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.

Getting Started

Prerequisites

Python 3.10+
Ripgrep (rg): REQUIRED for full-text search. (Windows: choco install ripgrep, macOS: brew install ripgrep)
SQLite: Handled automatically by Python.

Configuration (v0.6.0)

Configuration is managed via Environment Variables.

1. mcp.json Structure

{
  "mcpServers": {
    "adaptive-agent-mcp": {
      "command": "uvx",
      "args": ["adaptive-agent-mcp"],
      "env": {
        "ADAPTIVE_EMBEDDING_BASE_URL": "https://api.xxx.cn/v1",
        "ADAPTIVE_EMBEDDING_API_KEY": "sk-your-xxx-key",
        "ADAPTIVE_EMBEDDING_MODEL": "Qwen/Qwen2.5-Coder-7B-Instruct",
        "ADAPTIVE_RERANK_BASE_URL": "https://api.xxx.cn/v1",
        "ADAPTIVE_RERANK_API_KEY": "sk-your-xxx-key",
        "ADAPTIVE_RERANK_MODEL": "BAAI/bge-reranker-v2-m3"
      }
    }
  }
}

Local Models:

Ollama: Set ADAPTIVE_EMBEDDING_PROVIDER to ollama.

LM Studio/vLLM: Set ADAPTIVE_EMBEDDING_PROVIDER to openai_compatible.

Base URL: Set to your local endpoint (e.g., http://localhost:11434/v1 or http://localhost:1234/v1).

API Key: Any string.

2. Environment Variables

All variables are prefixed with ADAPTIVE_.

| Variable | Description | Default | |---|---|---| | ADAPTIVE_STORAGE_PATH | Storage location | ~/.adaptive-agent/memory | | ADAPTIVE_RIPGREP_PATH | Path to rg executable | Auto-detect | | ADAPTIVE_EMBEDDING_PROVIDER | Embedding provider (openai_compatible) | openai_compatible | | ADAPTIVE_EMBEDDING_BASE_URL | API Endpoint | None | | ADAPTIVE_EMBEDDING_API_KEY | API Key | None | | ADAPTIVE_EMBEDDING_MODEL | Embedding Model | Qwen/Qwen3-Embedding-8B | | ADAPTIVE_RERANK_PROVIDER | Rerank provider (cohere_compatible) | cohere_compatible | | ADAPTIVE_RERANK_BASE_URL | API Endpoint | None | | ADAPTIVE_RERANK_API_KEY | API Key | None | | ADAPTIVE_RERANK_MODEL | Reranker Model | Qwen/Qwen3-Reranker-8B |

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 Items | v0.1.0 | | Scope Isolation | project:xxx, app:xxx, global | v0.2.0 | | Concurrent Safety | Cross-process file locking + async locks | v0.3.0 | | Incremental Indexing | mtime-based smart updates | v0.3.0 | | Hybrid Search | Vector + FTS5 with RRF fusion | v0.6.0 | | Rerank Service | Cohere-compatible re-ranking for higher precision | v0.6.1 | | Area Partitioning | Scope-based knowledge routing | v0.6.0 | | Knowledge Graph | NetworkX-based entity relations | v0.5.0 | | Async Foundation | Non-blocking I/O throughout | v0.6.0 |

Available Tools (14 tools)

Session & Retrieval

| Tool | Description | |:---|:---| | initialize_session | Initialize session with user profile and recent context | | query_memory_headers | Index scan — browse memory file metadata | | read_memory_content | Read complete memory file content | | search_memory_content | Full-text search using ripgrep |

Memory & Knowledge

| Tool | Description | |:---|:---| | update_preference | Intelligently update user preferences | | append_daily_log | Append content to daily log or knowledge items | | query_knowledge | Hybrid search (Vector + FTS5 + RRF fusion) with browse fallback | | delete_knowledge | Soft-delete knowledge items | | get_period_context | Aggregate weekly/monthly logs for summaries | | archive_period | Save period summaries |

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/
│   └── areas/
│       ├── general/items.json         # Global knowledge
│       ├── chat/items.json            # Chat-scope knowledge
│       ├── coding/items.json          # Coding-scope knowledge
│       ├── writing/items.json         # Writing-scope knowledge
│       └── projects/{name}/items.json # Project-specific knowledge
├── .index/
│   ├── vectors.db                     # SQLite + sqlite-vec + FTS5
│   └── index.json                     # Indexer metadata
├── .graph/
│   └── knowledge.json                 # NetworkX graph
├── .locks/                            # File lock directory
└── memory/
    └── 2026/
        └── 02_february/
            └── week_07/
                └── 2026-02-10.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

License

MIT License - See LICENSE for details.

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

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