How do I install Adaptive Recall?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Adaptive Recall need an API key?

The publisher lists these environment variables: ADAPTIVE_RECALL_API_KEY.

What transport does Adaptive Recall use?

Adaptive Recall runs locally over stdio.

When was Adaptive Recall last checked?

Last checked 2026-10-16. Adaptive memory system with cognitive scoring, knowledge graph, and self-improving ML.

Adaptive Recall MCP server

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

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

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.

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.

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

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"
      }
    }
  }
}

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

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.

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.

Adaptive Recall

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

Questions

Adaptive Recall

What It Does

Connect

MCP Configuration

REST API

Actions

Memory Types

Pricing

Links

Adaptive Recall

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

Questions

Adaptive Recall

What It Does

Connect

MCP Configuration

REST API

Actions

Memory Types

Pricing

Links