SyncContext
MCP server that provides a shared semantic memory layer for AI coding agents, enabling teams to store, search, and sync context, decisions, and knowledge across projects with project-based isolation and multi-backend support.
README
SyncContext
Shared team memory for AI coding agents. Sync context, decisions, and knowledge across your entire team via the Model Context Protocol.
The Problem
AI coding agents (Claude Code, Cursor, Windsurf) each maintain isolated context. Developer A's agent knows nothing about Developer B's decisions. This leads to:
- Conflicting architecture decisions across team members
- Repeated mistakes and lost institutional knowledge
- Painful onboarding for new developers
- No shared understanding between frontend, backend, and infra
The Solution
SyncContext provides a shared semantic memory layer that connects your team's AI agents. One token per project, shared brain, unlimited team members.
Developer A (Frontend) --> saves: "Button uses Tailwind, prop X is required"
Developer B (Backend) --> searches: "frontend patterns" --> gets full context
Developer C (New hire) --> runs: get_project_context --> instant onboarding
How It Works
- Your team deploys SyncContext (self-hosted or cloud)
- Each developer adds the server URL + their project token to their MCP client
- On first connection, the project is auto-created in the database
- AI agents read and write shared memories scoped to the project
MCP Client (Claude Code, Cursor)
│
│ Authorization: Bearer <project-token>
│ X-Project-Name: "My Project"
│
▼
SyncContext Server (HTTPS)
│
├── New token? → Auto-create project in DB
├── Known token? → Load existing project
│
▼
PostgreSQL + pgvector (semantic search)
Quick Start
Option 1: Connect to a hosted instance
Add to your .mcp.json (Claude Code) or MCP settings (Cursor):
{
"mcpServers": {
"synccontext": {
"url": "https://your-synccontext-server.com/mcp",
"headers": {
"Authorization": "Bearer your-project-token",
"X-Project-Name": "My Project"
}
}
}
}
That's it. The project is auto-created on first connection.
Option 2: Self-hosted with Docker
git clone https://github.com/infinity-ai-dev/SyncContext.git
cd SyncContext
cp .env.example .env
# Edit .env: set SYNCCONTEXT_GEMINI_API_KEY
docker compose up -d
Option 3: Local development (stdio)
# Requires PostgreSQL with pgvector
uv sync
uv run synccontext
MCP Client Configuration
Cloud / HTTP mode (recommended)
Works with any MCP client that supports HTTP transport:
{
"mcpServers": {
"synccontext": {
"url": "https://your-server.com/mcp",
"headers": {
"Authorization": "Bearer your-project-token",
"X-Project-Name": "My Project"
}
}
}
}
Local / stdio mode
For local development with a direct database connection:
{
"mcpServers": {
"synccontext": {
"command": "uv",
"args": ["--directory", "/path/to/SyncContext", "run", "synccontext"],
"env": {
"SYNCCONTEXT_PROJECT_TOKEN": "my-team-token",
"SYNCCONTEXT_DATABASE_URL": "postgresql://user:pass@localhost:5432/synccontext",
"SYNCCONTEXT_GEMINI_API_KEY": "your-key"
}
}
}
}
Tools (14 total)
Memory Management
| Tool | Description |
|---|---|
save_memory |
Store decisions, patterns, bugs, conventions with metadata |
get_memory |
Retrieve a specific memory by UUID |
update_memory |
Update content (auto re-embeds if changed) |
delete_memory |
Remove a specific memory |
bulk_save_memories |
Import multiple memories at once |
Search & Discovery
| Tool | Description |
|---|---|
search_memories |
Semantic search across all team knowledge |
search_by_file |
Find context about specific files |
find_similar |
Discover related memories by similarity |
list_memories |
Browse recent memories with filters |
Project Overview
| Tool | Description |
|---|---|
get_project_context |
Full project summary (onboarding) |
list_tags |
All knowledge categories with counts |
list_contributors |
Who's contributing knowledge |
Admin
| Tool | Description |
|---|---|
create_project |
Create a new project (admin token required) |
list_projects |
List all registered projects (admin token required) |
Architecture
┌─────────────────────────────────────┐
│ Claude Code / Cursor / Windsurf │
│ (MCP Client) │
└──────────┬──────────────────────────┘
│ HTTPS + Bearer Token
┌──────────▼──────────────────────────┐
│ SyncContext MCP Server │
│ ┌────────────┐ ┌───────────────┐ │
│ │ Auth │ │ Per-request │ │
│ │ Middleware │──│ Project Scope │ │
│ └────────────┘ └───────────────┘ │
│ ┌────────────┐ ┌───────────────┐ │
│ │ Embedding │ │ Memory + │ │
│ │ Provider │ │ Search Service│ │
│ └────────────┘ └───────────────┘ │
└──────────┬──────────────────────────┘
│
┌──────────▼──────────────────────────┐
│ PostgreSQL + pgvector │
│ ┌──────────┐ ┌──────────────────┐ │
│ │ projects │ │ memories + │ │
│ │ (tokens) │──│ memory_vectors │ │
│ └──────────┘ └──────────────────┘ │
└─────────────────────────────────────┘
Multi-Project Isolation
Each project token maps to an isolated namespace. Multiple teams share the same server with full data isolation:
Token A ("sc_frontend...") → Project "Frontend App" → memories scoped to frontend
Token B ("sc_backend...") → Project "Backend API" → memories scoped to backend
Token C ("sc_infra...") → Project "Infrastructure" → memories scoped to infra
Embedding Providers (auto-detected)
| Provider | Dimensions | Cost | Offline | Detected by |
|---|---|---|---|---|
| Gemini | 768 | Free (1500 req/min) | No | GEMINI_API_KEY set |
| OpenAI | 1536 | $0.02/1M tokens | No | OPENAI_API_KEY set |
| Ollama | 768 | Free | Yes | OLLAMA_BASE_URL set |
Vector Store Backends
| Backend | Best For | Persistence |
|---|---|---|
| pgvector (default) | Relational queries + vectors | Disk (durable) |
| Redis Stack | Sub-ms latency | AOF + volume (durable) |
Configuration
All settings via environment variables (prefix SYNCCONTEXT_):
| Variable | Default | Description |
|---|---|---|
PROJECT_TOKEN |
— | Default project token (stdio mode) |
ADMIN_TOKEN |
— | Admin token for create/list projects |
DATABASE_URL |
postgresql://... |
PostgreSQL connection string |
VECTOR_STORE |
pgvector |
pgvector or redis |
EMBEDDING_PROVIDER |
auto |
auto, gemini, openai, or ollama |
GEMINI_API_KEY |
— | Gemini API key |
OPENAI_API_KEY |
— | OpenAI API key |
OLLAMA_BASE_URL |
— | Ollama server URL |
TRANSPORT |
stdio |
stdio, sse, or streamable-http |
HOST |
0.0.0.0 |
HTTP bind address |
PORT |
8080 |
HTTP port |
Self-Hosted Deployment (Docker Swarm)
Prerequisites
- Docker Swarm with Traefik
- PostgreSQL with pgvector extension
- A domain pointing to your server
1. Prepare the database
# Install pgvector
docker exec $(docker ps -q -f name=postgres) bash -c \
"apt-get update && apt-get install -y postgresql-16-pgvector"
# Create database + extensions
docker exec $(docker ps -q -f name=postgres) psql -U postgres -c "CREATE DATABASE synccontext"
docker exec $(docker ps -q -f name=postgres) psql -U postgres -d synccontext -c \
'CREATE EXTENSION IF NOT EXISTS "uuid-ossp"; CREATE EXTENSION IF NOT EXISTS "vector";'
2. Deploy the stack
See deploy/swarm-stack.yml for a complete Portainer-ready stack with Traefik integration.
3. Tables are created automatically
On first startup, the container runs migrations and creates all tables. Check logs to confirm.
Development
uv sync --extra dev
uv run pytest tests/ -v # 53 tests
uv run ruff check core/ server/
uv run synccontext # run locally (stdio)
Docker Images
Multi-arch images for linux/amd64 and linux/arm64:
docker pull infinitytools/synccontext:latest
Roadmap
- [x] 14 MCP tools (CRUD, search, bulk, admin)
- [x] pgvector + Redis backends
- [x] Gemini / OpenAI / Ollama embeddings (auto-detected)
- [x] Docker multi-arch builds (amd64 + arm64)
- [x] Multi-project with per-request auth
- [x] Auto-create projects from Bearer token
- [x] Auto-migrations on container startup
- [ ] SyncContext Cloud (managed SaaS)
- [ ] Web dashboard for memory management
- [ ] Webhook notifications on memory changes
- [ ] Memory expiration / archival policies
- [ ] RAG integration (index entire codebases)
License
MIT — see LICENSE for details.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。