storeLABSCO SUMMARY
The lifecycle is the differentiator, not the search.
Plenty of memory servers embed and retrieve. Here a memory has a state that moves: confidence rises when something else corroborates it, falls when it does not, and unused memories fade. That is what stops a store filling with things that were true once. Note the split in memory types — a procedure or a script reference is static and should not decay, and choosing the wrong type is the mistake that will make recall behave oddly.
What it is
A managed memory service reachable over MCP or REST. Stored memories are embedded, mined for entities and relationships, and retrieved by four strategies at once — vector similarity, temporal recency, keyword search and graph traversal — with the system learning which to weight for each kind of query.
What you get
storesaves a memory, generating embeddings and extracting entities automaticallyrecallsearches with multi-strategy retrieval and cognitive scoring, ranked by an activation model drawn from cognitive science that factors in recency, access frequency, entity connections and validated confidenceupdatemodifies a memory and re-embeds it if the content changed;forgetremoves one by id or by closest match to a querygraphtraverses entity relationships by name and depthstatusreports memory counts, confidence distribution and detected knowledge gaps;snapshotgives a formatted overview organised by type- Memory types change how a memory is managed:
general_knowledgeanduser_knowledgeevolve, gaining and losing confidence over a lifecycle, whilecallable_scripts,work_project,cross_referenceandlearned_procedureare static reference - Every action is also an HTTP endpoint, so the same store is reachable from code that is not an MCP client
Requirements
An account at adaptiverecall.com, which issues a server URL and an API key; the free tier is enough to start. Add it as an HTTP MCP server with the key in an Authorization bearer header — Claude Code, Codex, Cursor and Gemini CLI configurations are all documented. The registry entry names ADAPTIVE_RECALL_API_KEY as a required secret and the npm package adaptive-recall, version 1.0.1.
Setup effort
Get your endpoint, then paste it — add your endpoint to your client
USE CASES
Giving an assistant memory across tools
One hosted store reachable from every MCP client you use, rather than a separate memory per application.
Answering a question that needs connections, not similarity
Traverse the entity graph by name and depth instead of hoping vector search surfaces the related item.
Finding out what the store does not know
Read the status report for confidence distribution and detected knowledge gaps.
Correcting something that changed
Update the memory and let it re-embed, or forget it by describing it rather than looking up its id.
8 tools
Listed in project docs. Your client may show a different list.
recallupdateforgetgraphstatussnapshotfeedbackBEFORE YOU INSTALL
What to know first
Tools you get
8 tools named in the project's documentation.
Requires
A client that can reach a remote MCP server over HTTP
INSTALLATION
Connect your client
MCP Clients
Claude Code
{
"mcpServers": {
"adaptive-recall": {
"type": "http",
"url": "https://<YOUR_SERVER_URL>/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_KEY>"
}
}
}
}Cursor
{
"mcpServers": {
"adaptive-recall": {
"url": "https://<YOUR_SERVER_URL>/mcp",
"headers": {
"Authorization": "Bearer <YOUR_API_KEY>"
}
}
}
}