Adaptive memory system for AI applications. Patent pending.
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Adaptive Recall is a hosted memory server that stores, retrieves, and manages long-term memory for AI applications. It connects via MCP or REST API.
Multi-strategy retrieval: four search strategies run in parallel (vector similarity, temporal recency, full-text keyword, knowledge graph traversal) and the system learns which to prioritize for each type of query
Cognitive scoring: results ranked using ACT-R activation modeling from cognitive science, factoring in recency, access frequency, entity connections, and validated confidence
Knowledge graph: entities and relationships extracted automatically from stored memories, used as a retrieval pathway alongside text similarity
Memory lifecycle: memories progress through stages, gain or lose confidence based on corroborating evidence, and fade naturally when unused
Self-improving: ML models train on your usage patterns, every parameter change must pass statistical validation against real query history before being adopted
Retrieval quality monitoring: the system verifies its own retrieval consistency and identifies knowledge gaps
Related MCP server: genesys-memory
Sign up at adaptiverecall.com to get your server URL and API key.
Add to your MCP client config (Claude Code, Codex, Cursor, or any MCP-compatible tool):
{
"mcpServers": {
"adaptive-recall": {
"type": "url",
"url": "https://YOUR_SERVER_URL/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}For Claude Code, add this to .mcp.json in your project or ~/.claude/settings.json for global access. For Gemini CLI, add to ~/.gemini/settings.json using httpUrl instead of url. For Codex, add to your Codex MCP configuration.
Every action is also available as an HTTP endpoint at https://YOUR_SERVER_URL/v1/. All requests require a Bearer token in the Authorization header.
Action | Description |
store | Save a new memory. Generates embeddings and extracts entities automatically. |
recall | Search memories using multi-strategy retrieval with cognitive scoring. |
update | Modify an existing memory. Re-embeds automatically if content changes. |
forget | Remove a memory by ID or by finding the closest match to a query. |
graph | Explore the knowledge graph, traversing entity relationships by name and depth. |
status | System health, memory counts, confidence distribution, and knowledge gap detection. |
snapshot | Get a formatted overview of stored memories, organized by type. |
feedback | Send feedback directly to the Adaptive Recall developers. |
When storing memories, assign a type that affects how the memory is managed:
Learning types (evolve over time, gain/lose confidence, have lifecycle stages):
general_knowledge - facts, observations, reference information
user_knowledge - information about people and their preferences
Lookup types (static reference, no lifecycle):
callable_scripts - tool and script references
work_project - project tracking, tasks, deadlines
cross_reference - pointers to external information and resources
learned_procedure - multi-step workflows and procedures
Free, Starter, Pro, and Business plans available. See adaptiverecall.com for details.
This server cannot be deployed
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