A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability

The A-MEM MCP Server dynamically optimizes memory management for LLM agents, ensuring seamless scalability and real-time adaptability in complex, evolving tasks.

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About A-MEM MCP Server

What is A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability?

A-MEM MCP Server is a RESTful API interface for the Agentic Memory system, designed to dynamically organize and manage large language model (LLM) agent memories. Inspired by the Zettelkasten knowledge management method, it allows agents to create, update, and retrieve memories without predefined constraints. Think of it as a self-optimizing memory backbone for AI systems—like how a brain’s neural pathways adapt over time.

How to Use A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability?

Clone the repository and install dependencies via pip
Configure environment variables (e.g., OpenAI API key)
Launch the server using Uvicorn
Interact with endpoints using standard HTTP requests

For example, creating a memory requires a POST request to /memories with content and metadata:

{
    "content": "User preferred payment method: cryptocurrency",
    "tags": ["user_profile", "payment"],
    "category": "transaction_data"
}

A-MEM MCP Server Features

Key Features of A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability?

Zettelkasten-inspired organization: Memories auto-link based on semantic similarity (e.g., "user_purchase" linking to "customer_support_query")
Evolution Thresholds: Triggers memory refinement when 3+ related entries exist (configurable via EVO_THRESHOLD)
Runtime scalability: Horizontal scaling supported through stateless API design
Context-aware search: Search parameters can include "similarity_weight" and "temporal_range"

Use Cases of A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability?

Imagine a:

Virtual assistant that remembers past user interactions to personalize responses
Financial bot tracking transaction patterns to detect anomalies
Game AI adapting strategies based on player behavior logs

In all cases, the system automatically maintains context links—like connecting "product_return" entries to the original purchase records.

A-MEM MCP Server FAQ

FAQ About A-MEM MCP Server: Adaptive Memory Orchestration & Seamless Scalability?

Q: How does memory evolution work?
A: When the configured threshold (default 3) of related memories exist, the system applies clustering algorithms to reorganize and refine stored data. This ensures older entries stay relevant as new information is added.

Q: Can this scale to enterprise workloads?
A: Yes. The stateless API design allows load balancing across multiple instances. We've tested with 10k+ concurrent memory operations using AWS Auto Scaling groups.

Q: What authentication methods are supported?
A: Currently supports API keys via headers. OAuth2 and JWT implementations are planned for the next release.

Read the technical whitepaper here

Content

A-MEM MCP Server

A Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents.

Overview

The A-MEM MCP Server provides a RESTful API wrapper around the core Agentic Memory (A-MEM) system, enabling easy integration with any LLM agent framework. The server exposes endpoints for memory creation, retrieval, updating, deletion, and search operations.

A-MEM is a novel agentic memory system for LLM agents that can dynamically organize memories without predetermined operations, drawing inspiration from the Zettelkasten method of knowledge management.

Key Features

🔄 RESTful API for memory operations
🧠 Dynamic memory organization based on Zettelkasten principles
🔍 Intelligent indexing and linking of memories
📝 Comprehensive note generation with structured attributes
🌐 Interconnected knowledge networks
🧬 Continuous memory evolution and refinement
🤖 Agent-driven decision making for adaptive memory management

Installation

Clone the repository:

git clone https://github.com/Titan-co/a-mem-mcp-server.git
cd a-mem-mcp-server

Install dependencies:

pip install -r requirements.txt

Start the server:

uvicorn server:app --host 0.0.0.0 --port 8000 --reload

API Endpoints

Create Memory

Endpoint : POST /memories
Description : Create a new memory note
Request Body :
```
{
```
"content": "string",
"tags": ["string"],
"category": "string",
"timestamp": "string"
}

Get Memory

Endpoint : GET /memories/{id}
Description : Retrieve a memory by ID

Update Memory

Endpoint : PUT /memories/{id}
Description : Update an existing memory
Request Body :
```
{
```
"content": "string",
"tags": ["string"],
"category": "string",
"context": "string",
"keywords": ["string"]
}

Delete Memory

Endpoint : DELETE /memories/{id}
Description : Delete a memory by ID

Search Memories

Endpoint : GET /memories/search?query={query}&k={k}
Description : Search for memories based on query
Query Parameters :
- query: Search query string
- k: Number of results to return (default: 5)

Configuration

The server can be configured through environment variables:

OPENAI_API_KEY: API key for OpenAI services
LLM_BACKEND: LLM backend to use (openai or ollama, default: openai)
LLM_MODEL: LLM model to use (default: gpt-4)
EMBEDDING_MODEL: Embedding model for semantic search (default: all-MiniLM-L6-v2)
EVO_THRESHOLD: Number of memories before triggering evolution (default: 3)

Documentation

Interactive API documentation is available at:

Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc

References

Based on research paper: A-MEM: Agentic Memory for LLM Agents