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agent runtime — HTTP API and orchestration engine with bundled dashboard","maintainers":[{"name":"dbanksdesign","email":"dbanks.design@gmail.com"}],"readme":"# @artificer-ai/runtime\n\nA containerized agent runtime. It runs as a single web server that _is_ an agent\nentity: you talk to it like a person (send it messages), it does work using its\nown credentials, and it persists everything as files on disk.\n\nUnlike a single long \"conversation\" with a model, the runtime orchestrates many\nsmall, hierarchical LLM calls.\n\n## The orchestration model\n\nEvery role is one LLM call (an Anthropic tool-use loop). The shared\n`orchestrator` tool lets any call query system state and spawn child calls,\nwhich is what enables arbitrary depth.\n\n```\ninbound message\n      │\n      ▼\n communicator ── reply ──▶ you\n      │  (start / steer / status)\n      ▼\n orchestrator ◀────────────────┐  (re-invoked after every child finishes)\n   │ invoke_call               │\n   ├──▶ planner  ── plan.md ───┤\n   ├──▶ worker   ── 1 file ────┤\n   └──▶ reviewer ── notes ─────┘\n```\n\n- **communicator** — the only role that talks to the outside world; routes intent.\n- **orchestrator** — owns a workstream, deals out work, re-invoked with each result.\n- **planner** — produces a plan artifact (`plan.md`).\n- **reviewer** — reviews a plan or work and records notes.\n- **worker** — implements a single focused unit (usually one file).\n\n### Per-call model selection\n\nEach call picks a model by complexity tier rather than using one model\neverywhere. Defaults: communicator=`low`, orchestrator=`medium`, planner=`high`,\nreviewer=`medium`, worker=`high`. Tiers map to model ids via `ANTHROPIC_MODEL`\n(medium), `ANTHROPIC_MODEL_LOW`, and `ANTHROPIC_MODEL_HIGH`. Override a role's\ntier with `RUNTIME_TIER_<ROLE>` (e.g. `RUNTIME_TIER_WORKER=medium`), and the\norchestrator can dynamically set `complexity` per spawned call via `invoke_call`.\nThe model used is recorded on each run, so `GET /usage` breaks cost down by model.\n\n## Storage layout (under `ARTIFICER_DATA_DIR`)\n\n```\nmessages/                 conversation with the outside world (one file per message)\nworkstreams/<id>/\n  workstream.json         status + running summary\n  plan.md                 planner artifact\n  tasks/<taskId>.json     unit-of-work records\n  reviews/<reviewId>.json reviewer notes\nruns/<runId>.json         full record of every LLM call (role, task, tool calls, output)\nmemories/                 long-term notes the agent can query\nworkspace/<id>/           per-workstream working directory (repos, edited files)\n```\n\n## HTTP API\n\n- `POST /messages` `{ \"content\": \"...\" }` — send the agent a message (async).\n- `GET /messages` — full conversation history.\n- `GET /state` — high-level snapshot: workstreams, queue, what's running now.\n- `GET /workstreams` / `GET /workstreams/:id` — workstream detail (tasks, reviews, plan, runs).\n- `GET /events` — Server-Sent Events stream of state changes (for a future UI).\n- `GET /health`.\n\n## Run locally\n\n```bash\ncp .env.example .env   # set ANTHROPIC_API_KEY\npnpm install\npnpm dev               # http://localhost:4100\n```\n\nSend it a message:\n\n```bash\ncurl -X POST localhost:4100/messages \\\n  -H 'content-type: application/json' \\\n  -d '{\"content\":\"What are you currently working on?\"}'\n\ncurl localhost:4100/state\n```\n\n## Run in a container\n\n```bash\npnpm docker:build\ndocker compose up\n```\n\nWork history persists in the `runtime-data` volume (mounted at `/data`).\n\n## Configuration\n\nSee `.env.example`. Key variables: `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`,\n`ARTIFICER_DATA_DIR`, `PORT`, `RUNTIME_CONCURRENCY`, `RUNTIME_MAX_ITERATIONS`,\n`RUNTIME_MAX_RUNS_PER_WORKSTREAM`, `AGENT_NAME`.\n\n## Benchmarking\n\n`bench/` runs SWE-bench/DeepSWE-style task suites against a preset (model tiers,\nconcurrency, executor) and grades the results. See `bench/README.md` for the\nfull task/preset/eval workflow; `pnpm bench:full:swebench` runs a full\nrun-eval-report cycle end to end.\n","readmeFilename":""}