Suma Memory

io.github.sumanaddankiPublished by GitHub account sumanaddankiv1.5.0Updated Oct 24, 2026

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

The publisher's own server: Memory.fit

VerifiedStreamable HTTPWeb executableKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

Suma Memory gives AI coding clients a persistent, shared knowledge graph so project decisions, bug root causes and rules carry across sessions.

What you can do
  • Store project knowledge such as architecture decisions, bug root causes and team rules in a persistent graph
  • Retrieve relevant context by natural-language query, ranked by recency, connectivity, similarity and reinforcement
  • Search and learn in one call, so a session both reads and updates the graph
  • Correct wrong facts by superseding them, and remove noisy nodes from search results
  • Check connection and API key health
When to use it
Useful when you repeatedly re-explain the same project context to Claude Code, Cursor or similar clients, or when several agents or teammates should share one memory of decisions and fixes. Less useful for one-off chats where no context needs to persist.
Requirements
A SUMA Pro API key, supplied as the SUMA_API_KEY environment variable or as a bearer token in the Authorization header. The hosted endpoint is reached over streamable HTTP; an npm package is offered as a stdio proxy. A free tier exists, with paid plans above it.
Before you install
The API key is a live secret and must be kept out of shared config files. Anything ingested is stored on a third-party hosted service and shared with the organization's graph, so avoid pasting secrets, credentials or sensitive code. Ingest, correct and clean calls write to the graph; corrections supersede rather than delete. Usage is rate-limited and metered by plan.

Installation

In SourceWeft

  1. Open Suma Memory in the dashboard and add it to a workspace.
  2. Connect Authorization token credentials. They are stored privately and never shown on public pages.
  3. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.sumanaddanki%2Fsuma-memory/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "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.

Source: README.md at commit 4bf7cfb

Tools

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Version history

1
  1. v1.5.0LatestSep 16, 2026

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