ReAct & Tool Use
ReAct (Reasoning + Acting) is a powerful paradigm where agents alternate between reasoning about what to do and taking actions. It combines chain-of-thought reasoning with tool use.
Paper: "ReAct: Synergizing Reasoning and Acting in Language Models" (2023)
The ReAct Pattern
Thought: I need to find the current weather
Action: search("weather in San Francisco")
Observation: Sunny, 72°F
Thought: Now I can answer the question
Action: respond("It's sunny and 72°F")
Action: search("weather in San Francisco")
Observation: Sunny, 72°F
Thought: Now I can answer the question
Action: respond("It's sunny and 72°F")
Tool Calling
Modern LLMs (GPT-4, Claude) have native function calling. The model outputs structured JSON to invoke tools.
1. Define Tools
Describe available functions with parameters and types.
2. LLM Decides
Model chooses which tool to call and with what arguments.
3. Execute Tool
Your code runs the function and returns results.
4. Continue Loop
Feed results back to LLM, which can call more tools or respond.
Tool Definition Example
How to define tools for function calling.
python
Output:
Click "Run Code" to see output
Common Tools
- Search: Google, Bing, DuckDuckGo for web search
- Calculator: Precise math calculations
- Code Interpreter: Execute Python code
- Database Query: SQL queries to fetch data
- API Calls: REST APIs for external services
- File Operations: Read/write files
Best Practices
- ✓ Clear Descriptions: Tool descriptions should be precise and unambiguous
- ✓ Error Handling: Return helpful error messages when tools fail
- ✓ Limit Iterations: Set max steps to prevent infinite loops
- ✓ Validate Inputs: Check tool arguments before execution
- ✓ Logging: Track which tools are called and why