_AGENT_PROVIDER=codex AUTOCONTEXT_CODEX_MODEL=o4-mini uv run autoctx solve "..." --iterations 3

Common commands

Command	Purpose
uv run autoctx solve "..." --iterations 3	Generate and run a scenario from a plain-language goal
uv run autoctx run <scenario> --iterations 3	Improve an existing scenario
uv run autoctx simulate --description "..."	Create/replay/compare modeled-world simulations
uv run autoctx investigate --description "..."	Run synthetic or iterative investigations
uv run autoctx list / status <run_id> / show <run_id>	Inspect runs
uv run autoctx replay <run_id> --generation 1	Replay a generation before accepting knowledge
uv run autoctx queue add --task-prompt "..." --rubric "..."	Queue evaluation/improvement work
uv run autoctx serve --host 127.0.0.1 --port 8000	Start the local HTTP API
uv run autoctx worker --poll-interval 5 --concurrency 2	Process queued tasks beside the API server
uv run autoctx mcp-serve	Expose the MCP tool surface
uv run autoctx export-training-data --scenario <name> --all-runs --output data.jsonl	Build a training corpus
uv run autoctx train --scenario <name> --data data.jsonl --time-budget 300	Run the local training hook
uv run autoctx hermes inspect --json	Inspect Hermes Curator state

Saved custom scenarios under knowledge/_custom_scenarios/ can be rerun and benchmarked by name after their spec.json is persisted.

HTTP, MCP, and agents

uv sync --group dev --extra mcp
uv run autoctx mcp-serve

Python runtime-backed run and solve calls append provider prompts/responses to run-scoped runtime-session logs. The same logs are readable through the cockpit HTTP API and MCP tools.

Detailed setup moved out of this README:

• External agents and provider routing: docs/agent-integration.md
• Persistent worker trust boundaries: docs/persistent-host.md
• Sandbox/executor notes: docs/sandbox.md
• Extension hooks: docs/extensions.md

Contract probes

Contract probes turn observed harness traces into executable checks:

uv run autoctx probes check --suite contract-probes.json
uv run autoctx probes check --suite contract-probes.json --json
uv run autoctx probes extract --trace harness-trace.json --output contract-probes.json

Probe suites are strict JSON: unknown keys fail validation and required observation fields must be present. Pipe stdin with --suite - when another tool generates the suite.

Production traces

Wrap an existing Anthropic/OpenAI client once, then persist emitted traces through a sink:

from anthropic import Anthropic
from autocontext.integrations.anthropic import FileSink, instrument_client

sink = FileSink("./traces/anthropic.jsonl")
client = instrument_client(
    Anthropic(),
    sink=sink,
    app_id="billing-bot",
    environment_tag="prod",
)

For lower-level emit APIs, use autocontext.production_traces.build_trace and write_jsonl. Architecture notes are in ../docs/analytics.md and ../docs/opentelemetry-bridge.md.

Training

uv run autoctx export-training-data \
  --scenario support_triage --all-runs \
  --output training/support_triage.jsonl
uv run autoctx train \
  --scenario support_triage \
  --data training/support_triage.jsonl \
  --time-budget 300

For MLX/CUDA setup and case studies, use:

• docs/mlx-training.md
• docs/case-study-recursive-loop.md
• case study: on-policy distillation

Repository layout

autocontext/
├── src/autocontext/       # Python package
├── tests/                 # pytest suite
├── docs/                  # package-specific docs
├── demo_data/             # small bundled examples
├── migrations/            # SQLite migrations
└── pyproject.toml

Development

uv run ruff check .
uv run mypy src
uv run pytest

Keep this README concise. Add deep reference prose to docs/ or the repo-level docs index instead.