Add Persistent Memory to Your AI Agent in 5 Minutes

Your AI forgets everything. Every conversation starts from scratch. Every preference explained again. Every context rebuilt.

What if it didn't have to?

The Problem

I've been building AI agents for the past year, and the biggest limitation isn't the model—it's the amnesia. Context windows are just short-term memory. When the conversation ends, everything disappears.

Sure, you can stuff conversation history into the prompt. But that burns tokens, hits context limits, and doesn't scale.

The Solution: Semantic Memory

I built Remembra to solve this. It's an open-source memory layer that gives your AI persistent, semantic memory.

Here's what "semantic" means: instead of storing raw text, it understands meaning. Ask "what does the user prefer?" and it retrieves relevant memories even if you never used the word "prefer."

Quick Start (Actually 5 Minutes)

1. Run Remembra (One Command)

docker run -d -p 8080:8080 remembra/remembra:latest

That's it. No database setup. No configuration. Just works.

2. Install the Python SDK

pip install remembra

3. Store and Recall Memories

from remembra import RemembraClient

client = RemembraClient("http://localhost:8080")

# Store a memory
client.store("User prefers TypeScript over JavaScript")
client.store("User works at Acme Corp on the payments team")
client.store("User's deadline is March 15th")

# Recall relevant memories
memories = client.recall("What programming language should I use?")
# Returns: "User prefers TypeScript over JavaScript"

memories = client.recall("When is the project due?")
# Returns: "User's deadline is March 15th"

The magic: you don't need exact keyword matches. Semantic search finds what's relevant.

What Makes This Different?

Entity Extraction: Remembra automatically identifies people, organizations, dates, and concepts. Query "memories about Acme Corp" and get everything related.

Conflict Resolution: If you store contradictory memories, Remembra handles it intelligently—newer facts can override older ones.

Privacy First: Self-hosted means your data never leaves your infrastructure. Built-in PII detection flags sensitive information.

100% on LoCoMo: The standard benchmark for memory retrieval. We score 100%. The VC-backed alternatives? 66%.

Use Cases

• Coding Assistants: AI that remembers your codebase patterns, past decisions, what broke last time
• Customer Support: Bots that actually know your history
• Personal AI: Assistants that learn your preferences over time
• Multi-Session Agents: Any agent that needs context across conversations

MCP Integration (Claude Desktop/Cursor)

If you use Claude Desktop or Cursor, we have an MCP server:

{
  "mcpServers": {
    "remembra": {
      "command": "uvx",
      "args": ["remembra-mcp"]
    }
  }
}

Now Claude remembers everything across sessions.

It's Free and Open Source

MIT licensed. Self-host forever at no cost. We also have a managed cloud option at remembra.dev if you don't want to run infrastructure.

GitHub: https://github.com/remembra-ai/remembra
Docs: https://docs.remembra.dev
Discord: https://discord.gg/Bzv3JshRa3

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Launching officially March 11th on Product Hunt, but the code is live now. Try it, break it, tell me what sucks.

What are you building that needs memory? Drop a comment—I'd love to hear your use case.