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langchain-tools skill

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

This skill helps you create and integrate LangChain tools with robust schemas, enabling reliable function calling and resilient tool orchestration.

npx playbooks add skill a5c-ai/babysitter --skill langchain-tools

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SKILL.md
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---
name: langchain-tools
description: LangChain tool creation and integration utilities for agent systems
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
---

# LangChain Tools Skill

## Capabilities

- Create custom LangChain tools with proper schemas
- Integrate existing tools and APIs
- Design tool descriptions for optimal LLM understanding
- Implement structured tool inputs with Pydantic
- Handle tool errors and fallbacks
- Create tool chains and pipelines

## Target Processes

- custom-tool-development
- function-calling-agent

## Implementation Details

### Tool Creation Patterns

1. **@tool decorator**: Simple function-based tools
2. **StructuredTool**: Tools with complex input schemas
3. **BaseTool subclass**: Full control over tool behavior
4. **Tool from functions**: Dynamic tool creation

### Configuration Options

- Tool name and description
- Input schema (args_schema)
- Return type specification
- Error handling strategy
- Async/sync execution modes

### Best Practices

- Clear, action-oriented descriptions
- Explicit input parameter documentation
- Proper error messages for LLM understanding
- Idempotent operations where possible

### Dependencies

- langchain-core
- pydantic

Overview

This skill provides utilities to create and integrate LangChain tools for agent systems. It focuses on building well-typed, resilient tools and composing them into chains and pipelines. The goal is deterministic, resumable orchestration suitable for Claude Code style agent workflows.

How this skill works

The skill offers patterns for tool construction: simple function tools, StructuredTool with input schemas, BaseTool subclasses, and dynamic tool-from-function factories. It supports configuring names, descriptions, args_schema, return types, sync/async execution, and error handling strategies. Tools can be combined into chains and pipelines that expose clear schemas for function-calling agents.

When to use it

  • Building new LangChain tools for function-calling agents
  • Wrapping external APIs with explicit input/output schemas
  • Designing tool descriptions optimized for LLM interpretation
  • Creating error-resilient tool chains for automated workflows
  • Migrating ad-hoc helpers into typed, reusable tools

Best practices

  • Write action-oriented, concise tool names and descriptions
  • Define explicit input schemas (Pydantic) to validate and document inputs
  • Make side effects idempotent when possible to simplify retries
  • Provide clear, structured error messages so agents can choose fallbacks
  • Prefer small, composable tools and assemble them into pipelines

Example use cases

  • Expose a third-party API as a StructuredTool with validated inputs and typed outputs
  • Create a chain that fetches data, transforms it, and stores results with retries
  • Implement a BaseTool subclass to handle streaming or long-running operations
  • Dynamically generate tools at runtime for plugin-style extensibility
  • Instrument tools with deterministic checkpoints for resumable orchestration

FAQ

Which pattern should I pick for a simple helper?

Use the @tool decorator or a Tool from function factory for quick, stateless helpers.

When do I need StructuredTool or BaseTool?

Choose StructuredTool when inputs are complex and need Pydantic validation. Use BaseTool for full control over execution, streaming, or custom lifecycle behavior.