Skip to main content
Glama

Self-Evolving RAG for AI Agents

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

License: MIT Python 3.10+ MCP PyPI

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


Related MCP server: knowledge-base-server

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

  1. Python 3.10+

  2. Ripgrep (rg): REQUIRED for full-text search. (Windows: choco install ripgrep, macOS: brew install ripgrep)

  3. 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 MCPWhere agents learn, remember, and evolve.

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.
    6 npm
    178
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.
    35
    MIT
  • F
    license
    Not graded
    quality
    C
    maintenance
    An agentic memory system that enables AI assistants to store, search, and manage persistent memories with semantic understanding using natural language instructions.
    -