Model Context Protocol + Cognee: LLM Memory Made Simple

On November 25, 2024, Anthropic introduced the Model Context Protocol (MCP)—an open standard designed to connect AI systems with data repositories, business tools, and development environments. Its aim is to link AI assistants securely and efficiently with various data sources and services so they can generate more relevant, context-aware responses.

Here at cognee, we share this vision. We’re dedicated to bridging large language models (LLMs) with robust data infrastructure—what’s often referred to as AI memory or LLM memory. With the new MCP specification, adding a powerful, context-aware memory layer to your AI systems has never been easier.

In this blog, we’ll tackle a few core questions like “What is MCP?” and “How can you use MCP with cognee’s pipelines to create context-aware LLMs for your needs?” You’ll also find a step-by-step guide on setting up the cognee MCP server, a demonstration of how it integrates with Anthropic’s Claude Desktop app, and insights into leveraging cognee’s AI memory to get the most out of your LLM.

Before diving into the details, let’s step back and take a look at the bigger picture of a modern LLM-driven application.

The Challenge: Lack of LLM Agent Memory

AI technologies have advanced rapidly over the past few years, but one major sticking point remains: context retention. If you’ve ever tried to build a sophisticated LLM-driven application without the right memory system, you’ve likely run into problems like:

1. Fragmented Data: Important documents, transcripts, and conversation histories are often scattered across multiple systems. Connecting them consistently to your LLM can involve custom integrations, intricate data pipelines, or even manual effort.
2. Hallucinations: Sometimes, LLMs generate made-up facts or references. This “hallucination” happens partly because the model doesn’t have a robust knowledge store—especially when handling domain-specific queries.
3. Scalability Issues: As your application grows, so does your data. Without a well-designed memory architecture, you risk facing skyrocketing compute costs and data bottlenecks.
4. Context Window Limitations: Traditional LLMs operate within a fixed context window. When you exceed those constraints, the model “forgets” previous information. That’s why AI memory is so important—it helps maintain state and data across multiple interactions, even over long periods.

In our previous blog, we described these challenges in detail, focusing on how short-term and long-term memory mechanisms might function in AI. The inevitable conclusion is that a robust memory AI architecture is key to building hallucination-proof and cost-effective LLM solutions.

Recap: Understanding LLM Memory and Its Importance

We know that LLMs excel at generating coherent text but they don’t inherently store knowledge across interactions the way humans do. They handle one input at a time, based on the immediate context window.

In contrast, robust memory solutions let AI recall relevant user data or information from knowledge bases—even after extended breaks. This approach has profound implications for:

• Personalization: Tailoring responses to user preferences accumulated over time.
• Accuracy: Reducing guesswork by anchoring answers in verified data.
• Efficiency: Minimizing repeated queries or the need to reprocess large datasets.
• Collaboration: Enabling multiple AI agents or microservices to share a common memory, which is essential for tackling complex, multi-step tasks.

In short, a strong memory foundation elevates an LLM from a novelty to an indispensable business tool. As we noted in our previous blog, even though it might not capture the full complexity of how AI can store and retrieve data, dividing memory into short-term and long-term compartments is a helpful analogy. Let’s revisit the long term memory once again.

How Long-Term Memory Works in LLMs

Typically, an LLM uses a “context window” to interpret prompts—but that window is temporary, disappearing once a response is generated.

Retrieval-Augmented Generation

Instead of relying only on its immediate context window, the model can tap into external data sources to find the most relevant information.

This process, called retrieval-augmented generation (RAG), allows the LLM to generate answers that are not just coherent but also grounded in real, up-to-date content.

When a user query comes in, an RAG-augmented LLM will:

1. Parse the query.
2. Retrieve relevant information from external memory (whether it’s a vector or a graph database).
3. Combine the retrieved data with the prompt.
4. Generate a final, data-grounded answer.

Data Storage Techniques

Efficient data retrieval hinges on how your information is stored and structured. Different storage techniques cater to different priorities, such as semantic similarity or logical connections among facts.

Here’s a quick overview of several key AI data storage methods:

• Vector Databases: They convert data into numerical embeddings for semantic search. When the LLM processes a new query, the system fetches only the most relevant vectors (chunks of knowledge) from the database.
• Graph Databases: They store knowledge as nodes (entities) and edges (their relationships), enabling more insight into the connections between concepts and facts. For instance, you can visualize how a certain product relates to user feedback or inventory logs.
• Hybrid Approach: It combines the strengths of both vectors and graphs, delivering semantic similarity (vector search) and structural context (graph search). This method significantly reduces hallucinations, as the model relies on verified, contextually enriched information. This is what cognee does best.

Memory-Augmented LLMs: Enhancing AI Retention

Now let’s see how it all comes together. A memory-augmented LLM fuses the raw generative power of large models with robust data retrieval and storage techniques, ensuring that every interaction builds on a cumulative understanding of past context.

What we get is more human-like recall that boosts both accuracy and personalization. Instead of repeating prompts or risking hallucinations, the model uses a persistent data layer to fetch exactly what it needs. The outcome is a more responsive and scalable AI platform capable of delivering context-rich, production-ready interactions.

Cognee builds on these concepts by using advanced data ingestion strategies to unify data from texts, PDFs, audio transcriptions, and more. By combining vector embeddings with graph structures, cognee helps LLMs see the “bigger picture,” uncovering insights that a single data structure alone might miss.

Breaking Down Anthropic’s Model Context Protocol (MCP)

Here’s what Anthropic had to say:

“The Model Context Protocol (MCP) is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses.”

Essentially, Anthropic is tackling the problem of fragmented data. Rather than building custom connectors for every data source—be it Google Drive, Slack, GitHub, or others—MCP standardizes the way AI applications interact with these systems. Developers have two options:

• Expose data through an MCP server (like the cognee MCP server).
• Build an AI app (an MCP client) that connects to those servers.

As the ecosystem matures, more data platforms will speak a common language like MCP, enabling AI solutions to pull context seamlessly from multiple sources.

Cognee + MCP: The Perfect AI Memory Duo

Here at cognee, we’re all about building AI memory systems that are:

1. Scalable: Capable of handling large, evolving datasets.
2. Modular: Able to integrate seamlessly with new or existing data sources.
3. Cost-Effective: Reducing developer workload, expensive reprocessing, and hallucinations.

By connecting to the cognee MCP server, you can:

• Automatically load data from multiple sources and repositories.
• Generate knowledge graphs to better understand underlying relationships.
• Search and retrieve domain-specific knowledge on demand during your LLM sessions.

This approach is a major leap forward from the old days of building one-off connectors for each system.

How to Get Started with the Cognee MCP Server

We’ve put together a short video demonstrating how easy it is to use our MCP server with Claude Desktop. You’ll also find a quick-start guide below, along with another use case example featuring Cline—a coding assistant.

Use Cases and Scenarios

1. Using Cognee with Claude Desktop

In this video, you can see how Claude Desktop uses cognee’s cognify tool to generate a knowledge graph from your connected data source. This graph supports more precise searches and answers by feeding the model structured, contextual knowledge. In practice, this drastically reduces hallucinations and ensures that you’re always working with the most relevant data.

Below is a step-by-step guide for this example. Once you complete these steps, the Claude Desktop app should automatically recognize the cognee MCP server and present you with cognee tool options. This means that every time you run a query in Claude, you can leverage cognee’s pipelines as MCP tools to generate more accurate, context-rich responses.

# 1. Clone the cognee repo
git clone https://github.com/topoteretes/cognee.git
cd cognee-mcp

# 2. Install dependencies
brew install uv
uv sync --reinstall

# 3. Add the new server to your Claude config using an editor of your choice (e.g., nano)
#   The file is usually located at:
nano ~/Library/Application\ Support/Claude/claude_desktop_config.json

#   If it doesn't exist, create it:

# 4. Example claude_desktop_config.json content:
{
    "mcpServers": {
        "cognee": {
            "command": "uv",
            "args": [
                "--directory",
                "/{cognee_root_path}/cognee-mcp",
                "run",
                "cognee"
            ],
            "env": {
                "ENV": "local",
                "TOKENIZERS_PARALLELISM": "false",
                "LLM_API_KEY": "sk-"
            }
        }
    }
}
# 5. Restart Claude Desktop to apply your changes

2. Using Cognee with Cline

Cline is an AI assistant for developers that can act as an MCP client, allowing you to interact with various services (including cognee) directly from your terminal. By configuring Cline to point to the cognee MCP server, you can easily use cognee’s tools. Connecting cognee’s codify tool (powered by our codegraph pipeline) to Cline via the MCP server creates a simple, API-like connection. This gives you robust, cost-friendly context management, resulting in a memory-augmented coding experience. Check out cognee’s Cline integration step by step in our documentation.

It’s also featured on PulseMCP, which shows how you can transform a Python codebase into a knowledge graph using cognee to map out relationships and dependencies. From there, you can seamlessly query the codebase for contextual insights.

Ready to Try Out? Let’s Get Started!

1. Clone cognee’s repo on GitHub

   Head over to our GitHub repository to grab the cognee MCP server code, and get everything you need to start experimenting.

2. Try the Streamlined Setup

   Whether you’re using Claude Desktop or Cline, our step-by-step instructions make installing and configuring the cognee MCP server straightforward.

3. Join Our Community

   Jump into our Discord to share your experiences, ask questions, and learn about the latest updates. We encourage you to discuss your use cases and any custom connectors or data flows you create—your insights help push forward the field of LLM memory management.

The Model Context Protocol offers a powerful new way to overcome the inherent limitations of large language models. By aligning your data pipelines with MCP—and combining that with cognee’s advanced memory solutions—you can build an efficient, cost-effective memory layer that scales with your data as your needs grow.

Happy building!

FAQ

1. What is Anthropic’s Model Context Protocol (MCP)?

   MCP is an open standard from Anthropic that streamlines how AI systems connect to various data sources. By standardizing these connections, MCP helps Large Language Models (LLMs) access up-to-date, context-rich information.

2. Can I integrate cognee with Anthropic’s Claude Desktop or Cline?

   Absolutely. Cognee offers an MCP-compatible server that connects seamlessly with Claude Desktop, Cline, or any MCP-ready client. This simplifies how developers manage context and helps create cost-effective, memory-augmented LLM applications.

3. How does cognee integrate with MCP?

   You can clone the cognee repository from GitHub, install dependencies, and configure your client (Claude Desktop, Cline, or something else) with just a few steps. Once set up, you’ll have a powerful, long-term memory system for your LLMs.

4. Is an AI memory solution so important for LLMs?

   Traditional LLMs can only “remember” what’s in their current context window. By pairing them with a robust AI memory solution like cognee, you enable long-term retention of user history, domain knowledge, and other critical data, leading to higher accuracy and personalization.

5. How does cognee improves LLM accuracy?

   Cognee provides an external “memory layer” for LLMs. It uses vector and graph databases to store critical context, enabling AI models to retrieve accurate, domain-specific data. This approach drastically cuts down on hallucinations and makes LLM responses more reliable.

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