Skip to main content

Persistent MCP server — session memory, engineering pipeline, and living knowledge base for AI agents

Project description

Synapse MCP

Where AI agents remember, plan, and ship — without losing context.

一个让 AI 代理拥有持久记忆、结构化思考和端到端交付能力的 MCP 服务器。


English | 中文


English

The Problem

Today's AI coding assistants are brilliant but amnesiac. They forget what they built between sessions, lose track of why decisions were made, and treat every request as if it's the first conversation. You end up repeating context, re-explaining architecture, and manually tracking what stage you're at.

Synapse changes that. It gives AI agents the infrastructure they need to persist across sessions — structured memory, disciplined engineering process, and a living knowledge base that grows with every interaction.

What Makes Synapse Different

Three pillars, one server:

  MEMORY ──────── PROCESS ──────── KNOWLEDGE
  Session state   6-stage pipe    Living wiki
  Never forget    Quality gates   Self-growing
  Cross-project   Contract-first  Always queryable

1. Persistent Session Memory — Development sessions survive restarts, crashes, and coffee breaks. Every task, decision, and timestamp is atomically persisted. Pick up exactly where you left off, days or weeks later.

2. Engineering Pipeline — Natural language becomes structured delivery: requirements flow through architecture with contract generation, implementation, integration, adversarial QA, and deployment. Each stage must pass validation before the next begins. No shortcuts.

3. Living Knowledge Base — Initialize wikis, ingest any content, query with natural language. The knowledge base grows with every project, creating institutional memory that outlasts any single session.

The Architecture

Synapse is the infrastructure layer of the Synapse ecosystem — the persistent backbone that connects the orchestration brain (synapse-brain) with specialized execution skills (synapse-code, synapse-wiki).

┌──────────────────────────────────────────────┐
│              MCP Client (you)                 │
│    Claude Desktop / Cursor / Claude Code      │
└─────────────────┬────────────────────────────┘
                  │ "Build a REST API with auth"
                  ▼
┌──────────────────────────────────────────────┐
│           Synapse MCP Server                  │
│                                               │
│  MEMORY   session_create  ← "remember this"  │
│           session_status  ← "where are we?"  │
│           session_archive ← "ship & store"   │
│                                               │
│  PROCESS  pipeline_run    ← "execute plan"   │
│           pipeline_status ← "how's it going" │
│                                               │
│  KNOWLEDGE wiki_init     ← "new workspace"   │
│           wiki_ingest    ← "learn this"      │
│           wiki_query     ← "what do we know" │
│                                               │
│  URI      wiki:// state:// log://  ← direct   │
└─────────────────┬────────────────────────────┘
                  │
         ┌────────┼────────┐
         ▼        ▼        ▼
    ~/.synapse/  Pipeline  Wiki files
    (atomic)    (stages)  (growing)

MCP Tools (12)

Session Management — Never Lose Context

Tool Description
session_create Start a tracked development session
session_status See progress, tasks, and decisions at a glance
session_list Browse all sessions across projects
session_save Checkpoint state with atomic persistence
session_archive Store completed work with timestamps

Pipeline Execution — Discipline Built In

Tool Description
pipeline_run Execute REQ → ARCH → DEV → INT → QA → DEPLOY with live progress
pipeline_status Check which stage passed, which is running
pipeline_stages See what each stage validates

Knowledge Management — Institutional Memory

Tool Description
wiki_init Create a structured knowledge workspace
wiki_ingest Feed it files, directories, or raw text
wiki_query Ask questions in natural language
wiki_lint Verify knowledge integrity

MCP Resources (3 URI Schemes)

URI Purpose
wiki://{path} Read any wiki page directly
state://{project} Access session state as JSON
log://{project} View activity timeline

MCP Prompts (2 Templates)

Prompt Purpose
pipeline_template Structured prompt for each pipeline stage
wiki_page_template Consistent wiki page formats

Quick Start

# Via uv (recommended)
uvx --from synapse-mcp synapse-mcp-server

# Via pip
pip install synapse-mcp
python -m synapse_mcp.server

Client Configuration

Claude Desktop / Cursor / Windsurf

{
  "mcpServers": {
    "synapse": {
      "command": "uv",
      "args": ["run", "--from", "synapse-mcp", "synapse-mcp-server"]
    }
  }
}

Claude Code

claude mcp add synapse -- uv run --from synapse-mcp synapse-mcp-server

HTTP Transport (remote)

python -m synapse_mcp.server --transport http --port 8000

中文

问题所在

今天的 AI 编程助手才华横溢却患有"失忆症"。它们在会话之间遗忘已构建的内容,丢失决策背后的原因,把每次请求都当作第一次对话。你不得不重复上下文、重新解释架构、手动跟踪进度。

Synapse 改变了这一切。 它为 AI 代理提供了跨会话持久化的基础设施——结构化的记忆、严谨的工程流程、以及随每次交互共同成长的知识库。

Synapse 的独特之处

三大支柱,一个服务器:

  记忆 ──────── 流程 ──────── 知识
  会话状态      6 阶段流水线   活体 Wiki
  永不遗忘      质量门禁       自我成长
  跨项目关联    契约驱动       随时可查

1. 持久化会话记忆 — 开发会话在重启、崩溃、甚至隔天之后依然完整。每个任务、决策、时间戳都通过原子写入持久化。精确回到上次离开的地方,无论过了多久。

2. 工程化流水线 — 自然语言驱动结构化交付:需求经过架构设计(含契约生成)、实现、集成、对抗性测试、部署。每个阶段必须通过校验才能进入下一阶段。没有捷径。

3. 活体知识库 — 初始化知识空间,摄入任意内容,用自然语言查询。知识库随每个项目增长,形成超越单次会话的组织记忆。

架构定位

Synapse 是 Synapse 生态系统的基础设施层——连接编排大脑 (synapse-brain) 与专业执行技能 (synapse-code, synapse-wiki) 的持久化骨干。

快速开始

# 使用 uv(推荐)
uvx --from synapse-mcp synapse-mcp-server

# 使用 pip
pip install synapse-mcp
python -m synapse_mcp.server

客户端配置

Claude Desktop / Cursor / Windsurf

{
  "mcpServers": {
    "synapse": {
      "command": "uv",
      "args": ["run", "--from", "synapse-mcp", "synapse-mcp-server"]
    }
  }
}

Claude Code

claude mcp add synapse -- uv run --from synapse-mcp synapse-mcp-server

HTTP 传输(远程模式)

python -m synapse_mcp.server --transport http --port 8000

Related Projects / 相关项目

  • synapse-brain — OpenClaw 持久化编排代理,Synapse 的"大脑"
  • synapse-code — 智能代码开发工作流引擎,70 项测试全部通过
  • synapse-wiki — 智能知识管理系统

License / 许可

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

synapse_mcp-1.0.1.tar.gz (20.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

synapse_mcp-1.0.1-py3-none-any.whl (20.0 kB view details)

Uploaded Python 3

File details

Details for the file synapse_mcp-1.0.1.tar.gz.

File metadata

  • Download URL: synapse_mcp-1.0.1.tar.gz
  • Upload date:
  • Size: 20.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for synapse_mcp-1.0.1.tar.gz
Algorithm Hash digest
SHA256 c7dca97bbf2ec76483313fc251e15dd1b0714258e284c6d05e9c8f32864be5ac
MD5 6b8e5a19839d11348916eab957d762b0
BLAKE2b-256 f0cad2bea945e31d45bb3bf01c6113c0e45b730d52926e0e6c8620479115d222

See more details on using hashes here.

File details

Details for the file synapse_mcp-1.0.1-py3-none-any.whl.

File metadata

  • Download URL: synapse_mcp-1.0.1-py3-none-any.whl
  • Upload date:
  • Size: 20.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for synapse_mcp-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 435a553ae37f166cc69abb26ac2304c47bc47060a564ed9763ce438d70833e86
MD5 631df716cdc6b744b04e485454b5f6dd
BLAKE2b-256 324f5793b71244c8721d3a6d1aeb705dc4af8a542f96033d4ef1e0ff96b4d856

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page