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orchestration agent — decomposes tasks and coordinates specialist agents via shared vault and configurable LLM providers.","maintainers":[{"name":"backendkit.dev","email":"backendkit.dev@gmail.com"}],"readme":"# @backendkit-labs/orchestrator-agent\r\n\r\nConfig-driven multi-agent orchestration for enterprise workflows. Decomposes natural language tasks into sub-tasks, coordinates specialist agents via a shared knowledge vault, and enforces human approval gates — with full audit trail.\r\n\r\n## What it does\r\n\r\n- **Plans**: decomposes a task into a dependency-ordered plan (static flow or LLM-generated)\r\n- **Executes**: runs steps in parallel when dependencies allow, each delegated to a specialist agent\r\n- **Gates**: pauses for human approval at critical steps, either by config or by active policy rules\r\n- **Persists**: every run is saved to disk (or Redis) — survives restarts, resumes after gate approval\r\n- **Learns**: enterprise reflection monitors gate outcomes and promotes recurring failure patterns into deterministic policy rules\r\n- **Remembers**: completed runs are auto-distilled into the knowledge vault; specialist agents receive episodic context from past successful runs on similar tasks\r\n\r\n## Installation\r\n\r\n```bash\r\nnpm install -g @backendkit-labs/orchestrator-agent\r\n# or as a local dep in a monorepo\r\nnpm install @backendkit-labs/orchestrator-agent\r\n```\r\n\r\n## Quick start (5 minutes)\r\n\r\n**1. Create `orchestrator.yaml`**\r\n\r\n```yaml\r\nversion: 1\r\n\r\norchestrator:\r\n  name: \"My Orchestrator\"\r\n  provider: deepseek\r\n\r\nproviders:\r\n  deepseek:\r\n    api_key: ${DEEPSEEK_API_KEY}\r\n    base_url: https://api.deepseek.com/v1\r\n    model: deepseek-chat\r\n\r\nagents:\r\n  - id: analyst\r\n    name: Data Analyst\r\n    description: Analyzes data and produces reports\r\n    capabilities: [analysis, reporting, metrics]\r\n    provider: deepseek\r\n\r\n  - id: writer\r\n    name: Technical Writer\r\n    description: Writes documents and summaries\r\n    capabilities: [writing, documentation, summaries]\r\n    provider: deepseek\r\n```\r\n\r\n**2. Create `.env`**\r\n\r\n```\r\nDEEPSEEK_API_KEY=sk-...\r\n```\r\n\r\n**3. Wire into Claude Desktop** (`claude_desktop_config.json`):\r\n\r\n```json\r\n{\r\n  \"mcpServers\": {\r\n    \"orchestrator\": {\r\n      \"command\": \"npx\",\r\n      \"args\": [\"-y\", \"@backendkit-labs/orchestrator-agent\"],\r\n      \"env\": {\r\n        \"DEEPSEEK_API_KEY\": \"sk-...\"\r\n      }\r\n    }\r\n  }\r\n}\r\n```\r\n\r\n**4. Run a task** (in Claude Desktop):\r\n\r\n```\r\norchestrator_run\r\n  config_path: /path/to/orchestrator.yaml\r\n  task: \"Analyze Q1 sales data and write an executive summary\"\r\n```\r\n\r\n## Feature overview\r\n\r\n| Feature | Description |\r\n|---------|-------------|\r\n| **Dynamic planning** | LLM decomposes any task into a dependency-ordered plan |\r\n| **Static flows** | Pre-defined YAML workflows triggered by regex or `flow_id` |\r\n| **Parallel execution** | Independent steps run concurrently (`Promise.all`) |\r\n| **Human gates** | Steps pause for approval — configurable per-agent or per-step |\r\n| **Policy rules (Cable 2)** | Active rules automatically gate matching steps — no LLM involved |\r\n| **LanceDB RAG** | Incremental vault indexing with ANN search, fast at scale |\r\n| **Redis + BullMQ** | Distributed execution across worker processes for production |\r\n| **HTTP trigger** | REST endpoint for n8n / external system integration |\r\n| **Enterprise reflection** | MAPE-K: monitors outcomes → promotes patterns → enforces rules |\r\n| **Run consolidation** | Completed runs auto-distilled by LLM into vault — episodic→semantic loop |\r\n| **Cross-run episodic memory** | Agents receive past successful outputs for similar tasks as context |\r\n| **Forgetting curve** | Episodic memory prunes by relevance (recency × recall frequency), not just age |\r\n\r\n## MCP Tools\r\n\r\n| Tool | Description |\r\n|------|-------------|\r\n| `orchestrator_run` | Start a task — returns `runId` immediately |\r\n| `orchestrator_status` | Poll status by `runId` |\r\n| `orchestrator_approve` | Approve or reject a waiting gate |\r\n| `orchestrator_list_runs` | List all persisted runs |\r\n| `orchestrator_list_agents` | Show configured agents |\r\n| `orchestrator_reflect` | View pending promotions, active rules, demotion candidates |\r\n| `orchestrator_reflect_promote` | Approve or reject a pattern promotion to policy rule |\r\n| `orchestrator_reflect_demote` | Demote a policy rule with audit trail |\r\n\r\n## Documentation\r\n\r\n- [Configuration reference](docs/config-reference.md) — full `orchestrator.yaml` schema\r\n- [MCP tools](docs/mcp-tools.md) — parameters, examples, and responses for all 8 tools\r\n- [Static flows](docs/static-flows.md) — YAML flow format, payload interpolation, auto-detection\r\n- [Gates and policies](docs/gates-and-policies.md) — human approval, Cable 2 automatic gates\r\n- [HTTP trigger](docs/http-trigger.md) — REST API for n8n and external systems\r\n- [Redis + workers](docs/redis-workers.md) — distributed execution with BullMQ\r\n- [RAG vault](docs/rag-vault.md) — LanceDB indexing, run consolidation, cross-run episodic injection\r\n- [Enterprise reflection](docs/reflection.md) — MAPE-K loop, promotions, demotions\r\n- [Memory system](docs/memory-system.md) — full architecture: episodic, semantic, procedural, forgetting curve\r\n\r\n## Data directory\r\n\r\nBy default, run state and RAG index live in `.orchestrator/` adjacent to your `orchestrator.yaml`:\r\n\r\n```\r\n.orchestrator/\r\n  runs/           ← JSON run files (one per run)\r\n  rag-lance/      ← LanceDB vector index\r\n```\r\n\r\nOverride with `ORCHESTRATOR_DATA_DIR` env var or `data_dir` in the YAML.\r\n\r\n## License\r\n\r\nMIT\r\n","readmeFilename":"README.md"}