agimem

Stop re-explaining your codebase, your conventions, and yesterday's work to every new chat. Context survives the session, the project, and the tool.

Free, no credit card. Setup in under a minute.

One agent writes a memory into a Capsule and a different agent recalls it later, shown across three scenarios: An incident you only debug once, A convention every agent follows, A quirk of your setup.

Index every new user lookup field before shipping. The last unindexed one caused a production rollback.

- database/indexed-lookups
- style/commit-messages
- ci/pnpm-catalog

This change adds a lookup field, so I've included the index — per the earlier rollback.

An isolated key-value store. One per agent, project, or use case.

Each Capsule gets its own key. Share it only with the agent that needs access.

Point any MCP client at the Capsule, or call the REST API.

Every Capsule is its own key-value store with its own API key. Rotate or revoke from the dashboard.

Every read and write is timestamped and traceable.

Use the open protocol, or call plain HTTP from anything else.

Agents can search stored memories, group them with tags, and set a TTL on anything that shouldn't outlive the task.

Every memory makes the next agent smarter

Three agents, one week, one Capsule. Watch a memory get written, recalled, and reused.

Watch agents build shared memory across a week

Cursor saves preferences → Claude reads them → Windsurf avoids a known bug

Structure, naming, and preferred libraries live in memory, so agents start with your rules instead of guessing.

“Always use Drizzle, never Prisma. Server actions live in lib/.”

Architecture notes and environment quirks persist, so a new agent is useful without rediscovering context.

“Monorepo with pnpm workspaces. Auth in Clerk. Deploy on Vercel.”

Save tone and formatting rules once and reuse them without repeating the same setup prompt.

“Concise, no emojis, prefer bullets over paragraphs.”

Checkpoints and outputs persist between runs, so long automations resume instead of starting over.

“Last row processed: 4,281. Retry queue: empty.”

Questions developers ask about MCP memory

An MCP memory server lets AI agents store and retrieve persistent context through the Model Context Protocol. Instead of resetting each session, agents can remember decisions, conventions, and project state. agimem also offers a REST API for agents and tools that don't support MCP.

Which AI tools can I use with agimem?▾ agimem works with MCP-compatible clients including Claude, Cursor, Windsurf, and VS Code. For tools without MCP support, use the REST API directly.

How quickly can I set it up?▾ Most users can connect their first agent in under a minute. Create a Capsule, generate an API key, and paste one setup prompt.

Yes. You can point multiple agents to the same Capsule so they share context, or isolate each agent with separate Capsules for stricter boundaries.

Yes. You can sign up and create your first Capsule without a credit card.

Two-layer memory for AI agents with an immune system — episodes compress into identity through citation-validated graduation. Zero dependencies, 5 MCP tools.

A production-ready Model Context Protocol (MCP) server that provides a powerful, vector-native memory bank for AI agents. Built with the Protocol-Lattice Go Agent Framework, this server offers persistent, searchable, and shareable memory with multiple database backends.

Mnemex is a Python MCP server that provides AI assistants with human-like memory dynamics through temporal decay and natural spaced repetition, storing memories locally in human-readable JSONL and Markdown formats.

Persistent cross-agent semantic memory for AI agents. Recall past sessions, share knowledge across agents. Multi-agent (isolated writes, shared reads), local-first (SQLite + FTS5), works with any LLM — local Ollama at $0 or cloud APIs like Gemini and OpenAI. Integrations for Claude Code, Claude Desktop, and OpenClaw.

The media memory layer for AI agents and their humans. Track books, movies, music, shows, and anime.

Engram is a hosted MCP server that provides reliable memory for AI agents:

Agent memory with git-like version control. Custom LLMs turn conversations into structured facts with automatic conflict detection - your agent sees how decisions evolved, not four contradictory text blobs. 80% token reduction vs RAG/graph systems. MCP + REST.

Persistent memory for AI assistants and coding agents across ChatGPT, Claude, Cursor, and other MCP-compatible tools.

Local Work Model for AI agents that learns from real outcomes.