io.github.AIAppsAPI/adaptive-recall MCP Server

Adaptive memory system with cognitive scoring, knowledge graph, and self-improving ML. The server is a hosted long-term memory component for AI applications that stores, retrieves, and manages memory. It connects via MCP or a REST API, and uses multiple retrieval approaches.

🛠️ Key Features

Adaptive Recall hosted memory server
Long-term memory storage, retrieval, and management
Multi-strategy retrieval running in parallel:
- Vector similarity
- Temporal recency
- Full-text keyword
- Knowledge graph traversal
Learns which retrieval strategy to use (per readme excerpt)
Cognitive scoring, knowledge graph, self-improving ML

🚀 Use Cases

Long-term memory for AI applications
Memory access over MCP or REST API

⚡ Developer Benefits

Integration options via MCP and REST API
Retrieval across multiple strategies, including knowledge-graph traversal

⚠️ Limitations

Public details provided do not specify tool count or full API surface; only MCP/REST connectivity and retrieval strategies are described.

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):

json

{
  "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.

io.github.AIAppsAPI/adaptive-recall