# Developing AI Agents > AI Agent Development with LangChain and LangGraph Use when this capability is needed. - Skill: `tomevault-io/developing-ai-agents` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/developing-ai-agents` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/developing-ai-agents/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/developing-ai-agents --- # AI Agent Development Skill This skill provides expertise in building AI agents using modern frameworks like LangChain, LangGraph, CrewAI, and AutoGen. ## Core Capabilities ### 1. Agent Architecture Design - Choose appropriate agent patterns (ReAct, Plan-Execute, Reflection) - Design multi-agent systems with proper coordination - Implement state management and memory - Structure agent workflows for complex tasks ### 2. Tool Integration - Create custom tools for specific domains - Integrate external APIs and services - Implement tool error handling and fallbacks - Optimize tool selection and usage ### 3. LangChain Expertise - Build agents with LCEL (LangChain Expression Language) - Implement RAG systems with vector databases - Use LangSmith for observability and debugging - Deploy with LangServe ### 4. LangGraph Workflows - Design state graphs for complex workflows - Implement checkpointing and persistence - Add human-in-the-loop capabilities - Build supervisor and worker patterns ### 5. Multi-Agent Orchestration - Coordinate multiple specialized agents - Implement communication protocols - Handle agent delegation and task routing - Manage shared state and context ## Key Patterns ### ReAct Pattern ```python # Reasoning + Acting loop # 1. Thought: What should I do? # 2. Action: Execute tool # 3. Observation: See result # 4. Repeat until done ``` ### Supervisor Pattern ```python # Central coordinator delegates to workers # - Supervisor: Routes tasks # - Workers: Execute specialized tasks # - Synthesis: Combine results ``` ### Plan-and-Execute ```python # Strategic planning before execution # 1. Create comprehensive plan # 2. Execute steps sequentially # 3. Replan if needed # 4. Synthesize results ``` ## Tools and Technologies - **LangChain**: Core framework for agents - **LangGraph**: State machine workflows - **CrewAI**: Role-based multi-agent systems - **AutoGen**: Microsoft's agent framework - **Vector DBs**: ChromaDB, Pinecone, Qdrant - **LLMs**: GPT-4o, Claude Opus 4.6, Gemini 3 Pro - **Observability**: LangSmith, Weights & Biases ## Best Practices 1. **Start Simple**: Begin with basic agents, add complexity gradually 2. **Use Structured Outputs**: Leverage Pydantic models for reliability 3. **Implement Logging**: Track all agent actions and decisions 4. **Set Limits**: Max iterations, token limits, timeouts 5. **Error Handling**: Graceful degradation and fallbacks 6. **Test Thoroughly**: Unit tests, integration tests, end-to-end tests 7. **Monitor Costs**: Track API usage and optimize 8. **Human Oversight**: Add human-in-the-loop for critical decisions ## Common Challenges and Solutions ### Challenge: Agent Loops **Solution**: Set max_iterations, implement loop detection, add explicit termination conditions ### Challenge: High Costs **Solution**: Use cheaper models for planning, cache results, implement rate limiting ### Challenge: Unreliable Outputs **Solution**: Use structured outputs, add validation, implement retry logic ### Challenge: Context Overflow **Solution**: Implement memory management, use summarization, offload to vector DB ### Challenge: Tool Selection Errors **Solution**: Improve tool descriptions, add examples, implement fallbacks ## Example Projects 1. **Research Assistant**: Multi-agent system for comprehensive research 2. **Code Review Agent**: Automated code analysis and suggestions 3. **Customer Support**: Intelligent ticket routing and response 4. **Data Analysis**: Automated data exploration and insights 5. **Content Creation**: Multi-step content generation pipeline ## Resources - LangChain Documentation: https://python.langchain.com - LangGraph Documentation: https://langchain-ai.github.io/langgraph/ - LangSmith: https://smith.langchain.com - Example Repositories: See github-agent-templates.json ## Usage in Antigravity This skill is automatically available when using the `ai-agent-development` or `multi-agent-systems` blueprints. It provides: - Template selection guidance - Architecture recommendations - Code generation assistance - Debugging support - Best practices enforcement ## When to Use Placeholder content for When to Use. ## Prerequisites Placeholder content for Prerequisites. ## Process Placeholder content for Process. --- > Source: [gitwalter/antigravity-agent-factory](https://github.com/gitwalter/antigravity-agent-factory) — distributed by [TomeVault](https://tomevault.io).