Langchain Summarization Middleware

Overview

This module provides a specialized middleware designed for LangChain agents that manage conversation history. Its primary function is to intercept the message flow and summarize lengthy chat histories when they approach token limits. By intelligently distilling the most critical information, it ensures the agent retains necessary context without exceeding the LLM's input capacity.

Capabilities

• Token Overflow Prevention: Actively monitors conversation history length against model constraints.
• Context Extraction: Uses a structured prompt to guide the LLM in extracting only the most relevant facts, decisions, and goals from past interactions.
• History Replacement: Replaces the entire chat history with the newly generated, concise summary context.
• Framework Integration: Designed to seamlessly integrate into existing LangChain agent workflows via middleware hooks.

Example Use Cases

1. Long-Running Chatbots: Ideal for customer support bots or assistants that require multi-session memory over many turns without losing critical details.
2. Complex Workflow Agents: Useful in research agents where an investigation spans dozens of steps, and the initial context becomes too large to process efficiently.
3. Stateful Tool Use: When an agent uses multiple tools sequentially, this middleware ensures that only the outcomes and decisions from those tool calls are retained for future reasoning.