Suma Memory

io.github.sumanaddanki由 GitHub 帳號 sumanaddanki 發布v1.5.0更新於 Oct 24, 2026

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

發布方自己的服務:Memory.fit

已驗證Streamable HTTP可網頁執行知識與記憶開發者工具

概覽

AI 產生的概覽

Suma Memory 為 AI 程式開發客戶端提供持久且可共用的知識圖譜,讓架構決策、錯誤根因與專案規則跨工作階段保留。

可以做什麼
  • 把架構決策、錯誤根因與團隊規則等專案知識寫入持久圖譜
  • 以自然語言查詢取回相關脈絡,依新近度、連結度、語意相似度與強化程度排序
  • 一次呼叫同時檢索與學習,讓工作階段既讀取也更新圖譜
  • 更正錯誤資訊(以新節點取代舊節點),並清除干擾檢索的雜訊節點
  • 檢查連線狀態與 API 金鑰是否有效
適用情境
適合反覆向 Claude Code、Cursor 等客戶端說明同一專案背景的情況,或需要多個代理、多位成員共用同一份決策與修復記憶的團隊。若只是臨時對話、不需保留脈絡,則幫助有限。
執行需求
需要 SUMA Pro API 金鑰,透過 SUMA_API_KEY 環境變數或 Authorization 標頭中的 bearer 權杖提供。託管端點以 streamable HTTP 存取,另提供 npm 套件作為 stdio 代理。有免費額度,超出後為付費方案。
安裝前請注意
API 金鑰是有效憑證,不要寫進共用的設定檔。寫入的內容會存放在第三方託管服務並進入組織共用圖譜,避免貼上金鑰、憑證或敏感程式碼。寫入、更正與清理操作會修改圖譜,更正為取代而非刪除。用量受速率限制並依方案計量。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Suma Memory,將其新增到工作區。
  2. 連接 Authorization token 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.sumanaddanki%2Fsuma-memory/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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.

來源:README.md,提交 4bf7cfb

工具

0
SourceWeft 尚未讀取此伺服器的工具。

版本歷史

1
  1. v1.5.0最新Sep 16, 2026

評分

已滿足適用檢查項的 75%

該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分

    請讓伺服器在無憑證時回應 tools/list,或發布可在沙箱中無憑證啟動的軟體包。

  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分

    請在儲存庫或註冊中心條目中宣告授權條款。

其他 Memory 整合