Adaptive Agent MCP - MCP Servers

Self-Evolving RAG for AI Agents

Agents don't just read memory — they write it.

中文 | English

Core Concept

Traditional RAG

User Input → Retrieve KB → Generate
               ↑
            Read-only
        (Human-maintained)

Self-Evolving RAG

User Input → Retrieve Memory → Generate
               ↑↓
           Read + Write
    Agent autonomously evolves

Key Differences:

| | Traditional RAG | Adaptive Agent MCP | |:---:|:---|:---| | Read | Retrieves pre-indexed documents | Dynamically accumulates at runtime | | Write | Human-maintained knowledge base | Agent writes autonomously | | Scope | Generic knowledge | User-specific memory | | State | Static data | Continuously evolves |

How It Works

In Claude Code: "Remember, I prefer TypeScript"
         ↓
    Agent automatically calls:
    • append_daily_log() → Record to daily log
    • update_preference() → Update preferences
    • extract_knowledge() → Extract knowledge graph
         ↓
In Antigravity: "What are my coding preferences?"
         ↓
    AI: "You prefer TypeScript"

Teach once, remember forever. Share across apps, never forget.

Quick Start

Installation

Configure mcp.json in any MCP-compatible AI application:

Basic Configuration:

{
  "mcpServers": {
    "adaptive-agent-mcp": {
      "command": "uvx",
      "args": ["adaptive-agent-mcp"]
    }
  }
}

Full Configuration (with Semantic Search API):

{
  "mcpServers": {
    "adaptive-agent-mcp": {
      "command": "uvx",
      "args": ["adaptive-agent-mcp"],
      "env": {
        "ADAPTIVE_EMBEDDING_BASE_URL": "https://api.openai.com/v1",
        "ADAPTIVE_EMBEDDING_API_KEY": "your-api-key",
        "ADAPTIVE_EMBEDDING_MODEL": "text-embedding-3-small",
        "ADAPTIVE_RERANK_BASE_URL": "https://api.cohere.ai/v1",
        "ADAPTIVE_RERANK_API_KEY": "your-api-key",
        "ADAPTIVE_RERANK_MODEL": "rerank-english-v3.0"
      }
    }
  }
}

Default storage path: ~/.adaptive-agent/memory. All apps share the same memory.

Enhance Agent Memory Behavior (Optional)

If your AI doesn't actively read/write memory, add this to your system prompt or user rules:

## Memory System Instructions

- At the start of each conversation, call `initialize_session` to load user preferences.
- When user says "remember", "save", or expresses preferences, call `update_preference` or `append_daily_log`.
- After completing tasks, briefly record progress using `append_daily_log`.
- When user asks about past conversations, use `query_memory_headers` or `search_memory_content`.

Features

| Feature | Description | Version | |:---|:---|:---:| | Three-Layer Memory | MEMORY.md + Daily Logs + Knowledge Graph | v0.1.0 | | Scope Isolation | project:xxx, app:xxx, global | v0.2.0 | | Concurrent Safety | Cross-process file locking | v0.3.0 | | Incremental Indexing | mtime-based smart updates | v0.3.0 | | Semantic Search | Embedding + Rerank API | v0.4.0 | | FTS5 Full-text | SQLite built-in search | v0.4.0 | | Knowledge Graph | NetworkX-based entity relations | v0.5.0 |

Available Tools

Memory Management

| Tool | Description | |:---|:---| | initialize_session | Initialize session with user profile and recent context | | append_daily_log | Append content to today's log | | update_preference | Intelligently update user preferences | | query_memory_headers | Query memory file metadata | | read_memory_content | Read complete memory file content | | search_memory_content | Full-text search using ripgrep |

Semantic Search

| Tool | Description | |:---|:---| | semantic_search | Vector similarity search | | fulltext_search | FTS5 keyword search with BM25 ranking | | index_document | Index document to vector store |

Knowledge Graph

| Tool | Description | |:---|:---| | extract_knowledge | Extract entity relations from text | | add_knowledge_relation | Manually add relations | | query_knowledge_graph | Query entities, relations, or stats | | multi_hop_query | Multi-hop reasoning queries |

Storage Structure

~/.adaptive-agent/memory/
├── MEMORY.md              # User preferences (scope-based)
├── .knowledge/
│   └── items.json         # Atomic facts
├── .vector/
│   └── vector.db          # SQLite + sqlite-vec
├── .graph/
│   └── knowledge.json     # NetworkX graph
└── 2026/
    └── 02_february/
        └── week_06/
            └── 2026-02-07.md  # Daily logs

Data Safety

Isolated storage: Data stored in ~/.adaptive-agent/memory, independent of uvx installation
Concurrent safety: filelock prevents data corruption from multiple clients
Human-readable: All data in Markdown/JSON format, easy to backup and version control

Documentation

License

MIT License - See LICENSE for details.

Adaptive Agent MCP — Where agents learn, remember, and evolve.

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