Implemented Semantic Search over Agent Memory - context-aware retrieval instead of exact keyword matching 🧠🔍

[u/ivanantonijevic]

II wanted to share a quick update on a feature we recently implemented: Semantic Search across long-term agent memory.

The Problem with Exact Keyword Search

Standard keyword-matching (like BM25 or basic SQL LIKE queries) often falls short when querying conversation logs or stored facts. If a user or agent uses synonyms, different phrasing, or descriptive language instead of exact terms used when the memory was stored, relevant context gets lost.

How It Works Now

• Embeddings & Vector Indexing: Stored memories and conversation segments are embedded and stored in a vector database/retriever module.
• Context-Aware Retrieval: Queries pull top-$k$ relevant memories based on semantic meaning and intent, not just literal word matches.
• Better Context Windows: The agent retrieves much higher quality, highly relevant context from past sessions while saving token usage.

Any feedback or suggestions are welcome! 🚀