mcp-local-memory
MCP ServerFreeLightweight local memory for your AI agent. SQLite + embeddings, zero setup, no services to run. Minimal config: ``` { "mcpServers": { "memory": { "command": "npx", "args": ["-y", "mcp-local-memory"] } } } ``` Your agent remembers preferences, project details, procedures --
Capabilities3 decomposed
local memory storage with sqlite and embeddings
Medium confidenceThis capability utilizes SQLite as a lightweight database to store user preferences, project details, and procedures, while embeddings are used to enhance the retrieval of contextually relevant information. The architecture allows for zero setup, enabling users to run the memory server with minimal configuration through a simple command. This approach distinguishes it from other memory solutions by providing a fully local and self-contained environment without the need for external services.
Combines SQLite for persistent storage with embeddings for contextual retrieval, all in a zero-setup environment.
More user-friendly than traditional memory solutions because it requires no external services or complex configurations.
contextual retrieval of stored information
Medium confidenceThis capability allows the AI agent to retrieve previously stored information based on context using embeddings. By leveraging vector representations of data, the system can efficiently match user queries with relevant stored memories, enhancing the agent's ability to provide personalized responses. This implementation is distinct as it operates entirely locally, ensuring data privacy and quick access without network latency.
Utilizes embeddings for context-aware retrieval, enabling more relevant responses compared to traditional keyword-based searches.
Faster and more relevant than keyword-based retrieval systems because it leverages semantic understanding through embeddings.
dynamic memory configuration via prompts
Medium confidenceThis capability allows users to control the structure and behavior of the memory system through dynamic prompts. By defining how data should be stored and retrieved via user-defined instructions, the system adapts to various use cases without requiring code changes. This flexibility is a key differentiator, as it enables users to customize their memory management on the fly.
Enables real-time customization of memory behavior through prompts, allowing for flexible and user-driven memory management.
More adaptable than static memory systems, as it allows users to modify behavior without redeployment.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Memory Box MCP Server
Save, search, and format memories with semantic understanding. Enhance your memory management by leveraging advanced semantic search capabilities directly from Cline. Organize and retrieve your memories efficiently with structured formatting and detailed context.
Best For
- ✓developers looking for a lightweight memory solution for AI agents
- ✓AI developers building agents that require personalized memory
- ✓developers needing customizable memory solutions for AI agents
Known Limitations
- ⚠Limited to local storage; does not support cloud synchronization
- ⚠Performance may degrade with very large datasets due to SQLite's limitations
- ⚠Accuracy of retrieval may decrease if embeddings are not well-tuned
- ⚠Limited to the scope of data stored in SQLite
- ⚠Complex configurations may lead to user errors
- ⚠Prompt-based configurations may require user familiarity with the system
Requirements
Input / Output
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Repository Details
About
Lightweight local memory for your AI agent. SQLite + embeddings, zero setup, no services to run. Minimal config: ``` { "mcpServers": { "memory": { "command": "npx", "args": ["-y", "mcp-local-memory"] } } } ``` Your agent remembers preferences, project details, procedures -- whatever you want. All structure and behavior controlled via prompts. For details: [https://github.com/NickSmet/mcp-local-memory](https://github.com/NickSmet/mcp-local-memory)
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