pip install hawkins-agent
```
Requires Python 3.11 or higher.
## Quick Start
Here's a simple example to get you started:
```python
from hawkins_agent import AgentBuilder
from hawkins_agent.tools import WebSearchTool
from hawkins_agent.mock import KnowledgeBase
async def main():
# Create a knowledge base
kb = KnowledgeBase()
# Create agent with web search capabilities
agent = (AgentBuilder("researcher")
.with_model("gpt-4o")
.with_knowledge_base(kb)
.with_tool(WebSearchTool())
.build())
# Process a query
response = await agent.process("What are the latest developments in AI?")
print(response.message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
## Advanced Usage
### RAG-Enabled Knowledge Assistant
Create an agent that can answer questions based on its knowledge base:
```python
from hawkins_agent import AgentBuilder
from hawkins_agent.tools import RAGTool
from hawkins_rag import HawkinsRAG
# Initialize RAG system and tool
rag = HawkinsRAG()
rag_tool = RAGTool(knowledge_base=rag)
# Create agent with RAG capabilities
agent = (AgentBuilder("knowledge_assistant")
.with_model("gpt-4o")
.with_tool(rag_tool)
.build())
# Add documents to knowledge base
await rag_tool.add_document(
content="Document content...",
metadata={"topic": "AI", "source": "research_paper"}
)
# Query the knowledge base
response = await agent.process("What are the key findings in the AI research?")
print(response.message)
```
### Multi-Agent Workflow
Create complex workflows with multiple specialized agents:
```python
from hawkins_agent import AgentBuilder, FlowManager, FlowStep
from hawkins_agent.tools import WebSearchTool, WeatherTool
# Create specialized agents
research_agent = (AgentBuilder("researcher")
.with_model("gpt-4o")
.with_tool(WebSearchTool())
.build())
writer_agent = (AgentBuilder("writer")
.with_model("gpt-4o")
.build())
# Create flow manager
flow = FlowManager()
# Define workflow steps
async def research_step(input_data, context):
query = input_data.get("topic")
result = await research_agent.process(f"Research this topic: {query}")
return {"research": result.message}
async def writing_step(input_data, context):
research = context.get("research", {}).get("research")
result = await writer_agent.process(f"Write an article based on: {research}")
return {"article": result.message}
# Add steps to flow
flow.add_step(FlowStep(
name="research",
agent=research_agent,
process=research_step
))
flow.add_step(FlowStep(
name="writing",
agent=writer_agent,
process=writing_step,
requires=["research"]
))
# Execute flow
results = await flow.execute({"topic": "AI trends in 2024"})
```
### Using Custom Tools
Create your own tools by extending the BaseTool class:
```python
from hawkins_agent.tools.base import BaseTool
from hawkins_agent.types import ToolResponse
class CustomTool(BaseTool):
name = "custom_tool"
description = "A custom tool for specific tasks"
async def execute(self, query: str) -> ToolResponse:
try:
# Tool implementation here
result = await self._process(query)
return ToolResponse(success=True, result=result)
except Exception as e:
return ToolResponse(success=False, error=str(e))
```
## Documentation
For more detailed documentation, see:
- [Flow System Documentation](docs/flows.md)
- [Custom Tools Guide](docs/custom_tools.md)
- [Memory Management](docs/memory_management.md)
- [API Reference](docs/api_reference.md)
## Examples
The `examples/` directory contains several example implementations:
- `simple_agent.py`: Basic agent usage
- `multi_agent_flow.py`: Complex multi-agent workflow
- `tool_test.py`: Tool integration examples
- `rag_agent_example.py`: Knowledge-based agent with RAG capabilities
- `blog_writer_flow.py`: Content generation workflow
- `maldives_trip_planner.py`: Travel planning agent system
## Development
To contribute to the project:
1. Clone the repository
2. Install development dependencies:
```bash
pip install -e .[dev]
```
3. Run tests:
```bash
pytest
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"description": "pip install hawkins-agent\n```\n\nRequires Python 3.11 or higher.\n\n## Quick Start\n\nHere's a simple example to get you started:\n\n```python\nfrom hawkins_agent import AgentBuilder\nfrom hawkins_agent.tools import WebSearchTool\nfrom hawkins_agent.mock import KnowledgeBase\n\nasync def main():\n # Create a knowledge base\n kb = KnowledgeBase()\n\n # Create agent with web search capabilities\n agent = (AgentBuilder(\"researcher\")\n .with_model(\"gpt-4o\")\n .with_knowledge_base(kb)\n .with_tool(WebSearchTool())\n .build())\n\n # Process a query\n response = await agent.process(\"What are the latest developments in AI?\")\n print(response.message)\n\nif __name__ == \"__main__\":\n import asyncio\n asyncio.run(main())\n```\n\n## Advanced Usage\n\n### RAG-Enabled Knowledge Assistant\n\nCreate an agent that can answer questions based on its knowledge base:\n\n```python\nfrom hawkins_agent import AgentBuilder\nfrom hawkins_agent.tools import RAGTool\nfrom hawkins_rag import HawkinsRAG\n\n# Initialize RAG system and tool\nrag = HawkinsRAG()\nrag_tool = RAGTool(knowledge_base=rag)\n\n# Create agent with RAG capabilities\nagent = (AgentBuilder(\"knowledge_assistant\")\n .with_model(\"gpt-4o\")\n .with_tool(rag_tool)\n .build())\n\n# Add documents to knowledge base\nawait rag_tool.add_document(\n content=\"Document content...\",\n metadata={\"topic\": \"AI\", \"source\": \"research_paper\"}\n)\n\n# Query the knowledge base\nresponse = await agent.process(\"What are the key findings in the AI research?\")\nprint(response.message)\n```\n\n### Multi-Agent Workflow\n\nCreate complex workflows with multiple specialized agents:\n\n```python\nfrom hawkins_agent import AgentBuilder, FlowManager, FlowStep\nfrom hawkins_agent.tools import WebSearchTool, WeatherTool\n\n# Create specialized agents\nresearch_agent = (AgentBuilder(\"researcher\")\n .with_model(\"gpt-4o\")\n .with_tool(WebSearchTool())\n .build())\n\nwriter_agent = (AgentBuilder(\"writer\")\n .with_model(\"gpt-4o\")\n .build())\n\n# Create flow manager\nflow = FlowManager()\n\n# Define workflow steps\nasync def research_step(input_data, context):\n query = input_data.get(\"topic\")\n result = await research_agent.process(f\"Research this topic: {query}\")\n return {\"research\": result.message}\n\nasync def writing_step(input_data, context):\n research = context.get(\"research\", {}).get(\"research\")\n result = await writer_agent.process(f\"Write an article based on: {research}\")\n return {\"article\": result.message}\n\n# Add steps to flow\nflow.add_step(FlowStep(\n name=\"research\",\n agent=research_agent,\n process=research_step\n))\n\nflow.add_step(FlowStep(\n name=\"writing\",\n agent=writer_agent,\n process=writing_step,\n requires=[\"research\"]\n))\n\n# Execute flow\nresults = await flow.execute({\"topic\": \"AI trends in 2024\"})\n```\n\n### Using Custom Tools\n\nCreate your own tools by extending the BaseTool class:\n\n```python\nfrom hawkins_agent.tools.base import BaseTool\nfrom hawkins_agent.types import ToolResponse\n\nclass CustomTool(BaseTool):\n name = \"custom_tool\"\n description = \"A custom tool for specific tasks\"\n\n async def execute(self, query: str) -> ToolResponse:\n try:\n # Tool implementation here\n result = await self._process(query)\n return ToolResponse(success=True, result=result)\n except Exception as e:\n return ToolResponse(success=False, error=str(e))\n```\n\n## Documentation\n\nFor more detailed documentation, see:\n- [Flow System Documentation](docs/flows.md)\n- [Custom Tools Guide](docs/custom_tools.md)\n- [Memory Management](docs/memory_management.md)\n- [API Reference](docs/api_reference.md)\n\n## Examples\n\nThe `examples/` directory contains several example implementations:\n- `simple_agent.py`: Basic agent usage\n- `multi_agent_flow.py`: Complex multi-agent workflow\n- `tool_test.py`: Tool integration examples\n- `rag_agent_example.py`: Knowledge-based agent with RAG capabilities\n- `blog_writer_flow.py`: Content generation workflow\n- `maldives_trip_planner.py`: Travel planning agent system\n\n## Development\n\nTo contribute to the project:\n\n1. Clone the repository\n2. Install development dependencies:\n```bash\npip install -e .[dev]\n```\n3. Run tests:\n```bash\npytest\n",
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