Memori MCP server — persistent AI memory with recall and augmentation tools
Memori MCP server — persistent AI memory with recall and augmentation tools
Memori turns stateless agents into stateful systems by providing structured, persistent memory that works across sessions and workflows.
entity_id to identify the end user (e.g. user_123) - An optional process_id to identify the agent or workflow (e.g. my_agent) Export these in your shell or replace the placeholders directly in your config: ---Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| memori_recall | Start of each user turn |
| memori_recall_summary | Session starts, daily briefs, status updates, project overviews |
| memori_compaction | After context compaction |
| memori_advanced_augmentation | After composing a response |
| memori_feedback | When the user flags a memory issue or praises a result |
| memori_signup | When the user explicitly asks and provides an email |
| memori_quota | When the user asks about usage or quota errors appear |
entity_id to identify the end user (e.g. user_123) - An optional process_id to identify the agent or workflow (e.g. my_agent) Export these in your shell or replace the placeholders directly in your config: ---| Variable | Description | Required |
|---|---|---|
| MEMORI_API_KEY | Credential the server authenticates with. | Yes |
| MEMORI_ENTITY_ID | Configuration value read at startup. | Optional |
| MEMORI_PROCESS_ID | Configuration value read at startup. | Optional |
Store entities, relations and observations in a local knowledge graph with the reference Memory MCP server.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
A Model Context Protocol (MCP) server implementation powered by txtai, providing semantic search, knowledge graph capabilities, and AI-driven text
HexDocs MCP is a project that provides semantic search capabilities for Hex package documentation, designed specifically for AI applications. It consists of two main components:
Self-hosted shared-memory & coordination mesh for AI agent fleets. You host it; agents connect.
Persistent, consensus-validated institutional memory for AI agents. Runs locally.