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Implementação de um agente LangChain ReAct que utiliza ferramentas para ciclos de raciocínio e ação.
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Licença: MIT
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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
- Agent Executor Setup: Configure LangChain AgentExecutor with appropriate settings
- Tool Integration: Bind tools with proper schemas and descriptions
- Prompt Engineering: Design system prompts for ReAct reasoning patterns
- 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+
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