README.md

a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task

autocontext is a harness for agent improvement. Give it a goal, it runs the task against evaluation, keeps the useful lessons, discards dead ends, and leaves traces, reports, playbooks, datasets, and optional local-model training artifacts for the next run.

Docs: autocontext.ai/docs · quickstart · CLI reference · changelog

Install

Surface	Command
Python CLI	uv tool install autocontext==0.17.0
Python library/dev	uv pip install autocontext==0.17.0
TypeScript/Node CLI	bun add -g autoctx@0.17.0
Pi extension	pi install npm:pi-autocontext@0.10.0

The PyPI package is autocontext; the CLI is autoctx. The npm package is autoctx (not the unrelated autocontext npm package). Provider variables live in .env.example. The npm CLI and TUI require Node.js 22.19.0 or newer; contributors should use the version pinned in ts/.nvmrc.

30-Second Run

Pi is the lowest-friction provider because it uses your local agent auth:

AUTOCONTEXT_AGENT_PROVIDER=pi \
AUTOCONTEXT_PI_COMMAND=pi \
autoctx solve "improve customer-support replies for billing disputes" --iterations 3

Use AUTOCONTEXT_AGENT_PROVIDER=anthropic, openai-compatible, openrouter, claude-cli, codex, pi-rpc, or another provider when you need that runtime. See agent integration for the full matrix.

Running it on your own GPU instead? Self-hosted models covers the whole loop on vLLM, Ollama, or any OpenAI-compatible endpoint — including what each role actually resolves to, and why constrained output matters more on open weights. Self-hosted endpoints can additionally declare AUTOCONTEXT_PROVIDER_HOSTING=local and a fast, mid_tier, or frontier AUTOCONTEXT_PROVIDER_CAPABILITY; role-specific endpoints use matching <ROLE>_PROVIDER_* declarations.

Prime remote execution also supports opt-in accelerator requests with explicit type/count, immutable-image, region, and telemetry capability validation. It fails before provider creation when the configured pool cannot satisfy the request and never downgrades accelerator work to CPU; see remote execution sessions. Shipped Prime generation and campaign paths persist a durable pre-dispatch claim plus the complete result/ledger projection before returning paid work; restart never treats an unresolved or already committed request as permission to provision another sandbox.

Agent Entry Points

• Pi: install pi-autocontext, then ask Pi to solve, judge, improve, list, or inspect runs through the packaged skill.
• MCP clients: run autoctx serve mcp or bunx autoctx serve mcp and expose the tools to Claude Code, Cursor, or another MCP client.
• Hermes: export the CLI-first skill with uv run autoctx hermes export-skill --with-references --json.

Full setup: autocontext/docs/agent-integration.md.

What A Run Leaves Behind

runs/<run_id>/
├── trace.jsonl
├── generations/<n>/{strategy.json,analysis.md,score.json}
├── report.md
└── artifacts/

knowledge/<scenario>/
├── playbook.md
├── hints.md
├── tools/
└── context_bundles/{bundles,candidates,promotions,active.json}

Everything is filesystem-first: inspect it, diff it, replay it, export it, or feed it into training. Kernel campaigns extend that contract with exact provider-generation receipts, bounded paid-call accounting, content-addressed lineage, and safe stop/status/resume. Coach and architect context changes are stored as immutable candidates and are not served until matched candidate/incumbent trials confirm them. The live serving boundary can additionally require a cancellable independent audit and a durable campaign-wide false-promotion budget; exact causal credit is accepted only from verified single-component manifest additions. See context bundles and outcome-gated promotion. Controlled component trials feed ablation-backed attribution, so prompt selection can demote low-value context without presenting edit-size correlation as causal.

Python kernel evolution can also compose bounded studies across variable-shape matmul, fused elementwise/reduction, and causal-attention families. Each family retains independent primary/confirmation evidence and per-case floors; cross-shape, cross-hardware, and cross-family trials distinguish portable, partially transferring, specialist, and plateau outcomes without an aggregate score hiding a failed workload. See the kernel evolution guide.

Core Surfaces

Surface	Command	Use it for
solve	autoctx solve "..." --iterations 3	Start from a plain-language goal
run	autoctx run <scenario> --iterations 3	Improve a saved scenario
status	autoctx status <run-id> --json	Read one run snapshot
watch	autoctx watch <run-id> --ndjson	Stream run snapshots
show	autoctx show <run-id> --best --json	Inspect a selected generation
simulate	autoctx simulate -d "..."	Model/replay/compare system behavior
investigate	autoctx investigate -d "..."	Evidence-driven diagnosis
scenario	autoctx scenario create --help	Create from a description, template, or harness spec
mission	autoctx mission create --name "..." --goal "..."	Verifier-driven multi-step goals
train	uv run autoctx train --scenario <name> --data <jsonl>	Distill stable behavior into a cheaper runtime (Python)
serve mcp	autoctx serve mcp	Give an agent the autocontext tool surface
tui	autoctx tui [--connect <server>]	Operate or attach to a run from the pi-tui terminal UI

Running bare autoctx shows the concise paved-road workflow. Use autoctx --help --all in the npm CLI or autoctx commands --all in the Python CLI for the full catalog. --iterations is the primary iteration flag; --gens is a compatibility alias. autoctx --version --json reports the package version and runtime (python or typescript).

Python owns the full control-plane package; TypeScript owns several operator-facing surfaces, the TUI, and Node runtime adapters. Start with autocontext/README.md or ts/README.md.

What's New in 0.17.0

• Outcome-gated context bundles: immutable candidates now move through matched screening, adaptive confirmation, held-out evaluation, false-promotion control, causal attribution, and atomic activation while rejected evidence remains available for scoped retesting.
• Capability-scoped execution: generated research code can run in a locked-down Docker workspace, remote scenarios ship as verified content-addressed packages, and trusted-local execution remains an explicit operator choice rather than a fallback.
• Durable campaign operations: restart-safe scheduling, leases, heartbeats, cancellation, bounded reuse, campaign auditing, and a paid-result outbox make long-running local and remote evaluation inspectable, accountable, and recoverable without duplicate provider execution.
• Correctness-first kernel evolution: protected workers, fresh confirmation, finite-sample promotion gates, autonomous model-backed campaigns, and three-family transfer studies expose regressions, specialists, plateaus, and generalizing champions without averaging failures away.
• Capability-validated accelerators: Prime requests bind immutable images, accelerator type/count, region, telemetry, idempotency, and resolved hardware identity; unsupported or drifting configurations fail before paid candidate execution.
• Stronger learning evidence across runtimes: Python and TypeScript share context-bundle, attribution, and negative-result contracts, while Python training adds replayable adaptive confirmation and minimum-effect promotion artifacts.

npm runtime foundations carried into 0.17.0

The aligned autoctx@0.17.0 package also carries the TypeScript-first runtime work introduced in 0.16.0 and hardened in 0.16.1:

• Host-owned live composition: typed runtime capabilities, scoped cleanup and effect policies, reactive component graphs, and durable transactional activation/rollback for trusted hosts.
• A production-oriented operator TUI: the pi-tui client supports local and remote attachment, durable replay, run control and inspection, and bounded, redacted terminal state on Node.js 22.19+.
• Image-aware interactive sessions: compatible TypeScript providers can advertise image_attachments_v1; attachment validation is bounded and fail-closed before provider inference.
• Protocol and terminal hardening: exact capability negotiation, protected priority controls, bounded WebSocket resources, credential redaction, and terminal-control sanitization are enforced across the interactive path.

Python parity for the pi-tui client and image attachments remains deferred. See the TypeScript guide, runtime composition contracts, and the full changelog for details.

Scenario Families

The shipped families cover games, agent tasks, simulations, artifact editing, investigations, workflows, negotiation, schema evolution, tool fragility, operator loops, and coordination. Python and TypeScript share the family vocabulary; see docs/internal/scenario-parity-matrix.md for parity details.

Package Guides

Need	Go here
Python CLI/library, MCP, HTTP, training	autocontext/README.md
Node CLI, TUI, missions, Fetch/agent adapters	ts/README.md
Pi package	pi/README.md
Copy-paste examples	examples/README.md
Concepts and docs index	docs/README.md
Contributor setup	CONTRIBUTING.md
Repo guide for agents	AGENTS.md

Project Signals

Acknowledgments

Thanks to George for generously donating the autocontext name on PyPI.