agent-frameworks

# Agent Frameworks

Build autonomous AI agents with tools, planning, and multi-agent collaboration.

## Quick Start

### LangChain Agent
```python
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
from langchain import hub

# Define tools
def search(query: str) -> str:
    return f"Search results for: {query}"

def calculate(expression: str) -> str:
    return str(eval(expression))

tools = [
    Tool(name="Search", func=search, description="Search the web"),
    Tool(name="Calculator", func=calculate, description="Do math")
]

# Create agent
llm = ChatOpenAI(model="gpt-4", temperature=0)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)

# Execute
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
result = executor.invoke({"input": "What is 2+2 and who invented calculus?"})
```

### OpenAI Function Calling
```python
from openai import OpenAI
import json

client = OpenAI()

# Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    }
]

# Chat with tool use
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto"
)

# Handle tool calls
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    # Execute the tool and continue conversation
```

## Framework Comparis