Home/Agentic AI/Memory Consolidation/Summarization

Memory Consolidation

Master how AI agents consolidate short-term memories into efficient long-term knowledge bases

Your Progress

0 / 5 completed
1
2
3
4
5

Memory Summarization

Once memories are clustered, we consolidate each cluster into a concise summary. This step combines multiple related memories into single, dense knowledge entries—reducing storage, improving search quality, and preserving context.

Interactive: Summarization Demo

Cluster: Technical Preferences (5 memories)
1. User is ML engineer at Google
2. User prefers Python over Java
3. User uses TensorFlow for projects
4. User mentioned experience with PyTorch
5. User discussed gradient descent optimization

🔄 Summarization Approaches

📄

Extractive

Select most important sentences from memories.

• Fast and deterministic
• Uses original wording
• May be less fluent
🤖

Abstractive (LLM)

Generate new concise summary with LLM.

• More fluent and natural
• Can infer relationships
• Requires LLM call

💡 Summarization Best Practices

•
LLM Prompt Template: "Summarize these related memories into a single, dense paragraph. Preserve all key facts and relationships. Memories: [list]. Output: One concise summary."
•
Maintain Traceability: Store source memory IDs with each summary. If the agent needs to verify or expand, it can reference originals. Example metadata: source_ids: [12, 45, 78]
•
Compression Ratios: Typical ratios are 5:1 to 10:1 (5-10 memories → 1 summary). Higher compression loses detail; lower compression wastes storage. Balance based on use case.
•
Re-Embedding: After generating summary, compute new embedding vector for the consolidated memory. Store this in your vector database for future retrieval—it represents the entire cluster.

🎯 The Final Pipeline

1Score: Assign importance scores, filter below threshold
2Cluster: Group similar memories using embeddings
3Summarize: Consolidate each cluster into concise summary
4Store: Save summaries with metadata and new embeddings

This pipeline transforms 100 scattered short-term memories into 10-15 organized, searchable knowledge entries—dramatically improving agent intelligence while reducing costs.

← Prev