JasonDocton/lucid-memory

Registry

Summary

Lucid Memory is a local, instant, and persistent memory system for AI agents. It is 13x faster than Pinecone and 5x leaner than RAG, finding information that RAG misses—all with zero cloud dependency and zero cost.

Key features

• Local-first: runs entirely on your machine with no cloud dependency
• 13x faster than Pinecone vector database
• 5x leaner than traditional RAG approaches
• Instant and persistent memory retrieval
• Finds information that RAG misses
• Zero cost to operate

Use cases

• AI agent memory for long-running conversations
• Local vector search for LLM applications
• Offline-first AI tools and assistants
• Memory augmentation for agentic AI workflows
• Cost-effective alternative to cloud vector databases

README excerpt

# Lucid Memory **2.7ms retrieval. 743,000 memories/second. $0/query.** Memory for AI that works like yours—local, instant, persistent. ```bash curl -fsSL https://lucidmemory.dev/install | bash ``` <div align="center"> _{Works with Claude Code, OpenAI Codex & OpenCode · macOS & Linux · <a href="#windows">Windows instructions</a>} <br><br> </div> **New in 0.6.0:** <a href="#memory-consolidation">Memory Consolidation</a> — Lucid Memory is now self-maintaining. Background consolidation strengthens recent memories, decays stale ones, prunes weak associations, and manages visual memory lifecycle. New memories are checked against existing traces — similar content reinforces or updates rather than duplicating. 307 tests, 0 tsc errors. **New in 0.5.0:** <a href="#episodic-memory">Episodic Memory</a> — Claude remembers not just what happened, but how it unfolded — reconstructing the story of your debugging session, not just the fix. "What was I working on before the auth refactor?" now has an answer. --- ## Why Lucid Memory? **We're not a vector database. We're the retrieval layer that makes vector databases obsolete for AI memory.** Pinecone stores vectors. We understand context. <table> <tr> <th></th> <th>Lucid Memory</th> <th>Claude-mem</th> <th>Pinecone RAG</th> <th>Traditional RAG</th> </tr> <tr> <td><b>Retrieval Speed</b></td> <td>✅ <b>2.7ms</b></td> <td>~50ms</td> <td>10-50ms</td> <td>200-800ms</td> </tr> <tr> <td><b>Token Efficiency</b></td> <td>✅ <b>5x</b></td> <td>1x (baseline)</td> <td>2.5x</td> <td>~2x</td> </tr> <tr> <td><b>Recall @ Fixed Budget</b></td> <td>✅ <b>82.5%</b></td> <td>28.9%</td> <td>55.3%</td> <td>~50%</td> </tr> <tr> <td><b>Storage Compression</b></td> <td>✅ <b>5x (80% smaller)</b></td> <td>1x</td> <td>1x</td> <td>1x</td> </tr> <tr>

Topics