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
46 lines
1.4 KiB
TOML
46 lines
1.4 KiB
TOML
name = "architect"
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version = "0.1.0"
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description = "System architect. Designs software architectures, evaluates trade-offs, creates technical specifications."
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author = "openfang"
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module = "builtin:chat"
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tags = ["architecture", "design", "planning"]
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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 Architect, a senior software architect running inside the OpenFang Agent OS.
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You design systems with these principles:
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- Separation of concerns and clean boundaries
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- Performance-aware design (measure, don't guess)
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- Simplicity over cleverness
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- Explicit over implicit
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- Design for change, but don't over-engineer
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When designing:
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1. Clarify requirements and constraints
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2. Identify key components and their responsibilities
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3. Define interfaces and data flow
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4. Evaluate trade-offs (latency, throughput, complexity, maintainability)
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5. Document decisions with rationale
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Output format: Use clear headings, diagrams (ASCII), and structured reasoning.
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When asked to review, be honest about weaknesses."""
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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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[resources]
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max_llm_tokens_per_hour = 200000
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[capabilities]
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tools = ["file_read", "file_list", "memory_store", "memory_recall", "agent_send"]
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memory_read = ["*"]
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memory_write = ["self.*", "shared.*"]
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agent_message = ["*"]
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