mirror of
https://github.com/RightNow-AI/openfang.git
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jaberjaber23
5692c96494
Open-source Agent OS built in Rust. - 14 crates, 1,767+ tests, zero clippy warnings - 7 autonomous Hands (Clip, Lead, Collector, Predictor, Researcher, Twitter, Browser) - 16 security systems (WASM sandbox, Merkle audit trail, taint tracking, Ed25519 signing, SSRF protection, secret zeroization, HMAC-SHA256 mutual auth, and more) - 30 pre-built agents across 4 performance tiers - 40 channel adapters (Telegram, Discord, Slack, WhatsApp, Teams, and 35 more) - 38 built-in tools + MCP client/server + A2A protocol - 26 LLM providers with intelligent routing and cost tracking - 60+ bundled skills with FangHub marketplace - Tauri 2.0 native desktop app - 140+ REST/WS/SSE API endpoints with Alpine.js dashboard - OpenAI-compatible /v1/chat/completions endpoint - One-command install, production-ready
64 lines
2.1 KiB
TOML
64 lines
2.1 KiB
TOML
name = "orchestrator"
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version = "0.1.0"
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description = "Meta-agent that decomposes complex tasks, delegates to specialist agents, and synthesizes results."
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author = "openfang"
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module = "builtin:chat"
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[model]
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provider = "deepseek"
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model = "deepseek-chat"
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api_key_env = "DEEPSEEK_API_KEY"
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max_tokens = 8192
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temperature = 0.3
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system_prompt = """You are Orchestrator, the command center of the OpenFang Agent OS.
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Your role is to decompose complex tasks into subtasks and delegate them to specialist agents.
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AVAILABLE TOOLS:
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- agent_list: See all running agents and their capabilities
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- agent_send: Send a message to a specialist agent and get their response
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- agent_spawn: Create new agents when needed
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- agent_kill: Terminate agents no longer needed
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- memory_store: Save results and state to shared memory
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- memory_recall: Retrieve shared data from memory
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SPECIALIST AGENTS (spawn or message these):
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- coder: Writes and reviews code
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- researcher: Gathers information
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- writer: Creates documentation and content
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- ops: DevOps, system operations
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- analyst: Data analysis and metrics
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- architect: System design and architecture
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- debugger: Bug hunting and root cause analysis
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- security-auditor: Security review and vulnerability assessment
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- test-engineer: Test design and quality assurance
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WORKFLOW:
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1. Analyze the user's request
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2. Use agent_list to see available agents
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3. Break the task into subtasks
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4. Delegate each subtask to the most appropriate specialist via agent_send
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5. Synthesize all responses into a coherent final answer
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6. Store important results in shared memory for future reference
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Always explain your delegation strategy before executing it.
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Be thorough but efficient — don't delegate trivially simple tasks."""
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[[fallback_models]]
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provider = "groq"
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model = "llama-3.3-70b-versatile"
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api_key_env = "GROQ_API_KEY"
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[schedule]
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continuous = { check_interval_secs = 120 }
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[resources]
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max_llm_tokens_per_hour = 500000
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[capabilities]
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tools = ["agent_send", "agent_spawn", "agent_list", "agent_kill", "memory_store", "memory_recall", "file_read", "file_write"]
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memory_read = ["*"]
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memory_write = ["*"]
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agent_spawn = true
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agent_message = ["*"]
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