autocontext

autocontext is a closed-loop system for improving agent behavior over repeated runs.

It executes tasks, evaluates outcomes, updates persistent knowledge, and optionally distills successful behavior into cheaper local runtimes. The goal is to move from frontier-model exploration toward validated, reusable, lower-cost execution.

Why It Exists

Most agent systems start every run cold. They do not reliably carry forward what worked, what failed, and what should change next.

autocontext adds that missing feedback loop:

• run the task
• analyze what happened
• persist validated lessons
• use those lessons in the next run
• optionally train and route to local models when the task is stable enough

How It Works

Each generation runs through a structured multi-agent loop:

• competitor proposes a strategy or artifact for the task
• analyst explains what happened and why
• coach turns that analysis into playbook updates and future hints
• architect proposes tools, harness improvements, or structural changes
• curator gates what knowledge is allowed to persist

Strategies are then evaluated through scenario execution, staged validation, and gating. Weak changes are rolled back. Successful changes accumulate into reusable knowledge.

Core Capabilities

• Persistent playbooks, hints, tools, reports, and progress snapshots across runs
• Staged validation, harness synthesis, and harness-aware execution
• Frontier-to-local distillation with MLX on Apple Silicon
• Runtime routing across Anthropic, OpenAI-compatible backends, Ollama, vLLM, MLX, and Pi-based runtimes
• OpenClaw-facing APIs and agent integration surfaces
• CLI, API server, dashboard, and TypeScript/TUI surfaces for operators and external agents

Quick Start

The Python application lives in autocontext/, and most uv, pytest, ruff, and mypy commands should be run from there.

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

AUTOCONTEXT_AGENT_PROVIDER=deterministic uv run autoctx run \
  --scenario grid_ctf \
  --gens 3 \
  --run-id quickstart

That creates a local run, writes artifacts under runs/ and knowledge/, and works without external API keys.

Run with Anthropic:

cd autocontext
AUTOCONTEXT_AGENT_PROVIDER=anthropic \
AUTOCONTEXT_ANTHROPIC_API_KEY=your-key \
uv run autoctx run --scenario grid_ctf --gens 3

Start the API server and dashboard:

cd autocontext
uv run autoctx serve --host 127.0.0.1 --port 8000

Then open http://127.0.0.1:8000.

Common Workflows

• Run the generation loop: uv run autoctx run --scenario grid_ctf --gens 3
• Inspect runs: uv run autoctx list, uv run autoctx status <run_id>
• Export training data: uv run autoctx export-training-data --scenario grid_ctf --all-runs --output training/grid_ctf.jsonl
• Train a local model: uv run autoctx train --scenario grid_ctf --data training/grid_ctf.jsonl --time-budget 300
• Start the API server: uv run autoctx serve --host 127.0.0.1 --port 8000
• Start the MCP server: uv run autoctx mcp-serve

MLX training is host-only on Apple Silicon macOS. If you want a sandboxed OpenClaw agent to trigger training, use the file-based host watcher flow documented in autocontext/docs/mlx-training.md.

Repository Layout

• autocontext/: Python package, CLI, API server, dashboard, training loop
• ts/: TypeScript package and CLI/MCP-compatible tooling
• tui/: interactive terminal UI
• infra/: Docker, Fly.io, and bootstrap scripts
• docs/: higher-level repo notes

Where To Look Next

• Package and CLI guide: autocontext/README.md
• Contributor setup: CONTRIBUTING.md
• MLX host training and OpenClaw bridge: autocontext/docs/mlx-training.md
• Sandbox and executor notes: autocontext/docs/sandbox.md
• License: LICENSE

Note

This repo was previously named MTS. Some historical references may still use the older name or issue prefixes.