Claw-Recall: Building a Persistent Memory System for AI Agents

Claw-Recall is an open-source project focused on dialogue memory storage and retrieval for AI Agents. It addresses memory loss issues in multi-agent workflows through persistent context management, enhancing the continuous interaction capabilities of intelligent agents.

When building complex AI Agent systems, we face a fundamental challenge: Agents lack long-term memory capabilities. While current LLM-driven Agents possess strong reasoning and tool-using abilities, their "memory" is limited to the current context window. Once a session ends or the system restarts, all accumulated dialogue history, learned preferences, and established knowledge associations are lost.

This "goldfish-like" memory characteristic severely restricts Agents' performance in the following scenarios:

• Long-term task execution: Complex projects that require continuous follow-up over days or even weeks
• Personalized services: Service-oriented Agents that need to remember user preferences and historical interactions
• Multi-agent collaboration: Scenarios where different Agents need to share context and knowledge states
• Failure recovery: Seamless resumption of work status after system interruptions

The Claw-Recall project was born to address this pain point. It provides a complete dialogue memory storage and retrieval solution, enabling Agents to truly have the ability to "recall".

Claw-Recall's design is based on three core insights:

Traditional logging methods simply save dialogue text, which is both storage-intensive and difficult for effective retrieval. Claw-Recall adopts a semantic extraction strategy to automatically identify key information in dialogues:

• Entity extraction: Recognize key entities such as names, locations, projects, and concepts
• Relationship modeling: Record relationships and interaction history between entities
• Intent recognition: Understand the true intent behind user requests
• Emotion tagging: Track emotional changes and user satisfaction in dialogues

Drawing on the hierarchical structure of human memory, Claw-Recall implements a three-level memory system:

• Working Memory: Complete context of the current session, supporting fine-grained retrieval
• Short-term Memory: Summaries and key information from the latest N sessions
• Long-term Memory: Compressed and summarized historical knowledge base

This hierarchical architecture ensures real-time response speed while enabling effective management of massive historical data.

When an Agent needs to recall relevant information, Claw-Recall provides multiple retrieval modes:

• Semantic similarity search: Similar dialogue retrieval based on vector embeddings
• Timeline backtracking: Reconstruct interaction history of a specific period in chronological order
• Entity association query: Track all interactions related to a specific entity
• Pattern matching: Identify recurring scenarios and solutions

Claw-Recall adopts a hybrid storage strategy:

• Vector database: Stores semantic embeddings of dialogues, supporting similarity retrieval (integrates Pinecone, Milvus, Qdrant by default)
• Graph database: Maintains entity relationship networks, supporting complex association queries (supports Neo4j, Amazon Neptune)
• Time-series database: Records time-series features of dialogues, supporting trend analysis
• Object storage: Saves raw dialogue records and large-volume attachments

To address the storage bloat issue of long-term memory, the system implements an intelligent compression mechanism:

• Summary generation: Use lightweight models to compress long dialogues into key points
• Deduplication and merging: Identify and merge similar or duplicate memory fragments
• Importance scoring: Automatically clean up low-value memories based on access frequency and timeliness
• Hierarchical encoding: Store multiple copies of important information and archive secondary information in single copies