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langchain-react-agent skill

/plugins/babysitter/skills/babysit/process/specializations/ai-agents-conversational/skills/langchain-react-agent

This skill enables a LangChain React agent to reason, act, and manage tool bindings with memory, error handling, and multi-LLM support.

npx playbooks add skill a5c-ai/babysitter --skill langchain-react-agent

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: langchain-react-agent
description: LangChain ReAct agent implementation with tool binding for reasoning and action loops
allowed-tools:
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  - Grep
---

# 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+

Overview

This skill implements a LangChain ReAct (Reasoning + Acting) agent pattern with tool binding and function-calling support. It provides a deterministic, resumable orchestration layer so an agent can alternate thought-action-observation loops while persisting state. The skill targets developer workflows that need structured agent reasoning with integrated tools and recoverable execution.

How this skill works

The agent executor is configured to run iterative ReAct loops: generate reasoning, select a tool or function call, execute the action, and ingest the observation back into the next reasoning step. Tools are registered with schemas and clear descriptions so the agent can reliably call them; responses are parsed into structured outputs. The system supports configuring LLM provider, memory backend, max iterations and timeout, plus retry and error-handling strategies for robust runs.

When to use it

  • Building agents that must call external tools or functions during multi-step reasoning
  • Creating deterministic, resumable workflows for development automation or CI tasks
  • Integrating multiple LLM providers while needing consistent tool schemas
  • Implementing agents that require state persistence and retryable actions
  • Prototyping function-calling agents that emit structured responses

Best practices

  • Define precise tool schemas and clear descriptions so the agent reliably chooses the right action
  • Limit max iterations and set timeouts to avoid runaway reasoning loops
  • Use a memory strategy appropriate for your task (buffer for short context, vector for retrieval)
  • Enable verbose/debug mode during development to inspect thought-action-observation traces
  • Implement idempotent tool actions when possible to simplify retries and resumption

Example use cases

  • Automating multi-step code changes: agent reasons about edits, calls code-mod tools, and validates results
  • Orchestrating deployment flows: agent runs diagnostic tools, fixes config issues, and resumes on failure
  • Customer support augmentation: agent invokes knowledge-base search tools and composes structured responses
  • Data preparation pipelines: agent selects transformation tools, runs them, and records observations for auditing
  • Function-calling prototypes: validate function schemas and structured outputs across different LLMs

FAQ

Which LLMs does this support?

It works with any LangChain-compatible provider; common choices are OpenAI and Anthropic via their LangChain integrations.

How do I make runs resumable?

Persist agent state and memory entries after each action. Resume by restoring the executor state and continuing from the last observation.