Agent Memory — Shared Memory for AI Coding Agents

Fully local, persistent, semantic memory for your AI coding agents. No external API keys required.

Works with: Claude Code, OpenCode, Qwen Code, KiloCode, Antigravity, and any MCP-compatible agent.

Architecture

Global Qdrant (Docker)          Ollama (Embeddings)
      │                              │
      └────── Mem0 MCP Server ───────┘
                    │
    ┌───────┬───────┼───────┬───────────┐
  Qwen   Claude   OpenCode KiloCode  Antigravity

• Qdrant — Vector store, one global instance shared across all projects
• Ollama — Local LLM (gemma3:4b) + embedding model (nomic-embed-text)
• Mem0 — Memory orchestration layer
• MCP Server — Exposes memory tools to your agents

Prerequisites

• Python 3.11+
• Qdrant running (Docker or native)
• Ollama running locally with nomic-embed-text and gemma3:4b models

Quick Start

1. Start Qdrant (one-time, global)

docker run -d --name qdrant-mem0 --restart unless-stopped \
  -p 6333:6333 -v qdrant_mem0_global:/qdrant/storage \
  qdrant/qdrant:latest

2. Pull the embedding model

ollama pull nomic-embed-text

3. Set up the Python environment

python3.13 -m venv .venv
.venv/bin/pip install mem0ai mcp ollama

4. Seed memories for your project

.venv/bin/python3 seed_memories.py

Edit seed_memories.py to add your project-specific memories (TDD rules, architecture decisions, coding conventions, etc.).

5. Generate agent configs

python3 generate_configs.py

This creates MCP config files for all supported agents in the project root.

6. Done — agents auto-load rules

Every agent reads .agent-memory-rules.md on startup and automatically:

• Searches memory before coding (finds TDD rules, conventions, past decisions)
• Follows retrieved rules during coding
• Updates memory after coding (stores bug fixes, architecture decisions, learnings)

How Agents Connect

The Universal Pattern

Every project gets these files:

File	Purpose	Loaded by
.agent-memory-rules.md	Universal brain — rules for before/during/after coding	All agents
CLAUDE.md	Tells Claude Code to load the rules file	Claude Code
AGENTS.md	Tells OpenCode to load the rules file	OpenCode
.kilocode/mcp.json	MCP config + native instruction injection	KiloCode
mcp_config.json	MCP config	Antigravity
.qwen/mcp.json	MCP config	Qwen Code

MCP Server

The mem0_mcp_server.py exposes 4 tools to agents:

Tool	Description
mem0_add	Store a new memory with optional category
mem0_search	Semantic search across memories
mem0_list	List all memories for an agent
mem0_delete	Delete a memory by ID

Project Setup

For each new project:

1. Copy mem0_mcp_server.py, seed_memories.py, generate_configs.py, .agent-memory-rules.md, and the .venv/ into the project
2. Edit seed_memories.py — add your project-specific memories (use a unique MEM0_COLLECTION name)
3. Run seed_memories.py to populate memory
4. Run generate_configs.py to create agent MCP configs
5. Done — all agents in that project share the same memory

Env Variables

Variable	Description	Default
MEM0_AGENT_ID	Agent/user identifier	agent-memory
MEM0_COLLECTION	Qdrant collection name	agent_memory
LLM_MODEL	Ollama LLM model	gemma3:4b
EMBED_MODEL	Ollama embedding model	nomic-embed-text
OLLAMA_BASE_URL	Ollama URL	http://localhost:11434
QDRANT_URL	Qdrant URL	http://localhost:6333

License

MIT