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orchestration plugin for OpenCode with memini-ai memory","maintainers":[{"name":"veedubin","email":"jakob.charles@gmail.com"}],"readme":"# Boomerang: Intelligent Multi-Agent Orchestration for OpenCode\n\n[![npm version](https://img.shields.io/npm/v/@veedubin/boomerang-v3.svg)](https://www.npmjs.com/package/@veedubin/boomerang-v3)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n[![OpenCode Plugin](https://img.shields.io/badge/OpenCode-Plugin-ff6b35?style=flat-square)](https://opencode.ai)\n[![TypeScript](https://img.shields.io/badge/TypeScript-5.0-blue?style=flat-square)](https://www.typescriptlang.org/)\n\nBoomerang is an **intelligent multi-agent orchestration plugin** for OpenCode. It acts as a routing brain that:\n- Picks the right specialist agent for every task\n- Arms agents with trust-weighted memory and context\n- Enforces an 8-step quality protocol for reliable execution\n- Integrates with memini-ai for persistent, semantic memory\n\n## What It Does\n\n- **Intelligent Routing**: Analyzes tasks and dispatches to 13 specialist agents (coder, architect, tester, writer, etc.)\n- **Trust-Weighted Memory**: Uses memini-ai to store and retrieve memories with trust scores (0.0-1.0)\n- **8-Step Protocol**: Enforces mandatory steps (memory query, planning, quality gates, doc updates, etc.)\n- **Parallel Execution**: Launches multiple sub-agents simultaneously for independent tasks\n- **Context Packages**: Builds rich context for sub-agents (files, snippets, constraints, expectations)\n- **Security**: Explicit tool allow-lists per agent (~57-73% token reduction vs wildcards)\n- **Skills**: Markdown-based skills loaded at runtime for specialized workflows\n- **Pre-Question Rule**: Dispatches architect to research before asking user clarifying questions\n\n## Architecture\n\nBoomerang-v3 implements the **orchestrator** layer in this ecosystem:\n\n```mermaid\nflowchart LR\n    U[User] -->|prompt| OC[OpenCode TUI]\n    OC -->|task| ORCH[\"Neuralgentics Orchestrator<br/>12 personas + routing matrix\"]\n    ORCH -->|query / save| MEM[(\"memini-ai<br/>FIRST-CLASS MCP<br/>registered directly in opencode.json\")]\n    MEM --> PG[(\"PostgreSQL + pgvector<br/>trust-weighted memory\")]\n    ORCH -->|dispatch| AG[\"Specialist sub-agents<br/>coder · architect · tester · writer\"]\n    AG -->|\"long-tail tool calls\"| BRK[\"Neuralgentics Broker<br/>catalog · access control · audit\"]\n    BRK --> MCP[\"Brokered MCP servers<br/>searxng · github · videre · ssh<br/>behind the broker · on demand\"]\n    AG -->|outbound HTTP| GW[\"Neuralgentics Gateway<br/>egress policy + audit\"]\n    GW --> NET[\"Internet / LLM APIs\"]\n    MEM --> WEB[\"Neuralgentics Web<br/>dashboards\"]\n    GW --> WEB\n    BRK --> WEB\n```\nmemini-ai is a **first-class** MCP server — registered directly in `opencode.json` and always loaded. Every other MCP server sits **behind the broker**: catalog-advertised, access-controlled, and brokered on demand, which keeps long-tail tool schemas out of every prompt.\n\n\n[**View full architecture diagram**](https://veedubin.github.io/Boomerang-v3/architecture/)\n\n## Quickstart\n\n```bash\nnpx @veedubin/boomerang-v3 --setup\n```\n\nThis bootstraps:\n- 13 specialist agents (coder, architect, tester, writer, etc.)\n- Skills for common workflows\n- AGENTS.md with routing matrix\n- opencode.json patching for memini-ai integration\n\n**Provider Note**: Uses Ollama Cloud by default. See [docs/providers.md](https://github.com/Veedubin/Boomerang-v3/blob/main/docs/providers.md) for alternatives (local Ollama, Docker Model Runner, OpenAI, Anthropic, Google, OpenRouter).\n\n## Features\n\n### Orchestration\n- **Routing Matrix**: Code-enforced rules for agent selection (e.g., code → `boomerang-coder`, research → `boomerang-architect`)\n- **13 Specialist Agents**: coder, architect, tester, writer, git, explorer, scraper, release, linter, mcp-specialist, researcher, agent-builder, handoff\n- **Parallel Dispatch**: Uses Kahn's algorithm for dependency-free parallel execution\n- **Context Packages**: Rich context for sub-agents (files, snippets, constraints, expectations)\n\n### Protocol\n- **8-Step Mandatory State Machine**: Memory query → Sequential think → Plan → Delegate → Git check → Quality gates → Doc update → Memory save\n- **Strictness Levels**: lenient (log suggestions), standard (log warnings), strict (block execution)\n- **Waiver Phrases**: `skip planning`, `just do it`, `no plan needed`, `skip tests`, `git is fine`, `--force`, `no docs needed`\n- **Pre-Question Rule**: Dispatches architect to research before asking user clarifying questions\n\n### Memory\n- **memini-ai Integration**: MCP stdio interface to Python semantic memory server\n- **Trust Engine**: Memories start at trust=0.5, adjusted by feedback (`agent_used` +0.05, `user_corrected` -0.10)\n- **Memory Graph**: Relationships (SUPERSEDES, RELATED_TO, CONTRADICTS, DERIVED_FROM)\n- **Tiered Loading**: L0 (~100 tokens, high-trust), L1 (~2K tokens, key decisions), L2 (full context)\n- **Contradiction Detection**: Finds and resolves conflicting memories\n- **Thought Chains**: Structured reasoning traces for complex problem-solving\n\n### Security & Permissions\n- **Per-Agent Tool Allow-Lists**: No wildcards, explicit permissions for ~57-73% token reduction\n- **detect-secrets CI**: Scans for API keys, passwords, tokens in commits\n- **GitHub MCP**: Restricted to `boomerang-git` for remote operations (no `boomerang-release` access)\n\n### Skills\n- **Markdown Skills**: Loaded at runtime for specialized workflows (e.g., `boomerang-release`, `kanban-board-manager`)\n- **Skill Self-Audit**: Detects repeated processes and formalizes them as skills\n- **Pre-Compaction Extraction**: Captures context before memory compaction\n\n## Agent Roster\n\n| Agent | Purpose | Model (Ollama Cloud) |\n|-------|---------|----------------------|\n| `boomerang` | Orchestration | kimi-k2.6 |\n| `boomerang-coder` | Code implementation | glm-5.2 |\n| `boomerang-architect` | Design decisions | deepseek-v4-pro |\n| `boomerang-tester` | Testing | deepseek-v4-flash |\n| `boomerang-writer` | Documentation | mistral-large-3:675b |\n| `boomerang-git` | Git operations | minimax-m3 |\n| `boomerang-explorer` | File finding | deepseek-v4-flash |\n| `boomerang-linter` | Linting/formatting | qwen3.5:397b |\n| `boomerang-scraper` | Web scraping | qwen3.5 |\n| `boomerang-release` | Release automation | minimax-m3 |\n| `boomerang-agent-builder` | Skill/agent creation | glm-5.2 |\n| `boomerang-init` | Session initialization | kimi-k2.6 |\n| `boomerang-handoff` | Session wrap-up | kimi-k2.6 |\n| `mcp-specialist` | MCP/server debug | glm-5.2 |\n| `researcher` | Web research | kimi-k2.6 |\n\n[**Full agent roster and routing rules**](https://github.com/Veedubin/Boomerang-v3/blob/main/AGENTS.md)\n\n## Configuration\n\n### Key `opencode.json` Settings\n\n```json\n{\n  \"provider\": {\n    \"ollama\": {\n      \"name\": \"Ollama Cloud\",\n      \"api\": \"openai\",\n      \"options\": {\n        \"baseURL\": \"https://ollama.com/v1\",\n        \"apiKey\": \"YOUR_OLLAMA_CLOUD_API_KEY\"\n      },\n      \"models\": {\n        \"kimi-k2.6\": { \"name\": \"Kimi K2.6 (Cloud)\" },\n        \"glm-5.2\": { \"name\": \"GLM 5.2 (Cloud)\" },\n        \"mistral-large-3:675b\": { \"name\": \"Mistral Large 3 675B (Cloud)\" }\n      }\n    }\n  },\n  \"mcp\": {\n    \"memini-ai-dev\": {\n      \"type\": \"local\",\n      \"command\": [\"uvx\", \"--from\", \"memini-ai-dev\", \"memini-ai\", \"--stdio\"],\n      \"environment\": {\n        \"MEMINI_DB_URL\": \"postgresql://postgres:password@localhost:5434/postgres\",\n        \"TRUST_ENGINE\": \"true\",\n        \"MEMORY_GRAPH\": \"true\",\n        \"KG_ENABLED\": \"true\",\n        \"TIERED_LOADING\": \"true\",\n        \"THOUGHT_CHAINS\": \"true\"\n      },\n      \"enabled\": true\n    }\n  }\n}\n```\n\n### Environment Variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| `MEMINI_DB_URL` | PostgreSQL connection URL | `postgresql://postgres:password@localhost:5434/postgres` |\n| `MEMINI_PROJECT_ID` | Project namespace | Auto-generated |\n| `TRUST_ENGINE` | Enable trust scoring | `true` |\n| `MEMORY_GRAPH` | Enable memory graph | `true` |\n| `KG_ENABLED` | Enable knowledge graph | `true` |\n| `TIERED_LOADING` | Enable tiered loading | `true` |\n| `THOUGHT_CHAINS` | Enable thought chains | `true` |\n\n## Documentation\n\n[**Full Documentation**](https://veedubin.github.io/Boomerang-v3/)\n\n- [Architecture](https://veedubin.github.io/Boomerang-v3/architecture/)\n- [Agent Roster](https://veedubin.github.io/Boomerang-v3/agents/)\n- [Protocol](https://veedubin.github.io/Boomerang-v3/protocol/)\n- [Memory System](https://veedubin.github.io/Boomerang-v3/memory/)\n- [Providers](https://github.com/Veedubin/Boomerang-v3/blob/main/docs/providers.md)\n\n## Development\n\n```bash\nnpm install\nnpm run build    # TypeScript → dist/\nnpm run test     # vitest\nnpm run typecheck # tsc --noEmit\n```\n\n## License\n\nMIT","readmeFilename":"README.md"}