Adaptive Recall

Adaptive memory system for AI applications. Patent pending.

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What It Does

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

Connect

Sign up at adaptiverecall.com to get your server URL and API key.

MCP Configuration

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.

REST API

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.

Actions

Memory Types

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

Pricing

Free, Starter, Pro, and Business plans available. See adaptiverecall.com for details.

Links

• Website
• Documentation
• Sign Up