Memory engine for AI agents. Two axes: **time** (three-layer decay & promotion) and **space** (self-organizing topic tree). Important memories get
Connect Engram Rs to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Memory engine for AI agents. Two axes: time (three-layer decay & promotion) and space (self-organizing topic tree). Important memories get promoted, noise fades, related knowledge clusters automatically. The engram rs mcp server is what makes that connection.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Triggers — Tag a memory with trigger:deploy, and the agent can recall all deployment lessons before executing:engram-rs-mcp on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: ENGRAM_LLM_URL, ENGRAM_LLM_KEY, ENGRAM_LLM_PROVIDER. 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. Engram Rs's toolset — Triggers — is a fair guide to whether it matches your workflow. It is maintained by kael-bit; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Triggers | Tag a memory with trigger:deploy, and the agent can recall all deployment lessons before executing: |
{
"mcpServers": {
"engram-rs": {
"command": "npx",
"args": ["-y", "engram-rs-mcp"],
"env": {
"ENGRAM_LLM_URL": "your-value",
"ENGRAM_LLM_KEY": "your-value",
"ENGRAM_LLM_PROVIDER": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| ENGRAM_LLM_URL | Endpoint or connection string the server talks to. | Yes |
| ENGRAM_LLM_KEY | Credential the server authenticates with. | Yes |
| ENGRAM_LLM_PROVIDER | Configuration value read at startup. | Optional |
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.