How-to guides

These how-to guides answer "How do I...?" questions with practical solutions for specific problems.

Note: Many guides are still being written. Want to help? See our documentation contribution guide!

LLMs and chat models

Basic configuration

• How to configure different LLM providers

Advanced features

• How to handle API rate limits and retries
• How to stream responses from LLMs
• How to use function calling with OpenAI
• How to implement custom LLM providers

Prompts and templates

Template creation

• How to create dynamic prompt templates
• How to implement few-shot prompting

Output processing

• How to parse structured output from LLMs
• How to validate and sanitize LLM outputs

Memory and conversation

Memory management

• How to implement conversation memory
• How to persist conversation history
• How to implement context windowing
• How to handle long conversations

Agents and tools

Tool development

• How to create custom tools for agents
• How to handle tool execution errors

Agent optimization

• How to implement multi-step reasoning
• How to optimize agent performance

Production and deployment

Project structure

• How to structure LangChainGo projects
• How to handle secrets and configuration

Monitoring and scaling

• How to implement logging and monitoring
• How to deploy with Docker
• How to implement health checks
• How to scale LangChainGo applications

Testing and debugging

Testing strategies

• How to write tests for LangChainGo components
• How to mock LLM responses for testing

Performance

• How to debug chain execution
• How to benchmark performance

Integration patterns

Web applications

• How to integrate with web frameworks (Gin, Echo)
• How to implement background processing

Data integration

• How to integrate with databases
• How to implement caching strategies