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AMP (Agent Memory Protocol)

A persistent local memory server for AI agents that provides episodic and semantic storage with a visual dashboard. It allows agents to retain context across sessions using a 3-layer brain architecture comprising short-term, long-term, and graph-based memory.

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Updated
Dec 13, 2025
Validated
Jan 11, 2026

AMP: The Agent Memory Protocol 🧠

The Open Standard for Agentic Memory.

License: MIT Python 3.10+ MCP PyPI


The (Short) Story

I was tired of building AI agents that forgot everything the moment I closed the terminal.

RAG (Retrieval Augmented Generation) is great for documents, but terrible for experience. It chunks text blindly, losing the narrative. When I asked my agents "Why did we decide this yesterday?", they gave me hallucinated nonsense.

So I built AMP. It's not just a database; it's a Hippocampus for your agents. It mimics the human brain's distinction between Working Memory (Short-term context) and episodic Long-Term Memory, giving your agents a continuous, evolving sense of self.

Why developers are switching to AMP?

🌌 Galaxy View (Visualization)

Don't just guess what your agent knows. See it. AMP comes with a stunning, 60fps local dashboard. Watch memories form constellations in real-time. Nodes cluster by semantic meaningβ€”if two ideas are related, they physically move together.

Galaxy View


πŸ•ΈοΈ Force Mode (Physics)

Toggle to Force Mode to see the topological connections between your memories. It uses a physics simulation (D3.js) to show you how different memory clusters are "pulled" together by shared context.

Force Mode


πŸ” Semantic Query

Stop guessing keywords. Query your agent's memory using natural language. I built a dedicated interface that not only finds relevant memories but shows you the Relevance Score (0-100%) so you know exactly why a memory was retrieved.

Semantic Query


πŸ”Œ MCP Native (Plug & Play)

Built from day one for the Model Context Protocol.

  • Claude Desktop: Add AMP to your config, and Claude remembers you forever.
  • Cursor: Give your coding assistant persistent context of your project history.

🧠 The "3-Layer" Brain

I don't just dump text into a vector store. I structure it:

  1. ⚑ STM (Short Term): High-fidelity buffer. "What are we doing right now?"
  2. πŸ“š LTM (Long Term): Consolidated insights. "What did we learn last week?"
  3. πŸ•ΈοΈ Graph: Connections between entities. "How is function A related to bug B?"

πŸ† Best-in-Class Recall

I benchmarked AMP against the leading competitor (Mem0) on the complex LoCoMo dataset. The results weren't close.

SystemLLM Recall AccuracyWhy?
AMP81.6% πŸš€Context-First. Preserves the narrative.
Mem021.7%Extraction-First. Aggressive summarization loses detail.

Quick Setup (30 seconds)

1. Install via uv (Recommended)

# Install the tool
uv tool install amp-memory

# Start the brain
amp serve

2. Or, Install via pip

pip install amp-memory
amp serve

3. Open the Dashboard

Visit http://localhost:8000. The interface is Galaxy Mode by default. Switch to Force Mode to see physics-based connections.


4. Connect to IDEs & Tools

AMP works native with Antigravity, Cursor, VS Code Copilot, and Claude Desktop. Add this to your MCP configuration file (usually mcp_config.json or claude_desktop_config.json):

{
  "mcpServers": {
    "amp-memory": {
      "command": "uv",
      "args": ["tool", "run", "amp-memory", "serve"],
      "env": {
        "PYTHONPATH": "."
      }
    }
  }
}

Now you can say:

"@amp remember that I am refactoring the login controller." "@amp what was the last bug we fixed?"

It knows.


Roadmap πŸ—ΊοΈ

  • Galaxy View: Visual Semantic Space.
  • Graph API: D3.js powered visualization.
  • Semantic Search: Vector-based relevance sorting.
  • Cloud Sync: Sync memories across devices.
  • Multi-Agent Swarm: Shared memory for agent teams.

Star History

Star History Chart


Made with ❀️ by Akshay.

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