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langchain-tools

Agent Building
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LangChain tool creation and integration utilities for agent systems

QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/ai-agents-conversational/skills/langchain-tools/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/langchain-tools/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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