Suma Memory

io.github.sumanaddanki由 GitHub 账号 sumanaddanki 发布v1.5.0更新于 Oct 24, 2026

Persistent memory for Claude Code and Cursor. Stop re-explaining your project every session.

发布方自己的服务:Memory.fit

已验证Streamable HTTP可网页运行知识与记忆开发者工具

概览

AI 生成的概览

Suma Memory 为 AI 编程客户端提供持久化、可共享的知识图谱,让架构决策、缺陷根因和项目规则跨会话保留。

可以做什么
  • 把架构决策、缺陷根因和团队规则等项目知识写入持久化图谱
  • 用自然语言查询检索相关上下文,按新近度、关联度、语义相似度和强化程度排序
  • 一次调用同时检索与学习,让会话既读取也更新图谱
  • 纠正错误信息(以新节点取代旧节点),并清理干扰检索的噪声节点
  • 检查连接状态与 API 密钥是否有效
适用场景
适合反复向 Claude Code、Cursor 等客户端解释同一项目背景的场景,或需要多个智能体、多名成员共享同一份决策与修复记忆的团队。若只是临时对话、无需保留上下文,则意义不大。
运行要求
需要 SUMA Pro API 密钥,通过 SUMA_API_KEY 环境变量或 Authorization 请求头中的 bearer 令牌提供。托管端点使用 streamable HTTP 访问,另提供 npm 包作为 stdio 代理。有免费额度,超出后为付费方案。
安装前请注意
API 密钥是有效凭据,不要写进共享配置文件。写入的内容会存放在第三方托管服务并进入组织共享图谱,避免粘贴密钥、凭据或敏感代码。写入、纠正和清理操作会修改图谱,纠正是取代而非删除。用量受速率限制并按套餐计量。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Suma Memory,将其添加到工作区。
  2. 连接 Authorization token 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.sumanaddanki%2Fsuma-memory/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "suma-memory": {
      "type": "http",
      "url": "https://sumapro.quadframe.work/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

README

SUMA Memory MCP

Stop re-explaining your project to Claude every time you start a new chat.

Your repos now have permanent memory. SUMA gives any MCP-compatible AI client (Claude Code, Cursor, Devin) a persistent knowledge graph that remembers architectural decisions, bug root causes, and project rules — across sessions, across machines, across your entire team.


Install (30 seconds)

Get an API key at sumapro.quadframe.work — free tier available.

Add to your .mcp.json:

json
{  "mcpServers": {    "suma-memory": {      "url": "https://sumapro.quadframe.work/mcp",      "headers": {        "Authorization": "Bearer sk_live_your_key_here"      }    }  }}

That's it. No local server. No Docker. No npm install. SUMA runs on Cloud Run — stateless, auto-scaled, always available.


Onboard Your Project (2 minutes)

After installing, run this once per repo to seed your permanent context:

suma_ingest(text="Project: [name]. Framework: [Next.js / Flask / etc].Auth lives in: [path/to/auth.py]. Database: [PostgreSQL / SQLite / etc].Rules never to break: [e.g. never store plaintext keys, all routes require org_id filter].Deployment target: [Cloud Run / Vercel / etc].")

From this point forward, every new session inherits this context. You never explain it again.


How It Works

SUMA stores knowledge in a weighted graph. Every node has a gravity score across four dimensions:

  • Recency — newer facts surface first
  • Density — well-connected facts outrank isolated ones
  • Semantic similarity — vector distance to your query
  • Emotional weight — high-signal facts are reinforced over time

When you call suma_search, the K-WIL gravity algorithm traverses the graph and returns the highest-relevance context — not a flat list of chunks, not a raw embedding match, but the facts that actually matter for what you're doing right now.


Core Tools

ToolWhat it does
suma_pingHealth check — verify connection and API key
suma_ingestAdd knowledge to the graph (architecture decisions, bug fixes, rules)
suma_searchRetrieve relevant context by natural language query
suma_talkSearch + learn in one call — retrieves context and updates graph
suma_correctFix wrong information — supersedes original, queues replacement
suma_cleanRemove noise nodes that pollute search results

Three Use Cases

1. Persistent Architecture Memory

python
# After finalizing a decision:suma_ingest(text="We chose REST over GraphQL. Root cause: GraphQL N+1 queries            caused 3x latency on /search. Architect ruling Apr 10 2026.")
# Next session, cold start — full context in one call:suma_search(query="why did we switch to REST?")# → Returns ruling with full context. No re-explaining.

2. Bug Root Cause Archive

python
# After fixing a hard bug:suma_ingest(text="Cloud Run WebSocket bug: asyncio.run() in daemon thread killed            by Cloud Run recycling. Fix: use asyncio.get_event_loop() instead.            Never use asyncio.run() in long-lived Cloud Run services.")
# Six months later, same error:suma_search(query="asyncio cloud run daemon thread crash")# → Root cause retrieved instantly. Hours saved.

3. Multi-Agent Knowledge Fusion

Architect, developer, and QA agents each write to SUMA using their own sessions. Their knowledge merges into one shared org graph. When QA asks "what did the architect decide about auth?", it retrieves the architect's ruling — zero explicit handoff required.


Enterprise Safety

Anti-flood protection: Each source machine is rate-limited to 5 ingests per 60 seconds. Runaway agent loops are broken gracefully — the 6th request returns {"status": "throttled"} without crashing or corrupting the graph.

Multi-tenant isolation: Every node is scoped to org_id at the database layer. Two organizations on the same Cloud Run instance cannot access each other's data — enforced by SQL, not application logic.

Immutable audit trail: suma_correct and suma_clean never delete data. Nodes are superseded and invisible to the API while preserved in storage for compliance.


Key Metrics (Live Production — April 2026)

MetricValue
Compression ratio94.7% — 801 nodes replace 15.2M tokens
Cost saved per org$14.47 across 538 queries
K-WIL fidelity96.3% — 26/27 facts recoverable from 5-node graph
Automated tests118 (102 Playwright E2E + 16 pytest)

Pricing

PlanQueries/monthPrice
Starter20,000Free
Developer100,000$4.99/mo
Team500,000$29/mo
EnterpriseUnlimitedContact

Get your key: sumapro.quadframe.work


© 2025–2026 Suman Addanke / A2 Vibe Creators LLC
US Patent applications pending — 6 filed (2025–2026). Unauthorized commercial use prohibited.

来源:README.md,提交 4bf7cfb

工具

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版本历史

1
  1. v1.5.0最新Sep 16, 2026

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