Agent Memory Compressor: Intelligent Memory Compression for Long-Running LLM Agents

Overview of Agent Memory Compressor

Agent Memory Compressor is a Python library designed to address the issue of context window exhaustion in long-running language model (LLM) agents. It provides an intelligent memory pipeline that scores each piece of history by importance, compresses the least valuable entries, and assembles a token-bounded context that retains essential information such as decisions, discovered facts, and recent turns.

Key Components

1. Memory Store

   • A data structure to store messages exchanged between the agent and users.

2. Importance Scorer

   • Evaluates each message based on its importance using a multi-signal scoring mechanism.

3. Compression Strategies

   • Three strategies in CompressionEngine that compress less important entries while preserving critical information.

4. Forgetting Curve Triggers

   • Mechanisms to trigger compression based on either the number of turns or token usage thresholds.

5. Context Builder

   • Assembles a context from the compressed memory store, ensuring it fits within a specified token budget.

6. Session Adapter

   • Integrates the compressor directly into an existing agent session by projecting messages and metadata to/from the MemoryStore.

Implementation Details

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