Adaptive Recall MCP server
io.github.AIAppsAPI/adaptive-recall·v1.0.1·4 starsAdaptive memory system with cognitive scoring, knowledge graph, and self-improving ML.
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
- 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
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
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 MemoryCodebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.not reviewedWidely usedC
- Neo4j MemoryMCP Neo4j Knowledge Graph Memory Servernot reviewedEstablishedA
State Memory MCPPersistent workflow graph server tracking tasks, decisions, spatial entities, and blockers.not reviewedEstablishedA