langchain-react-agent
LangChain ReAct agent implementation with tool binding for reasoning and action loops
npx skills add a5c-ai/babysitter --skill langchain-react-agent --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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+
- Capabilities
- Target Processes
- Implementation Details
- Core Components
- Configuration Options
- Dependencies
What does the langchain-react-agent skill do?
LangChain ReAct agent implementation with tool binding for reasoning and action loops
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill langchain-react-agent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.