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Adaptive Recall MCP server

by AIAppsAPI·io.github.AIAppsAPI/adaptive-recall·v1.0.1·4 stars

Adaptive memory system with cognitive scoring, knowledge graph, and self-improving ML.

B72/100grade B
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Safety scan
B72/100

full report

Adoption
New

4 stars35 downloads/wk

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Adaptive Recall tools

No tool declarations could be read from the package source. They show once the server is installed.

Public scan report

scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan3 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 136 days ago8/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 72/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Adaptive Recall compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
72/100
higher than 38% of 48,255 servers
GitHub stars
4
more than 82% of 28,688 servers
Downloads a week
35
more than 25% of 12,191 servers with a package

Install Adaptive Recall in Claude Code, Cursor or VS Code

Runs npx -y adaptive-recall on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add adaptive-recall -- npx -y adaptive-recall
Add to Cursor

What the publisher says

From the Adaptive Recall repository's README, as published. We do not edit it. Read it on GitHub

Adaptive Recall

Adaptive memory system for AI applications. Patent pending.

adaptiverecall.com | Documentation | Sign Up Free

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://YOURSERVERURL/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

Nothing above is checked by us. What we check is on the safety report.

Adaptive Recall: common questions

Is Adaptive Recall MCP server safe?
Mostly: it is graded B (72/100). Read the Adaptive Recall safety report
How do I install Adaptive Recall?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Adaptive Recall need an API key?
Yes. The registry entry asks for ADAPTIVE_RECALL_API_KEY.
Is Adaptive Recall maintained?
The last commit was 174 days ago (2026-05-14). The latest release is v1.0.1.
What can I use instead of Adaptive Recall?
Servers from other publishers that do the same job: Codebase Memory MCP server, Neo4j Memory MCP server and State Memory MCP server.
How does Adaptive Recall compare with other MCP servers?
safety score 72/100 (higher than 38% of 48,255 servers); github stars 4 (more than 82% of 28,688 servers); downloads a week 35 (more than 25% of 12,191 servers with a package). Full comparison.

Alternatives to Adaptive Recall

Same job from other publishers: the closest match first, then the best rated.

  • Codebase Memory
    Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
    C
  • Neo4j Memory
    MCP Neo4j Knowledge Graph Memory Server
    A
  • State Memory MCP
    Persistent workflow graph server tracking tasks, decisions, spatial entities, and blockers.
    A