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https://github.com/hanzoai/agents.git
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zeekayandGitHub
5b5cf9e9db
Transforms hanzoai/agents from a marketplace config repo into a full agent orchestration platform by merging the AgentField infrastructure: - Go control plane (K8s-like agent orchestration, service mesh, DAG workflows) - Python SDK (FastAPI-based agent framework, LiteLLM, memory, DID/VC) - TypeScript SDK (@hanzo/agents - Vercel AI SDK, MCP, workflows) - Go SDK (idiomatic agent builder + client) - React 19 embedded dashboard (XyFlow DAGs, Recharts, Radix UI) - Docker/K8s/Helm deployment configs - Examples: hello world, deep research, RAG, simulation, benchmarks - Functional test suite across Python/Go/TypeScript All references rebranded: AgentField → Hanzo Agents - Module: github.com/hanzoai/agents/control-plane - Python: hanzo_agents - TypeScript: @hanzo/agents - Go: github.com/hanzoai/agents/sdk/go - Env vars: HANZO_AGENTS_* - Config: hanzo-agents.yaml Previous marketplace configs moved to config/ directory.
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
"""
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RAG Evaluation Agent Node
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Multi-reasoner evaluation system for RAG-generated responses featuring:
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- Faithfulness: Adversarial debate pattern (Prosecutor vs Defender)
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- Relevance: Multi-jury consensus voting
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- Hallucination: Hybrid ML+LLM chain-of-verification
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- Constitutional: Configurable principles-based evaluation
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Production-ready backend AI for enterprise RAG evaluation.
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"""
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import os
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# Load .env file for local development
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except ImportError:
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pass # python-dotenv not installed, use environment variables directly
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from hanzo_agents import Agent, AIConfig
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from reasoners import router
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# Initialize agent
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app = Agent(
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node_id="rag-evaluation",
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version="1.0.0",
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description="Multi-reasoner RAG evaluation with adversarial debate, jury consensus, and hybrid ML+LLM verification",
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hanzo_agents_server=os.getenv("HANZO_AGENTS_SERVER", "http://localhost:8080"),
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ai_config=AIConfig(
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model=os.getenv("AI_MODEL", "openrouter/deepseek/deepseek-chat-v3-0324"),
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),
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)
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# Include the evaluation router with all metric reasoners
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app.include_router(router)
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if __name__ == "__main__":
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# Start the agent server
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port_env = os.getenv("PORT")
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if port_env is None:
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app.run(auto_port=True, host="0.0.0.0")
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else:
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app.run(port=int(port_env), host="0.0.0.0")
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