LangChain ReAct Agent Skill

Capabilities

• Implement ReAct (Reasoning + Acting) agent patterns using LangChain
• Configure tool binding and function calling for agents
• Design thought-action-observation loops
• Integrate with various LLM providers (OpenAI, Anthropic, etc.)
• Handle agent memory and state persistence
• Implement error handling and retry logic for agent actions

Target Processes

• react-agent-implementation
• function-calling-agent

Implementation Details

Core Components

1. Agent Executor Setup: Configure LangChain AgentExecutor with appropriate settings
2. Tool Integration: Bind tools with proper schemas and descriptions
3. Prompt Engineering: Design system prompts for ReAct reasoning patterns
4. Output Parsing: Parse agent outputs and handle structured responses

Configuration Options

• LLM model selection and parameters
• Tool definitions and schemas
• Memory type (buffer, summary, vector)
• Max iterations and timeout settings
• Verbose/debug mode configuration

Dependencies

• langchain
• langchain-openai / langchain-anthropic
• Python 3.9+