mcp-openmemory
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MEMORY_DB_PATH | No | Path to SQLite database file. Must be configured to a persistent location to avoid losing conversation history. | ./memory.sqlite |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| save_memoryB | Save individual conversation messages to memory storage. Use this when you want to persist important parts of our current conversation. Call this for each significant message or exchange that should be remembered for future conversations. Typically used during or at the end of conversations to store key information, decisions, or context. |
| recall_memory_abstractA | Retrieve the current memory abstract that summarizes past conversations and context. Use this at the beginning of conversations to understand what has been discussed before, or when you need to check existing memory context. This gives you the processed summary of previous interactions, not raw messages. Call this to "remember" previous conversations with this user. |
| update_memory_abstractA | Save a new or updated memory abstract after processing recent conversations. Use this when you have reviewed recent messages, combined them with existing memory context, and created an improved summary. The typical workflow is: 1) Get current memory abstract, 2) Get recent memories, 3) Process and combine them, 4) Save the updated abstract here. This maintains the evolving memory summary over time. |
| get_recent_memoriesA | Retrieve recent raw conversation messages from the last few days. Use this when you need to see actual conversation history rather than the processed summary. Helpful for creating or updating memory abstracts, or when you need specific details from recent exchanges. This gives you the unprocessed message data to work with. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| memory_summary | Generate a summary of stored memories for an agent |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Database Schema | SQLite database schema for memory storage |
| Memory Statistics | Statistics about stored memories and conversations |
TDQS
Scored across 4 tools
Each tool has a clear, distinct purpose: raw message retrieval, summary retrieval, individual message storage, and summary update. No overlap in functionality.
All tool names follow a consistent verb_noun pattern with lowercase snake_case (e.g., get_recent_memories, update_memory_abstract). No deviations.
With 4 tools, the server is well-scoped for memory management: covering retrieval of raw and summary data, saving individual messages, and updating the summary. Neither too many nor too few.
The toolset covers core read and write operations for both raw messages and summaries. Missing a delete operation, but the described workflow is complete for typical use.