Persistent memory for AI agents — history, context, semantic search, queues. 17 tools, stdio/SSE.
Most knowledge and memory work still happens through a UI a human drives. Agenticmemory MCP server moves it into the conversation instead. Persistent memory for AI agents — history, context, semantic search, queues. 17 tools, stdio/SSE.
Your agent writes great code all day — then forgets every decision the moment its context window resets. So you become the memory layer: re-explaining the project, the preferences, what broke last time. Agentic Memory is the memory your agent runs itself: one install, and it stores, recalls, and searches its own state across sessions, machines, and even other agents. It can even sign itself up — one command returns a working API key, no browser, no human.
Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
The server publishes 10 tools. What each one is for:
Key — value context** — typed durable facts: decisions, preferences, runbooksLong — term entries** — titled, tagged knowledge that survives months, with auto-summarisationEntities — people and systems the agent should know aboutScratchpad — ephemeral working memory with TTLs (expiry is a feature)Bootstrap — full session context in one callQueues — FIFO agent bus with long-poll and dead-letter, agmry queue (exit 2 = empty)Multi — agent spaces** — a fleet of agents reads and writes one memoryEnd-to — end encryption** — zero-knowledge spaces where only you hold the key (agmry key generate)Export — full data takeout per space, one command (agmry space export)Multi-agent — Shared spaces across agentsConfiguration is passed through the environment: AGMRY_API_KEY, YOUR_API_KEY, AGMRY_ENCRYPTION_KEY, AGENTICMEMORY_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Agenticmemory's toolset — Key, Long, Entities and 7 more — is a fair guide to whether it matches your workflow. It is maintained by jyswee; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Agenticmemory's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Key | value context** — typed durable facts: decisions, preferences, runbooks |
| Long | term entries** — titled, tagged knowledge that survives months, with auto-summarisation |
| Entities | people and systems the agent should know about |
| Scratchpad | ephemeral working memory with TTLs (expiry is a feature) |
| Bootstrap | full session context in one call |
| Queues | FIFO agent bus with long-poll and dead-letter, agmry queue (exit 2 = empty) |
| Multi | agent spaces** — a fleet of agents reads and writes one memory |
| End-to | end encryption** — zero-knowledge spaces where only you hold the key (agmry key generate) |
| Export | full data takeout per space, one command (agmry space export) |
| Multi-agent | Shared spaces across agents |
{
"mcpServers": {
"agenticmemory": {
"command": "npx",
"args": ["-y", "agmry"],
"env": {
"AGMRY_API_KEY": "your-value",
"YOUR_API_KEY": "your-value",
"AGMRY_ENCRYPTION_KEY": "your-value",
"AGENTICMEMORY_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| AGMRY_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_API_KEY | Credential the server authenticates with. | Yes |
| AGMRY_ENCRYPTION_KEY | Credential the server authenticates with. | Yes |
| AGENTICMEMORY_API_KEY | Credential the server authenticates with. | Yes |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.