Knowledge
Agents

ReAct Agents

ReAct (Reasoning and Acting) is a powerful agent pattern that combines the reasoning capabilities of Large Language Models with the ability to take actions through tools. This approach allows agents to break down complex problems, gather necessary information, and provide accurate responses.

ReAct Agents

ReAct (Reasoning and Acting) is a powerful agent pattern that combines the reasoning capabilities of Large Language Models with the ability to take actions through tools. This approach allows agents to break down complex problems, gather necessary information, and provide accurate responses.

How ReAct Works

The ReAct pattern follows a cyclical process:

Diagram credit: Scrimba

The process consists of these key steps:

  1. Pass query to LLM - The user's question is sent to the language model
  2. LLM thinks about the next step - The model analyzes what information it needs
  3. LLM determines what data it needs and calls a function - Based on the analysis, the model selects and executes appropriate tools
  4. LLM observes the data and determines if it's ready to answer - The model evaluates if it has sufficient information
  5. Decision Point - If not ready, it loops back to step 2; if ready, it proceeds to answer
  6. Respond to user - The final answer is provided based on all gathered information

ReAct System Prompt Example

Here's a comprehensive system prompt for implementing a ReAct agent:

You are a helpful assistant that uses the ReAct (Reasoning and Acting) framework to answer questions.

Follow this format for each response:

Thought: [Your reasoning about what you need to do next]
Action: [The tool/function you want to call]
Action Input: [The input for the tool]
Observation: [The result of the tool call]
... (repeat Thought/Action/Observation as needed)
Thought: [I now have enough information to answer]
Final Answer: [Your final response to the user]

Available tools:
- getLocation: Get the user's current location
- getCurrentWeather: Get weather information for a location
- searchWeb: Search the web for information
- calculate: Perform mathematical calculations

Rules:
1. Always start with a Thought
2. Only use the available tools listed above
3. If you don't have enough information after using tools, continue the cycle
4. Provide a Final Answer only when you have sufficient information
5. Be specific and accurate in your responses

ReAct in Action

Here's how a ReAct agent handles a user query:

Diagram credit: Scrimba

Example Interaction:

User: "What should I do tonight for fun?"

Agent Response:

Thought: The user wants suggestions for fun activities tonight. To provide good recommendations, I should know their location and current weather conditions, as these factors affect what activities are suitable.

Action: getLocation
Action Input: {}
Observation: New York City, NY

Thought: Now I have the user's location. I should check the current weather to suggest appropriate activities for tonight.

Action: getCurrentWeather
Action Input: {"location": "New York City, NY"}
Observation: Temperature: 72°F, Clear skies, Low humidity

Thought: Perfect! I have the location (New York City) and weather information (clear, pleasant weather). Now I can suggest appropriate fun activities for tonight.

Final Answer: Based on your location in New York City and the clear, pleasant weather (72°F), here are some great options for tonight:

1. **Outdoor Activities**: Take a walk in Central Park, visit a rooftop bar with city views, or enjoy outdoor dining

2. **Entertainment**: Catch a Broadway show, visit a comedy club, or explore the nightlife in areas like Greenwich Village

3. **Cultural**: Visit museums that have evening hours, attend a live music venue, or explore art galleries

4. **Social**: Meet friends at a local restaurant, join a trivia night at a bar, or attend a community event

The clear weather makes outdoor activities particularly appealing tonight. What type of experience are you most interested in?

Key Benefits of ReAct

  1. Enhanced Accuracy: By gathering real-time information, ReAct agents provide more accurate and up-to-date responses
  2. Transparency: The Thought-Action-Observation cycle makes the agent's reasoning process visible and understandable
  3. Flexibility: Can handle a wide variety of tasks by combining different tools and reasoning approaches
  4. Problem Decomposition: Breaks complex problems into manageable steps
  5. Error Recovery: Can retry actions or try different approaches when initial attempts fail

Implementation Considerations

When building ReAct agents, consider:

  • Tool Selection: Choose tools that provide reliable, relevant information
  • Error Handling: Implement robust error handling for tool failures
  • Context Management: Maintain context throughout the reasoning cycle
  • Safety Measures: Include safeguards against malicious tool usage
  • Performance: Optimize for reasonable response times

ReAct agents represent a significant advancement in AI capabilities, enabling more sophisticated and helpful interactions that go beyond simple question-answering.

Overview

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. This section covers various agent architectures and patterns.

RAG

comprehensive guide to Retrieval-Augmented Generation (RAG), covering Vector Databases, Semantic Search, Embeddings, Chunking strategies, and the RAG workflow.