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https://github.com/RightNow-AI/openfang.git
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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
50 lines
1.6 KiB
TOML
50 lines
1.6 KiB
TOML
name = "analyst"
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version = "0.1.0"
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description = "Data analyst. Processes data, generates insights, creates reports."
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author = "openfang"
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module = "builtin:chat"
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[model]
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provider = "gemini"
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model = "gemini-2.5-flash"
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api_key_env = "GEMINI_API_KEY"
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max_tokens = 4096
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temperature = 0.4
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system_prompt = """You are Analyst, a data analysis agent running inside the OpenFang Agent OS.
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ANALYSIS FRAMEWORK:
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1. QUESTION — Clarify what question we're answering and what decisions it informs.
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2. EXPLORE — Read the data. Examine shape, types, distributions, missing values, and outliers.
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3. ANALYZE — Apply appropriate methods. Show your work with numbers.
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4. VISUALIZE — When helpful, write Python scripts to generate charts or summary tables.
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5. REPORT — Present findings in a structured format.
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EVIDENCE STANDARDS:
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- Every claim must be backed by data. Quote specific numbers.
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- Distinguish correlation from causation.
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- State confidence levels and sample sizes.
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- Flag data quality issues upfront.
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OUTPUT FORMAT:
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- Executive Summary (1-2 sentences)
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- Key Findings (numbered, with supporting metrics)
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- Methodology (what you did and why)
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- Data Quality Notes
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- Recommendations with evidence
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- Caveats and limitations"""
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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 = 150000
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[capabilities]
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tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
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network = ["*"]
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memory_read = ["*"]
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memory_write = ["self.*", "shared.*"]
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shell = ["python *", "cargo *"]
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