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Concepts

Understanding the fundamental concepts behind LangChainGo helps you build better applications.

Core architecture​

LangChainGo is built around several key architectural principles:

Framework design​

  • Interface-driven design: Every major component is defined by interfaces for modularity and testability
  • Component architecture: Clear separation between models, chains, memory, agents, and tools
  • Go-specific patterns: Leverage Go's strengths like interfaces, goroutines, and explicit error handling

Execution model​

  • Context propagation: All operations use context.Context for cancellation and timeouts
  • Error handling: Explicit error handling with typed errors for different failure modes
  • Concurrency: Native support for concurrent operations using goroutines and channels
  • Resource management: Proper cleanup and resource management patterns

Language models​

Model abstraction​

The Model interface provides a unified way to interact with different LLM providers:

  • Consistent API across OpenAI, Anthropic, Google AI, and local models
  • Multi-modal capabilities for text, images, and other content types
  • Flexible configuration through functional options
  • Provider-specific features accessible through type assertions

Communication patterns​

  • Request/Response: Standard synchronous communication with LLMs
  • Streaming: Real-time response streaming for better user experience
  • Batch processing: Efficient handling of multiple requests
  • Rate limiting: Built-in backoff and retry mechanisms

Memory and state management​

Memory types​

  • Buffer memory: Stores complete conversation history
  • Window memory: Maintains sliding window of recent messages
  • Token buffer: Manages memory based on token count limits
  • Summary memory: Automatically summarizes older conversations

State persistence​

  • In-memory storage for development and testing
  • File-based persistence for straightforward applications
  • Database integration for production applications
  • Custom storage backends through interfaces

Agents and autonomy​

Agent architecture​

Agents combine reasoning with tool usage:

  • Decision making: LLM determines which tools to use
  • Tool integration: Seamless integration with external APIs and functions
  • Execution loop: Iterative reasoning-action-observation cycles
  • Memory integration: Maintain context across multiple tool calls

Tool system​

  • Built-in tools for common operations (calculator, web search, file operations)
  • Custom tool creation through straightforward interfaces
  • Tool composition for complex operations
  • Error handling and timeout management

Production considerations​

Performance​

  • Connection pooling for HTTP clients
  • Caching strategies for responses and embeddings
  • Concurrent processing with goroutines
  • Memory-efficient streaming operations

Reliability​

  • Circuit breaker patterns for external API calls
  • Graceful degradation when services are unavailable
  • Comprehensive error handling and recovery
  • Health checks and monitoring integration

Security​

  • Secure API key management
  • Input validation and sanitization
  • Output filtering for sensitive data
  • Rate limiting and abuse protection

These concepts form the foundation for building robust, scalable applications with LangChainGo. Each concept builds upon Go's strengths while providing the flexibility needed for diverse AI applications.