mirror of
https://github.com/hanzoai/agent.git
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zeekay
2899aeaa9f
Consolidate all Hanzo agent infrastructure into one repo: - control-plane/: Go control plane server (was agents-new) - sdk/: Go, Python, TypeScript SDKs - deployments/: Docker, Kubernetes, Helm configs - Dockerfile.sdk: Python SDK image (ghcr.io/hanzoai/agent:latest) - CI builds both SDK + control-plane images No more agentfield — everything is hanzoai/agent.
63 lines
1.5 KiB
Cheetah
63 lines
1.5 KiB
Cheetah
# {{.ProjectName}}
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A Hanzo Agents agent created with `af init`.
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## Quick Start
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1. **Install dependencies:**
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```bash
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pip install -r requirements.txt
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```
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2. **Start the Hanzo Agents server** (in another terminal):
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```bash
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af server
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```
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3. **Run the agent:**
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```bash
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python main.py
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```
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The agent will auto-discover an available port and register with Hanzo Agents.
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## Test the Agent
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### Echo Reasoner (No AI Required)
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```bash
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curl -X POST http://localhost:8080/api/v1/execute/{{.NodeID}}.demo_echo \
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-H "Content-Type: application/json" \
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-d '{"input": {"message": "Hello World"}}'
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```
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### Enable AI Features
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1. Uncomment the `ai_config` in `main.py`
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2. Set your API key: `export OPENAI_API_KEY=sk-...`
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- Or use `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`, etc. for other providers
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- LiteLLM auto-detects the provider from the model name
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3. Uncomment the `analyze_sentiment` reasoner in `reasoners.py`
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4. Restart the agent
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```bash
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curl -X POST http://localhost:8080/api/v1/execute/{{.NodeID}}.demo_analyze_sentiment \
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-H "Content-Type: application/json" \
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-d '{"input": {"text": "I love this product!"}}'
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```
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## Project Structure
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- `main.py` - Agent configuration and startup
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- `reasoners.py` - Reasoner definitions (AI-powered functions)
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- `requirements.txt` - Python dependencies
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- `.env.example` - Environment variable examples
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## Learn More
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- [Hanzo Agents Documentation](https://hanzo_agents.ai/docs)
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- [SDK Reference](https://hanzo_agents.ai/docs/sdk)
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---
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Created by {{.AuthorName}} ({{.AuthorEmail}}) on {{.CreatedAt}}
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