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— always-on agent evaluation harness","maintainers":[{"name":"jayscambler","email":"jay@greyhaven.ai"}],"readme":"# autoctx — TypeScript package\n\n`autoctx` is the Node/TypeScript package for autocontext. It ships the operator-facing CLI, TUI, simulations, investigations, analysis, missions, MCP server, runtime/session primitives, production-trace SDK, and experimental agent-handler surface.\n\nUse the Python package when you need the full Python control plane or local MLX/CUDA training implementation. Use this package when you need Node, npm, the TUI, Fetch/agent adapters, or TypeScript library APIs.\n\nThe `autoctx/context-bundles` subpath provides the cross-runtime immutable\nmanifest, canonical digest, and matched-confirmation contract used by the\nPython control plane. It validates the same shared fixtures and can be used by\nNode evaluators before they return promotion evidence. The subpath also\nreproduces campaign alpha allocation, dependence-block decisions, persisted\nstate digests, and exact component-removal attribution verification. See\n[context bundles](../docs/context-bundles.md).\nThe root library also exports ablation-backed component attribution, bounded\nre-testing, causal-credit reconstruction, and prompt-selection helpers; see\n[ablation-backed attribution](../docs/context-attribution.md).\n\n## Install\n\nThe npm package requires Node.js 22.19.0 or newer. The pinned development\nversion is recorded in [`.nvmrc`](.nvmrc); pi-tui and the native SQLite module\nboth rely on this baseline.\n\n```bash\nbun add -g autoctx\n# or\nnpm install -g autoctx\n```\n\nImportant: use `autoctx`, not `autocontext`. `autocontext` on npm is a different package and not this project.\n\n### Optional provider SDKs\n\nThe OpenAI and Anthropic integrations (`autoctx/integrations/openai`, `autoctx/integrations/anthropic`) and their detectors treat the provider SDKs as optional peer dependencies, so installing `autoctx` does not pull them in. Install the one you use alongside `autoctx`:\n\n```bash\n# Anthropic integration and the Anthropic examples below\nnpm install autoctx @anthropic-ai/sdk\n# OpenAI integration\nnpm install autoctx openai\n```\n\n(Bun: `bun add autoctx @anthropic-ai/sdk` or `bun add autoctx openai`.) The integrations degrade gracefully when the SDK is absent, but any example that imports a provider SDK requires it to be installed.\n\nFrom a checkout:\n\n```bash\ncd ts\nnpm install\nnpm run build\n```\n\n## Quick start\n\n```bash\nAUTOCONTEXT_AGENT_PROVIDER=deterministic autoctx solve \"improve customer-support replies\" --iterations 3\n```\n\nUse a real provider by setting `AUTOCONTEXT_AGENT_PROVIDER` and its credential:\n\n```bash\nAUTOCONTEXT_AGENT_PROVIDER=anthropic ANTHROPIC_API_KEY=... autoctx solve \"...\" --iterations 3\nAUTOCONTEXT_AGENT_PROVIDER=openai-compatible AUTOCONTEXT_AGENT_BASE_URL=http://localhost:8000/v1 AUTOCONTEXT_AGENT_API_KEY=... AUTOCONTEXT_PROVIDER_HOSTING=local AUTOCONTEXT_PROVIDER_CAPABILITY=mid_tier autoctx solve \"...\" --iterations 3\nAUTOCONTEXT_AGENT_PROVIDER=pi AUTOCONTEXT_PI_COMMAND=pi autoctx solve \"...\" --iterations 3\n```\n\n`ANTHROPIC_API_KEY` is the preferred Anthropic credential env var; `AUTOCONTEXT_ANTHROPIC_API_KEY` remains supported as a compatibility alias.\n\nSupported providers: `anthropic`, `openai`, `openai-compatible`, `gemini`, `mistral`, `groq`, `openrouter`, `azure-openai`, `ollama`, `vllm`, `hermes`, `claude-cli`, `codex`, `pi`, `pi-rpc`, `deterministic`.\n\nWith `AUTOCONTEXT_ROLE_ROUTING=auto`, unset OpenAI and OpenAI-compatible tier\nslots resolve to GPT-5.6 Sol/Terra/Luna for frontier/mid-tier/fast roles;\nOpenRouter resolves those tiers to Claude Opus/Sonnet/Haiku. Set\n`AUTOCONTEXT_LOCAL_MODEL` for a generic OpenAI-compatible endpoint that serves\none model. Explicit role and tier models still win.\n\nAnthropic and OpenAI-compatible providers expose native, bounded `deep_think`\ntool collection through the optional `LLMProvider.completeWithThinking` method.\nUse `completeWithThinkingFallback(provider, options)` for arbitrary providers:\nit calls the native loop when available and otherwise returns an empty\n`thinkingStream` with `thinkingCapture: \"unsupported\"`. Tool payloads never\nbecome `text`. For GPT 5.6+ models, `reasoningEffort` selects the external\nnumeric prompt budget while native reasoning is requested off; compatible\ngateways that reject `none` use their lowest advertised level. Treat tool\npayloads as sensitive data and apply trace redaction before\npersistence or export. Local CLI/runtime and deterministic providers do not\ncurrently expose a structured tool channel.\n\nProvider routing details live in [../autocontext/docs/agent-integration.md](../autocontext/docs/agent-integration.md).\nUse `AUTOCONTEXT_PROVIDER_HOSTING=local|remote` to override transport-based\nhosting inference and `AUTOCONTEXT_PROVIDER_CAPABILITY=fast|mid_tier|frontier`\nto cap automatic tiers for a local endpoint. Role-specific endpoints use the\nmatching `AUTOCONTEXT_<ROLE>_PROVIDER_*` declarations.\n\nLibrary consumers using `AgentOrchestrator` get schema-constrained analyst and\ncoach output by default. Pass `constrainedOutput: false` to omit those schemas\nand use the existing Markdown parsers instead:\n\n```ts\nimport { AgentOrchestrator } from \"autoctx\";\n\nconst orchestrator = new AgentOrchestrator(provider, {\n  constrainedOutput: false,\n});\n```\n\n## CLI surfaces\n\n| Command                                                | Purpose                                                      |\n| ------------------------------------------------------ | ------------------------------------------------------------ |\n| `autoctx solve \"...\" --iterations 3`                   | Generate and run a scenario from a plain-language goal       |\n| `autoctx run <name> --iterations 3`                    | Run a saved scenario                                         |\n| `autoctx status <run-id> --json`                       | Read one run snapshot                                        |\n| `autoctx watch <run-id> --ndjson`                      | Stream run snapshots                                         |\n| `autoctx show <run-id> --best --json`                  | Inspect the best generation                                  |\n| `autoctx simulate -d \"...\"`                            | Build/replay/compare simulations                             |\n| `autoctx investigate -d \"...\"`                         | Evidence-driven diagnosis                                    |\n| `autoctx analyze --id <id> --type <kind>`              | Inspect runs, simulations, investigations, or missions       |\n| `autoctx mission create --name \"...\" --goal \"...\"`     | Create verifier-driven goals                                 |\n| `autoctx mission run --id <id> --max-iterations 3`     | Execute a mission                                            |\n| `autoctx queue add --task-prompt \"...\" --rubric \"...\"` | Add evaluation/improvement work                              |\n| `autoctx runtime-sessions timeline --run-id <run_id>`  | Inspect provider/tool/child-task timelines                   |\n| `autoctx benchmark --scenario <name> --runs 5`         | Run a scenario repeatedly and summarize outcomes             |\n| `autoctx export <run_id> [--format json\\|pi-package]`  | Export portable knowledge (JSON defaults to stdout)           |\n| `autoctx import-package --file pkg.json`               | Import a portable knowledge package                          |\n| `autoctx scenario create --description \"...\"`          | Generate a scenario from a plain-language description        |\n| `autoctx serve mcp`                                    | Expose MCP tools                                             |\n| `autoctx tui`                                          | Start the terminal UI                                        |\n| `autoctx train --scenario <name> --dataset <jsonl>`    | Validate training input and call an injected training runner |\n| `autoctx agent run <name> --payload '{...}'`           | Invoke experimental `.autoctx/agents` handlers               |\n| `autoctx promotion apply <id> --to <mode> --reason ...` | Promote a control-plane artifact                              |\n| `autoctx candidate rollback <id> --reason ...`         | Roll back a control-plane artifact                            |\n\n`train` is a validation/executor-hook surface in TypeScript; end-to-end MLX/CUDA training lives in the Python package unless your application injects a real `TrainingRunner`.\n\n## Operator TUI\n\nStart a local interactive server and attach the pi-tui client:\n\n```bash\nautoctx tui\n```\n\nAttach the same client to an existing TypeScript `autoctx serve` endpoint:\n\n```bash\nexport AUTOCONTEXT_SERVER_TOKEN=\"$(openssl rand -hex 32)\"\nautoctx tui --connect https://host.example\n```\n\nNon-loopback attach endpoints must use HTTPS/WSS. Credentials are never sent\nover a remote plaintext WebSocket, and URL userinfo or sensitive query values\nare rejected. A non-loopback server bind also requires\n`AUTOCONTEXT_SERVER_TOKEN` to contain at least 32 characters. The TUI reads the\nsame token from its environment and authenticates HTTP with a bearer header and\nbrowser-compatible WebSockets with the\n`autocontext.bearer.<base64url-token>` subprotocol. The shared token is intended\nfor a single trusted operator; use TLS, an authenticated reverse proxy, and\nexternal authorization for broader access.\n\nWhen a TLS-terminating reverse proxy serves a browser UI from a public origin,\nallow that exact browser origin explicitly before starting `autoctx serve`:\n\n```bash\nexport AUTOCONTEXT_SERVER_ALLOWED_ORIGINS=\"https://operator.example,https://operator.example:8443\"\n```\n\nThe value is a comma-separated list of exact `http://` or `https://` origins;\ncredentials, paths, queries, fragments, and wildcards are rejected. A browser\nconnecting over WSS sends the corresponding HTTPS `Origin`, so configure\n`https://operator.example`, not the WebSocket endpoint or a `wss://` URL. This\nallowlist supplements local-origin checks and controls HTTP mutation, CORS, and\nWebSocket origin validation. It does not change the bind address, infer trust\nfrom forwarded headers, replace `AUTOCONTEXT_SERVER_TOKEN`, or authorize a\nrequest by itself.\n\nLocal and remote mode use the same WebSocket transport, durable transcript\nview model, command registry, and HTTP cockpit read models. The renderer never\nopens SQLite or calls `RunManager` directly. The alternate-screen layout keeps\nconnection/run state at the top, the current task plan on wide terminals, a\nscrollable durable transcript, sticky run status, and a multiline editor. It\nsupports 40-column through wide layouts, Unicode-aware wrapping, mouse and\nPageUp/PageDown scrolling, selection, `Ctrl+Shift+F` search, `End` to resume\nfollow-tail, `Shift+Enter` for a newline, history, paste, and registry-driven\nslash completion. TUI logs live under `<runsRoot>/_tui/logs`; shutdown restores\nthe main screen and prints the final transcript document.\n\nUse `/help` or `F1` for the capability-aware command list. The main command\nfamilies are:\n\n- Run control: `/run`, `/pause`, `/resume`, `/hint`, `/gate`, and `/stop`.\n- Inspection: `/status`, `/show`, `/watch` (`/follow`), `/timeline`,\n  `/findings`, `/artifacts`, and `/export`.\n- Cockpit: `/runs` (`/list`), `/queue`, `/workers`, `/sessions`, and\n  `/session <session-id>` for parent/child drill-down.\n- Agent/scenario: `/chat`, `/solve`, `/scenarios`, `/routing`, `/approve`,\n  and `/reject`.\n- Authentication: `/login`, `/logout`, `/provider`, and `/whoami`.\n\n`/quit`, `/exit`, `/detach`, and `Ctrl+C` only detach the TUI; they never stop\nthe run. `/stop` is separately capability-gated, run-scoped, and requires the\nsame run to be confirmed with `/stop confirm`. Playbook approval and rejection\nlikewise require `/approve <scenario> confirm` or `/reject <scenario> confirm`.\nCommands are serialized so overlapping submissions cannot race. API keys are\nentered in a masked overlay, never inserted into editor history, and are\ncleared from the input component before it closes.\nArtifact discovery output uses OSC 8 hyperlinks when the terminal supports\nthem, while retaining the full URL as readable fallback text.\n\nApplications with an in-process component host can pass a\n`RegistryRuntimeActivationController` through the `runtimeActivation` option of\n`runControlPlaneCommand`. In that configuration, `promotion apply` and\n`candidate rollback` execute the durable runtime transaction before committing\nregistry metadata. Supply `--transaction-id <id>` when a retry must reuse the\nsame operation identity; live rollback also requires\n`--baseline <artifactId|none>`. The standalone command has no generic component\nmanifest resolver and therefore retains metadata-only behavior unless the host\nprovides this integration. See\n[transactional runtime activation](../docs/internal/runtime-transactional-activation.md).\n\n## Python-Only commands\n\nThese workflows require infrastructure not shipped in the npm package: `ecosystem`\n(multi-provider cycling), `ab-test` (requires the ecosystem runner), `resume` /\n`wait` (run recovery infrastructure), and `trigger-distillation` (training\npipeline). They are available via `pip install autocontext`; the npm package's\n`train` command is a validation/executor-hook surface only.\n\n## MCP and control plane\n\n```bash\nautoctx serve mcp\n```\n\nThe MCP server exposes 40+ tools across scenarios, runs, knowledge, evaluation, feedback, solve (`solve_scenario`, `solve_status`, `solve_result`), sandbox (`sandbox_create`, `sandbox_run`, `sandbox_status`, ...), export, and discovery (`capabilities`). Python and TypeScript share the same high-level vocabulary; parity details are tracked in [../docs/internal/scenario-parity-matrix.md](../docs/internal/scenario-parity-matrix.md).\n\n### Interactive run transcript extension\n\nThe TypeScript `/ws/interactive` server uses base WebSocket\n`protocol_version: 2` for accelerator-aware environment descriptors. CPU-only\nenvironment resources omit the new optional fields and retain their legacy v1\nshape. Clients explicitly opt into durable\ntranscripts with `/ws/interactive?transcript_protocol_version=1`; that connection\nadvertises `transcript_protocol_version: 1` plus the `run_transcript_v1` and\n`safe_run_stop_v1` capabilities. TypeScript engines that publish live execution\nplans and progress notes also advertise `agent_task_plan_v1` and\n`agent_progress_notes_v1` on the same transcript-enabled hello.\nClients may attach a stable `client_run_id` and `command_id` to `start_run`,\noperator-control, and chat commands. Run-scoped responses then include stable\n`event_id`, monotonic `sequence`, `client_run_id`, and `occurred_at` fields.\n\n#### Structured task creation\n\nTypeScript protocol-v2 servers in `autoctx@0.19.0` advertise\n`structured_task_creation_v1` only with the complete `create_task` handler and\nschema available. The command compiles a versioned improvement objective,\ndeliverable, evaluation contract, and at most eight bounded inline sources\ndirectly into a native agent-task scenario. Each source declares an explicit\nrole (`target`, `input`, `reference`, `constraint`, `example`, or `eval`),\nprovenance, retained byte count, and SHA-256 digest. `holdout` is rejected until\nwinner-only verification exists.\n\nCandidate-visible and evaluator-only data stay separate through generation,\nrevision, evaluation, persistence, progress events, and retained results. A\nvalid setup emits `scenario_generating`, then `scenario_preview`; after\n`confirm_scenario`, persistence completes before `scenario_ready`. Validation\nor persistence failures emit `scenario_error` and never advertise readiness.\nProvider responses that report truncation may use at most two continuation\nsegments; empty, non-growing, oversized, or exhausted continuations fail before\nevaluation or retention.\n\nThe package includes a copyable, integrity-valid command at\n[`protocol-fixtures/structured-task-create-v1.json`](protocol-fixtures/structured-task-create-v1.json).\nThe [machine-readable WebSocket contract](../docs/websocket-protocol-contract.json)\ndefines the exact TypeScript-only extension. Python does not advertise it.\n\n#### Minimum iteration floors\n\nTypeScript protocol-v2 servers advertise `minimum_iterations_v1`. A structured\ntask contract may set `minimumIterations`; `start_run` may supply an authoritative\n`minimum_generations` override, which must be between one and `generations`.\nQuality-threshold, plateau, unchanged-output, and cached-verdict convergence do\nnot stop below the effective floor, while cancellation, provider/evaluator\nfailure, and time/token safety exits remain immediate. Accepted, started,\nstatus, transcript, and SQLite-backed Cockpit history retain the effective\nminimum across reconnect and restart.\n\n#### Structured agent-task outcomes\n\nCompleted saved agent tasks expose `agent_task_outcome_v1`. The\n`run_completed.agent_task_outcome` receipt reports the termination reason,\nconfigured quality threshold, whether it was met, completed/max/best iteration,\nand best score. Each `generation_completed` event carries that evaluation's\nreasoning, dimension scores, judge-failure flag, and evaluator epoch. Transcript\nretention preserves the compact terminal receipt plus those per-generation\nevents; Cockpit run inspection returns the complete versioned outcome from\nSQLite as `agent_task_outcome`.\n\nThe outcome is derived directly from the improvement loop result and task spec.\nIt does not parse the generated skill package or run an additional synthesis\npass. A complete example ships at\n[`protocol-fixtures/agent-task-outcome-v1.json`](protocol-fixtures/agent-task-outcome-v1.json).\n\nReconnect with:\n\n```json\n{\n  \"type\": \"resume_run\",\n  \"client_run_id\": \"control-plane-run-id\",\n  \"after_sequence\": 42,\n  \"command_id\": \"resume-attempt-id\"\n}\n```\n\nThe server replays the exact retained wire frames after that cursor and finishes\nwith a correlated `ack`. Frames and request fingerprints are synchronously\npersisted before side effects or delivery under\n`runs/_interactive/run-transcript.ndjson`, survive server restarts, and only store\nan allowlisted, size-bounded, redacted presentation payload. Retention is bounded\nby age, file bytes, global/per-run frame counts, and command count; command\nidempotency has the same finite horizon as its retained request and response.\nCompaction uses an fsync-backed atomic replacement. The Python server accepts the\nadditive metadata fields for schema compatibility but does not advertise this\nTypeScript-only retention capability.\n\n#### Image attachments\n\nWhen the active provider and model can consume images, transcript-enabled\nTypeScript connections advertise `image_attachments_v1`. The capability is\ndynamic: provider or model changes refresh the hello/environment frames, and a\nclient must fail closed when the capability is absent. Python parity is\ndeferred; its interactive server does not advertise this capability.\n\n`chat_agent` and `inject_hint` accept the same optional additive\n`image_attachments` array without changing `protocol_version: 2`:\n\n```json\n{\n  \"type\": \"chat_agent\",\n  \"role\": \"analyst\",\n  \"message\": \"Inspect this image\",\n  \"image_attachments\": [\n    {\n      \"id\": \"image-1\",\n      \"name\": \"pixel.png\",\n      \"source\": \"picker\",\n      \"media_type\": \"image/png\",\n      \"data_base64\": \"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=\",\n      \"byte_length\": 68,\n      \"content_sha256\": \"431ced6916a2a21a156e38701afe55bbd7f88969fbbfc56d7fe099d47f265460\",\n      \"width\": 1,\n      \"height\": 1\n    }\n  ]\n}\n```\n\n`source` is `picker`, `paste`, or `drop`; supported media types are PNG, JPEG,\nGIF, and WebP. Before any provider call, the server enforces canonical base64,\nunique ids and content hashes, exact decoded byte count and SHA-256, matching\nformat headers and dimensions, and these limits: at most four images, 5 MiB\ndecoded source bytes and 7 MiB encoded bytes per image, dimensions no larger\nthan 8192×8192, a 64 MiB decoded RGBA budget per image, and 20 MiB aggregate\nencoded bytes. PNG, JPEG, GIF, and WebP inputs must pass a bounded full decode;\nanimated images are rejected, and decode work is serialized and queue-bounded.\nAnthropic receives exact base64 image blocks; supported OpenAI\nmodels receive exact data-URL image parts. Unknown OpenAI-compatible gateways\nare unsupported unless the embedding application explicitly opts them in.\n\nTranscript-enabled TypeScript runs expose their current semantic execution plan\nas `task_plan_updated` events. Every event is a complete snapshot rather than a\npatch, so a client can render the latest valid event directly:\n\n```json\n{\n  \"type\": \"event\",\n  \"event\": \"task_plan_updated\",\n  \"run_id\": \"engine-run-id\",\n  \"client_run_id\": \"control-plane-run-id\",\n  \"event_id\": \"7db8149c-8302-45dc-98c8-6d8c11f34d27\",\n  \"sequence\": 4,\n  \"occurred_at\": \"2026-07-21T16:20:00.000Z\",\n  \"payload\": {\n    \"run_id\": \"engine-run-id\",\n    \"plan_id\": \"engine-run-id:task-plan\",\n    \"version\": 2,\n    \"plan_revision\": 1,\n    \"update_kind\": \"progress\",\n    \"active_step_id\": \"evaluate\",\n    \"steps\": [\n      { \"id\": \"draft\", \"label\": \"Draft the result\", \"status\": \"completed\" },\n      { \"id\": \"evaluate\", \"label\": \"Evaluate and refine\", \"status\": \"in_progress\" },\n      { \"id\": \"finalize\", \"label\": \"Finalize the result\", \"status\": \"pending\" }\n    ]\n  }\n}\n```\n\nThe initial snapshot uses version/revision `1/1`. Every update increments\n`version`; ordinary progress keeps `plan_revision` stable, while a `replan`\nincrements it and includes a bounded summary. Completed steps never regress.\nExactly one step may be `in_progress`, and it must match `active_step_id`;\nterminal snapshots use a null active step and mark unfinished work as failed,\ninterrupted, or skipped before `run_completed`, `run_failed`, or `run_stopped`.\nSnapshots are redacted and validated atomically before persistence: an invalid or\noversized plan is omitted rather than truncated into a misleading partial plan.\nThe TypeScript producer limits the complete serialized event to 12 KiB so its\nescaped transcript record remains within the transcript's 32 KiB record ceiling.\nCursor replay returns the exact retained snapshots with their original identity\nand ordering. See the\n[shared WebSocket contract](../docs/websocket-protocol-contract.json) for the\nmachine-readable invariants. Python does not advertise this capability yet.\n\n#### Agent progress notes\n\nTranscript-enabled TypeScript runs also emit concise semantic checkpoints as\n`agent_progress_note` events:\n\n```json\n{\n  \"type\": \"event\",\n  \"event\": \"agent_progress_note\",\n  \"run_id\": \"engine-run-1\",\n  \"client_run_id\": \"control-run-1\",\n  \"event_id\": \"progress-event-1\",\n  \"sequence\": 2,\n  \"occurred_at\": \"2026-07-27T15:00:02.000Z\",\n  \"payload\": {\n    \"run_id\": \"engine-run-1\",\n    \"generation\": 1,\n    \"kind\": \"discovery\",\n    \"text\": \"The retained source confirms the release is reversible.\",\n    \"evidence_targets\": [\n      { \"kind\": \"action\", \"action_id\": \"inspect-release\" },\n      {\n        \"kind\": \"artifact\",\n        \"action_id\": \"inspect-release\",\n        \"artifact_id\": \"release-report\"\n      }\n    ]\n  }\n}\n```\n\nThe five kinds—`intent`, `discovery`, `decision`, `verification`, and\n`blocker`—are presentation checkpoints, not reasoning traces. `generation` is a\nnonnegative integer, and `text` is nonempty and at most 480 characters after\nsafety redaction and re-bounding. Hidden reasoning, prompts, raw model/tool I/O,\ncredentials, URLs, and selectors are never emitted.\n\nA note may cite at most five evidence targets. Action and artifact IDs are safe\nopaque identifiers of at most 200 characters. Every target must resolve to an\nearlier `action_detail` frame in the same retained client-run transcript, and an\nartifact must belong to its referenced action. One invalid or unresolved target\ndrops the entire note rather than retaining partial evidence. Built-in producers\nomit evidence where no stable action IDs are available.\n\nNotes are validated and bounded to a 4 KiB serialized event before the raw event\nemitter sees them. Transcript delivery retains the exact wire frame, identity,\nand ordering for cursor replay across reconnects and server restarts, subject to\nthe transcript's finite retention horizon. Python intentionally does not\nadvertise `agent_progress_notes_v1` until it can provide the same durable\nmetadata and replay guarantees.\n\nTo stop the currently bound run, send a retry-stable command:\n\n```json\n{\n  \"type\": \"stop\",\n  \"client_run_id\": \"control-plane-run-id\",\n  \"command_id\": \"stop-attempt-id\"\n}\n```\n\nThe TypeScript server synchronously persists and returns a correlated `ack`\nbefore the stop can take effect. Stop is cooperative: a paused run wakes\nimmediately, while an in-flight provider or scenario operation finishes before\nthe next safe boundary. Completed generations, artifacts, metrics, and\ntranscript frames remain available. The terminal receipt is a retained\n`run_stopped` event carrying the engine `run_id`, `reason: \"operator\"`, the\noriginating `command_id`, `completed_generations`, and optional `best_score`.\nWhichever terminal outcome occurs first wins, so a natural completion or failure\ncannot later become stopped, and a stop-first run cannot later emit completed or\nfailed.\n\nRetries with the same command ID never repeat the side effect. Before the\nterminal event exists they replay the exact acknowledgement; afterwards they\nreplay the exact terminal receipt. Cursor replay retains both ordered frames.\nThis idempotency guarantee shares the transcript's finite retention horizon.\nPython advertises the base `safe_run_stop_v1` capability and honors cooperative\nstop with live (within-run) idempotency: duplicate or reconnected commands are\ndeduplicated while the run is active. Durable reconnect-after-terminal replay of\nthe acknowledgement and terminal receipt remains TypeScript-only because it\ndepends on the transcript's retention horizon.\n\n## Library usage\n\n```ts\nimport { createProvider, LLMJudge } from \"autoctx\";\n\nconst provider = createProvider({\n  providerType: \"anthropic\",\n  apiKey: process.env.ANTHROPIC_API_KEY ?? \"\",\n});\n\nconst judge = new LLMJudge({\n  provider,\n  model: provider.defaultModel(),\n  rubric: \"Score clarity and correctness.\",\n});\n\nconst result = await judge.evaluate({\n  taskPrompt: \"Explain binary search.\",\n  agentOutput: \"Binary search halves the search space each step.\",\n});\n```\n\nPrefer package subpath exports for specialized surfaces:\n\n```ts\nimport { buildTrace } from \"autoctx/production-traces\";\nimport { instrumentClient } from \"autoctx/integrations/anthropic\";\nimport type { AutoctxAgentContext } from \"autoctx/agent-runtime\";\nimport { connectMcpRuntimeTools } from \"autoctx/runtimes/mcp\";\n```\n\n### Live runtime composition\n\nThe package root exports a host-owned `RuntimeComponentGraph` for reactive live\ncomponents. Manifests use typed capability keys and concrete `instanceId`\nvalues; changing provider identity drains affected consumers before replacing\nthe provider, while unrelated components remain active. Invalid cycles and\nduplicate exclusive providers fail before the live graph changes. See the\n[component graph contract](../docs/internal/runtime-component-graph.md) and\n[effect policy](../docs/internal/runtime-effect-policy.md) before exposing live\ncomposition to generated code.\n\nLive promotion is available through `RuntimeActivationSupervisor` and the\ngraph-backed activation driver. Durable journals make activation and rollback\nidempotent and restart-recoverable; registry/actuator adapters connect the live\noperation to existing artifact promotion state. Configure these APIs in a\ntrusted host with an artifact-to-component manifest resolver. See the\n[transactional activation contract](../docs/internal/runtime-transactional-activation.md)\nand [composition confluence harness](../docs/internal/runtime-composition-confluence.md).\n\n## Production traces\n\nRequires the optional `@anthropic-ai/sdk` peer (`npm install @anthropic-ai/sdk`, or `openai` for the OpenAI integration). See [Optional provider SDKs](#optional-provider-sdks).\n\n```ts\nimport Anthropic from \"@anthropic-ai/sdk\";\nimport { FileSink, instrumentClient } from \"autoctx/integrations/anthropic\";\n\nconst sink = new FileSink(\"./traces/anthropic.jsonl\");\nconst client = instrumentClient(new Anthropic(), {\n  sink,\n  appId: \"billing-bot\",\n  environmentTag: \"prod\",\n});\n```\n\nThe SDK captures provider-native content blocks, cache-aware usage, outcome\ntaxonomy, and dataset/retention helpers. Deeper notes live in\n[../docs/analytics.md](../docs/analytics.md),\n[../docs/opentelemetry-bridge.md](../docs/opentelemetry-bridge.md), and the\nsource under `src/production-traces/`.\n\n## Agent handlers\n\nThe experimental `autoctx/agent-runtime` subpath discovers handlers only from\n`.autoctx/agents` and invokes them with explicit `payload`, `env`, `workspace`,\nand `AgentRuntime` capabilities.\n\n```ts\n// .autoctx/agents/support.ts\nimport type { AutoctxAgentContext } from \"autoctx/agent-runtime\";\n\nexport default async function ({ init, payload }: AutoctxAgentContext<{ message: string }>) {\n  const runtime = await init();\n  const session = await runtime.session(\"support\");\n  return session.prompt(payload.message, { role: \"support-triager\" });\n}\n```\n\n```bash\nautoctx agent run support --payload '{\"message\":\"triage this ticket\"}' --json\nautoctx agent dev --port 3583\nautoctx agent build --target node --out .autoctx/build/node\n```\n\nExamples:\n[../examples/README.md](../examples/README.md#experimental-typescript-agent-handler).\n\n## Fetch/edge adapters\n\nGeneric Fetch/ESM hosts can use static catalogs and explicit host capabilities;\nthe package does not imply Cloudflare/Vercel/Deno deployment wrappers.\n\nReference docs moved out of this README:\n\n- [../docs/fetch-api-reference.md](../docs/fetch-api-reference.md)\n- [../docs/fetch-host-capability-manifest.md](../docs/fetch-host-capability-manifest.md)\n- [../docs/generated-fetch-packaging.md](../docs/generated-fetch-packaging.md)\n- [../docs/fetch-conformance.md](../docs/fetch-conformance.md)\n- [../docs/fetch-troubleshooting.md](../docs/fetch-troubleshooting.md)\n- [../docs/internal/edge-runtime-compatibility.md](../docs/internal/edge-runtime-compatibility.md)\n\nRunnable examples: [`examples/fetch-conformance-host-wrapper.ts`](examples/fetch-conformance-host-wrapper.ts)\n(typed executable wrapper) and\n[`examples/generated-fetch-runtime-factory-packaging.ts`](examples/generated-fetch-runtime-factory-packaging.ts)\n(generic Fetch/ESM packaging).\n\n## Contract probes\n\n```bash\nautoctx probes check --suite contract-probes.json\nautoctx probes check --suite contract-probes.json --json\nautoctx probes extract --trace harness-trace.json --output contract-probes.json\n```\n\nUse probes to turn observed harness behavior into strict executable checks.\n\n## Project defaults\n\n`autoctx` searches upward for `.autoctxrc.json`, `.autoctxrc`, or\n`autoctx.config.json`. Explicit CLI flags win over config files. Env file\nloading for agent handlers is explicit: pass `--env FILE` or set\n`AUTOCTX_ENV_FILE` for generated Node servers.\n\n## Development\n\n```bash\nnpm run build\nnpm run lint\nnpm test\n```\n\nKeep this README as the package entry point. Put long reference material in\n`../docs/`, `examples/`, or source-level API docs.\n","readmeFilename":"README.md"}