Related Pages

Related topics: SDKs and Framework Integrations, Applications, MCP Server, and Extensions, Memory Graph, Playground, and Developer Tooling

Platform Overview and Architecture

1. Purpose and Scope

Supermemory is described as the long-term and short-term memory infrastructure for AI agents, providing state-of-the-art memory and context management for personalized, context-aware AI applications. Source: skills/supermemory/README.md. The platform exposes a single unified API that combines four foundational capabilities: a Memory API for learned user context that evolves over time, User Profiles for static and dynamic facts, RAG (retrieval-augmented generation) for semantic search across knowledge bases, and Connectors with built-in file processing. Source: README.md.

The repository is a polyglot monorepo that packages the API together with multiple first-party clients and integrations. It serves three distinct audiences from the same project: end users who want their AI assistant to remember them, developers building AI products, and operators who want a self-hosted binary. Source: README.md.

2. Repository Topology

The monorepo contains four categories of artifacts that together form the platform.

Sources: packages/tools/package.json, packages/memory-graph/package.json, apps/mcp/README.md, apps/raycast-extension/package.json, apps/browser-extension/package.json, apps/memory-graph-playground/src/app/page.tsx, skills/supermemory/README.md.

The packages/tools package is the developer integration hub. It exports subpath entry points for each framework (for example @supermemory/tools/ai-sdk, @supermemory/tools/openai, and a VoltAgent bundle) and is consumed both by external applications and by the in-repo docs-test package. Source: packages/tools/package.json, packages/docs-test/package.json.

3. High-Level Architecture

The platform follows a layered design where a hosted API provides memory primitives, multiple SDKs translate those primitives into framework-native constructs, and a set of client apps present the same data through different UX surfaces.

flowchart TB
    subgraph Clients["Client Surfaces"]
        App["Consumer App<br/>(app.supermemory.ai)"]
        MCP["MCP Server<br/>(apps/mcp)"]
        BrExt["Browser Extension"]
        Rx["Raycast Extension"]
        Playground["Memory Graph Playground"]
    end

    subgraph Integrations["packages/tools (SDK integrations)"]
        AISDK["@supermemory/tools/ai-sdk"]
        OpenAI["@supermemory/tools/openai"]
        Others["LangChain / CrewAI / VoltAgent"]
    end

    subgraph Core["Supermemory API"]
        Memory["Memory API"]
        Profile["User Profiles"]
        RAG["RAG / Search"]
        Conn["Connectors + File Processing"]
    end

    App --> Core
    MCP --> Core
    BrExt --> Core
    Rx --> Core
    Playground --> Core
    AISDK --> Core
    OpenAI --> Core
    Others --> Core

The MCP server is a particularly important architectural piece: it runs on Cloudflare Workers using Durable Objects for session state, Hono as the HTTP framework, and the official @modelcontextprotocol/sdk plus the supermemory SDK to proxy requests. Source: apps/mcp/README.md. This server allows assistants such as Claude Desktop, Cursor, Windsurf, VS Code, Claude Code, OpenCode, OpenClaw, and Hermes to share a single memory namespace through a standard protocol. Source: README.md.

4. Memory Model

All integrations converge on a shared memory model defined in packages/tools/src/shared/types.ts. The model has three orthogonal concerns:

• Retrieval mode (mode): "profile" for static + dynamic user facts, "query" for semantic search of memories, and "full" to combine both. Source: packages/tools/src/shared/types.ts.
• Persistence mode (addMemory): controls when new information is written back to long-term storage. Source: packages/tools/src/shared/types.ts.
• Search mode (searchMode): "memories" (atomic facts), "documents" (chunks), or "hybrid" for both. Source: packages/tools/src/voltagent/types.ts.

User profile data is split into static facts (name, profession, long-term preferences, goals) and dynamic facts (current projects, recent interests). Search results are returned as an array of { memory, metadata } objects, plus pre-formatted markdown strings that middleware can inject directly into a system prompt. Source: packages/tools/src/shared/types.ts.

Memory is scoped by a container tag that groups related memories per user, project, or client, and an optional threadId groups messages of a single conversation for contextual memory generation. Source: packages/tools/src/shared/types.ts, packages/tools/README.md.

5. Integration Patterns

Two concrete integration patterns are documented.

The Vercel AI SDK pattern exposes searchMemoriesTool, addMemoryTool, getProfileTool, documentListTool, documentDeleteTool, documentAddTool, and memoryForgetTool as AI SDK tool() definitions with strict Zod schemas. Source: packages/tools/src/ai-sdk.ts, packages/tools/src/openai/index.ts. The middleware reads the latest user message, calls the Supermemory API in the configured mode, and injects the result into the system prompt through a customizable PromptTemplate function. Source: packages/tools/src/shared/types.ts.

The OpenAI middleware pattern uses withSupermemory(openai, options) to wrap any OpenAI client. Memories are fetched and injected automatically on every chat.completions.create call, supporting the same containerTag, customId, mode, and addMemory options as the AI SDK version. Source: packages/tools/README.md.

6. Operational Surfaces

Beyond the API and SDKs, the repo ships three operational surfaces. The memory graph component (packages/memory-graph) renders a force-directed graph of documents and memories using d3-force and React 18+; it supports pan, zoom, drag, arrow-key navigation, and stress tests of 50–500 documents with an FPS counter. Source: packages/memory-graph/package.json, apps/memory-graph-playground/src/app/page.tsx. The Raycast extension (apps/raycast-extension) exposes add-memory, search-memories, and search-projects commands on macOS and Windows. Source: apps/raycast-extension/package.json. The browser extension (apps/browser-extension) is a wxt + React 19 project that captures arbitrary web content into the memory store. Source: apps/browser-extension/package.json.

See Also

• Memory Model and Data Types
• SDK Integrations
• MCP Server
• Memory Graph Visualization

Related Pages

Related topics: Platform Overview and Architecture, Applications, MCP Server, and Extensions, Memory Graph, Playground, and Developer Tooling

SDKs and Framework Integrations

Overview

The supermemory repository exposes memory, RAG, and user-profile capabilities through a layered set of SDKs, framework wrappers, and integration surfaces. The top of the stack is a language-agnostic REST API, below it sits a first-party supermemory client (TypeScript and Python), and around it the packages/tools workspace provides drop-in middleware and tool definitions for the most common agent frameworks. Adjacent applications — an MCP server, a Raycast extension, a React knowledge-graph component, and a Claude skill — round out the integration surface so the same memory backend can be reached from desktop agents, IDEs, browser extensions, and chat clients. Source: README.md, packages/tools/package.json.

Architecture of the Integration Layer

The stack is intentionally composable: an application picks a framework wrapper, the wrapper calls the core client, and the client talks to the API. Surfaces that do not fit a framework wrapper (MCP, Raycast, Claude skill) bypass the wrapper and call the client or API directly.

flowchart TB
    App[AI Application / Agent]
    subgraph Wrappers ["packages/tools"]
        Vercel["@supermemory/tools/ai-sdk<br/>(Vercel AI SDK)"]
        OpenAI["@supermemory/tools/openai<br/>(function calling / middleware)"]
        Mastra["@supermemory/tools/mastra"]
        Volt["@supermemory/tools/voltagent"]
    end
    subgraph Surfaces ["Adjacent apps"]
        MCP["apps/mcp<br/>(Cloudflare Workers MCP server)"]
        Raycast["apps/raycast-extension<br/>(macOS / Windows)"]
        Graph["packages/memory-graph<br/>(React + d3-force)"]
    end
    Core["supermemory client<br/>(TS / Py)"]
    API[(Supermemory API)]
    App --> Vercel
    App --> OpenAI
    App --> Mastra
    App --> Volt
    Vercel --> Core
    OpenAI --> Core
    Mastra --> Core
    Volt --> Core
    App -. MCP .-> MCP
    App -. Raycast .-> Raycast
    App -. Graph .-> Graph
    MCP --> API
    Raycast --> Core
    Core --> API

Framework Wrappers in `packages/tools`

The packages/tools workspace is the canonical home for framework integrations. Its package.json declares subpath exports for four targets: ./ai-sdk, ./mastra, ./openai, and ./voltagent. Source: packages/tools/package.json.

Vercel AI SDK

The Vercel wrapper is built around withSupermemory(model, options). It accepts a containerTag (a user/project scope) and a customId (groups messages into the same document), and supports retrieval modes "profile", "query", and "full". A PromptTemplate function can customize how injected context is rendered; it receives a MemoryPromptData object with pre-formatted userMemories, pre-formatted generalSearchMemories, and the raw searchResults array for custom filtering. Source: packages/tools/src/shared/types.ts, packages/tools/README.md.

Internally, the wrapper normalizes both V2 and V3 message shapes from @ai-sdk/provider, so a single implementation supports AI SDK 5 and 6. Source: packages/tools/src/vercel/util.ts. The individual tool creators (e.g. searchMemoriesTool) are defined with Zod schemas and TOOL_DESCRIPTIONS / PARAMETER_DESCRIPTIONS constants to keep tool descriptions consistent with the documented behavior. Source: packages/tools/src/ai-sdk.ts.

OpenAI SDK

The OpenAI surface is exported from @supermemory/tools/openai. It provides both a withSupermemory middleware factory (for transparent prompt injection) and a complete tool set: searchMemories, addMemory, getProfile, documentList, documentDelete, documentAdd, and memoryForget. getToolDefinitions() returns the matching ChatCompletionTool[] array, while createToolCallExecutor resolves a function-call to a string result, making the helpers drop-in for OpenAI function-calling and Assistants flows. Source: packages/tools/src/openai/tools.ts, packages/tools/src/openai/index.ts.

Mastra and Voltagent

The same withSupermemory factory shape is exposed under ./mastra and ./voltagent, so an agent built on either framework can wrap its model in a single call and inherit profile, search, and document tools. The middleware is unit-tested end-to-end through the AI-SDK entry as well — the test suite covers searchMemoriesTool, getProfileTool, documentListTool, and documentDeleteTool against a real API. Source: packages/tools/src/tools.test.ts.

Adjacent Integration Surfaces

MCP server (apps/mcp). A Cloudflare Worker that speaks the Model Context Protocol over streamable HTTP. It uses Hono for routing, Durable Objects with SQLite for session state, and the official @modelcontextprotocol/sdk. The server exposes memory tools, a projects resource, and a context prompt that returns the user's profile and preferences for system-prompt injection. Source: apps/mcp/README.md. The top-level README documents MCP clients that work out of the box — Claude Desktop, Cursor, Windsurf, VS Code, Claude Code, OpenCode, OpenClaw, and Hermes — and shows the two-line JSON configuration for both OAuth and Bearer-token (sm_…) auth. Source: README.md.

Raycast extension (apps/raycast-extension). A productivity launcher integration with add-memory, search-memories, and search-projects commands for macOS and Windows, configured with a single API-key preference. Source: apps/raycast-extension/package.json.

Memory graph component (packages/memory-graph). A React 18+ component that renders the living knowledge graph using d3-force, useful for surfacing relationships between memories in admin or debug UIs. Source: packages/memory-graph/package.json.

Documentation test harness (packages/docs-test). A Bun-runnable suite that exercises the documentation snippets against a real API, with separate scripts for TypeScript, Python, integrations, quickstart, SDK, and search. Source: packages/docs-test/package.json.

Claude Skill

A skills/supermemory directory publishes a reference skill with auto-invocation enabled, so Claude proactively suggests supermemory when developers ask about persistent memory, personalization, or RAG. The skill covers the SDK, REST API, the 6-stage processing pipeline, eight real-world use cases (chatbot, long-term task assistant, document KB, customer-support AI, code-review assistant, learning companion, multi-tenant SaaS, research assistant), and best practices such as container-tag isolation, metadata, thresholds, and error handling. Source: skills/supermemory/README.md.

See Also

• README.md — top-level quickstart, search modes, and MCP configuration
• packages/tools/README.md — middleware options and PromptTemplate examples
• packages/tools/src/shared/types.ts — MemoryPromptData and PromptTemplate definitions
• apps/mcp/README.md — MCP server tools, resources, and prompts
• skills/supermemory/README.md — Claude skill reference and use cases

Related Pages

Related topics: Platform Overview and Architecture, SDKs and Framework Integrations, Memory Graph, Playground, and Developer Tooling

Applications, MCP Server, and Extensions

The supermemory monorepo is not just an API — it ships a full ecosystem of client applications, protocol servers, and AI agent integrations that wrap the core memory service. This page covers the four major surfaces developers and end users interact with: the hosted MCP server, dedicated browser and Raycast extensions, the memory-graph playground, and the framework-agnostic @supermemory/tools integration package (including the Claude skill).

1. Model Context Protocol (MCP) Server

The flagship application is the MCP server at apps/mcp, which exposes supermemory as a tool provider to any MCP-compatible AI client (Claude Desktop, Cursor, Windsurf, VS Code, Claude Code, OpenCode, OpenClaw, Hermes). The server is built on Cloudflare Workers with Durable Objects for stateful session handling, uses the Hono HTTP framework, and talks to the upstream supermemory API through the official SDK. Source: apps/mcp/src/server.ts.

The server implements the MCP specification via @modelcontextprotocol/sdk and the agents/mcp runtime, registering tools such as memory (save), recall (search), whoAmI, listProjects, and memory-graph / fetch-graph-data, plus a context prompt that injects the user's profile and preferences into the system message. Source: apps/mcp/README.md.

The server enforces a 5-minute TTL on cached containerTags (CONTAINER_TAGS_TTL_MS = 5 * 60 * 1000) and caps the recall payload at 200,000 characters (MAX_RECALL_CHARS = 200000) to prevent runaway context windows. Source: apps/mcp/src/server.ts.

Client Setup Matrix

The web app at apps/web/lib/mcp-client-setup.ts and apps/web/lib/mcp-manual-instructions.ts maintains a per-client configuration matrix that decides whether to show manual JSON/TOML snippets or a one-click (CLI / deeplink / URL copy) tab:

Source: apps/web/lib/mcp-client-setup.ts and apps/web/lib/mcp-manual-instructions.ts.

Authentication to the server supports both OAuth and a static Bearer API key (sm_… prefix). The main README documents both forms. Source: README.md.

2. Browser and Raycast Extensions

Two first-party extensions live in the apps/ workspace.

• Browser extension (apps/browser-extension/package.json) is a WXT + React project that ships a content-script + side-panel UI. Dependencies include turndown (HTML→Markdown conversion) and the standard React 19 stack. Source: apps/browser-extension/package.json.
• Raycast extension (apps/raycast-extension/package.json) exposes three commands — add-memory, search-memories, and search-projects — and requires a password-typed preference named apiKey sourced from the user's console. Source: apps/raycast-extension/package.json.

Both extensions are thin clients that call the same supermemory REST API the SDK uses, giving end users keyboard-driven or in-browser capture and recall.

3. Memory Graph Playground and `@supermemory/memory-graph`

The memory-graph playground (apps/memory-graph-playground) is a Next.js stress-test UI for the React component published in packages/memory-graph. The component is a react >= 18.0.0 peer that depends on d3-force for force-directed layout. Source: packages/memory-graph/package.json.

The playground surfaces a node-graph of every document the user's API key can see, with pan/zoom, click-to-detail, drag, arrow-key navigation, stress test buttons that seed 50–500 mock documents, an FPS counter, and a slideshow mode. Mock data is generated from the MEMORY_TEMPLATES array in packages/memory-graph/src/mock-data.ts, which produces sentences like "The user prefers {preference} when working with {topic}" or "Migration plan: move from {oldThing} to {newThing} by {deadline}". Source: apps/memory-graph-playground/src/app/page.tsx and packages/memory-graph/src/mock-data.ts.

4. `@supermemory/tools` and the Claude Skill

The packages/tools package is the framework-agnostic integration layer. It ships:

• A Vercel AI SDK tool factory — searchMemoriesTool accepts informationToGet, includeFullDocs (default false), and limit (default 10) and is built with zod schemas whose strictness is controlled by config.strict. Source: packages/tools/src/ai-sdk.ts.
• An OpenAI middleware — withSupermemory(openai, { containerTag, customId, mode, addMemory, verbose }) transparently injects memories into every chat.completions.create call. Source: packages/tools/README.md.
• Shared types — MemoryPromptData { userMemories, generalSearchMemories, searchResults } and a PromptTemplate function for customising what is injected into the system prompt. Source: packages/tools/src/shared/types.ts.

The configuration object SupermemoryToolsConfig enforces a mutually-exclusive relationship between containerTags and projectId — exactly one must be provided. Source: packages/tools/src/types.ts.

The Claude skill at skills/supermemory/README.md is an *auto-invocation* reference skill (Apache-2.0) that teaches Claude Code to recommend supermemory proactively, with code samples for TypeScript, Python, Vercel AI SDK, LangChain, CrewAI, and OpenAI SDK. Source: skills/supermemory/README.md.

Architecture Overview

flowchart LR
    User[End User] -->|installs| BrowserExt[browser-extension]
    User -->|installs| RaycastExt[raycast-extension]
    User -->|configures| MCPClient[MCP client<br/>Claude, Cursor, VS Code, ...]
    MCPClient -->|HTTPS / SSE| MCPServer[apps/mcp<br/>Cloudflare Worker + DO]
    BrowserExt -->|REST| API[supermemory API]
    RaycastExt -->|REST| API
    MCPServer -->|SDK| API
    Playground[memory-graph-playground] -->|SDK| API
    ToolsPkg[packages/tools<br/>AI SDK + OpenAI mw] -->|SDK| API
    Agent[Custom AI agent] --> ToolsPkg
    Agent --> MCPClient

Common Failure Modes

• OAuth callback fails locally — the MCP dev server (bun run dev on port 8788) needs the main API at the URL given by API_URL in .dev.vars for token validation. Source: apps/mcp/README.md.
• Schema validation errors on OpenAI — set strict: true on SupermemoryToolsConfig so all tool schema properties are marked required and satisfy OpenAI strict mode. Source: packages/tools/src/types.ts.
• Cross-tenant memory leakage — when configuring both the MCP server and the tools package, ensure you pass the same containerTag; mixing tags between memory and recall calls will produce empty results. Source: apps/mcp/src/server.ts.
• E2E tests silently skipped — the MCP e2e suite requires SUPERMEMORY_API_KEY and SUPERMEMORY_MCP_URL exports or it skips entirely. Source: apps/mcp/README.md.

See Also

• Core SDK and API Quickstart
• MCP Server Source
• @supermemory/tools Reference
• Memory Graph Component
• Console / API Keys

Related Pages

Related topics: Platform Overview and Architecture, SDKs and Framework Integrations, Applications, MCP Server, and Extensions

Memory Graph, Playground, and Developer Tooling

Overview

The supermemory repository ships more than a REST API: it provides an entire developer surface for inspecting, demoing, and wiring memory into AI agents. Three concerns sit at the center of this surface: the @supermemory/memory-graph React component for visualizing stored documents and their memory relationships, the memory-graph-playground app that demonstrates the component end-to-end, and a broad set of developer tooling (framework SDKs, MCP server, Pipecat service, and a Claude skill). Together they let a developer go from "I want to see what supermemory stored" to "I have a memory-augmented agent running in production" without leaving the repo. Source: README.md:1-120

The `@supermemory/memory-graph` Package

The @supermemory/memory-graph package is an interactive Canvas 2D visualization built on React 18+ and d3-force for physics-based layout. Source: packages/memory-graph/package.json:25-46 It renders two node types — documents as rectangles and memories as hexagons — connected by edges that represent document similarity and memory version chains. Source: packages/memory-graph/README.md:35-45

The component exposes a small, opinionated prop surface. The essential props are summarized below.

Source: packages/memory-graph/README.md:48-58, apps/memory-graph-playground/README.md:18-38

Behavior worth noting: nodes are pan/zoom/drag interactive, a space filter lets the user narrow by workspace, and a mock-data generator lives at packages/memory-graph/src/mock-data.ts to drive the playground without a live API key. Source: packages/memory-graph/src/mock-data.ts:1-60

The Memory Graph Playground

The playground is a Bun-served Next-style demo that wires the graph component to a real API. Source: apps/memory-graph-playground/README.md:1-40

bun install
bun dev      # http://localhost:3000

On first load the user pastes a Supermemory API key, and the playground fetches documents and feeds them into <MemoryGraph>. The README shows a minimal mount: documents, isLoading, variant="console", and a loadMoreDocuments callback are the only required wiring. Source: apps/memory-graph-playground/README.md:15-38

The playground's main job is to validate the visual contract — node shapes, edges, highlighting, pagination — before the component is dropped into a customer-facing product.

Developer Tooling Ecosystem

The packages/tools workspace and the apps/mcp server are the two halves of the framework-integration story. The tools package builds Zod-typed tool definitions over the Supermemory API for the Vercel AI SDK, LangChain, CrewAI, OpenAI SDK, Claude, and VoltAgent, all sharing a common description set. Source: packages/tools/src/tools-shared.ts:1-50

flowchart LR
  A[Agent / LLM] -->|tool call| T[packages/tools]
  T -->|searchMemories| SM[Supermemory API]
  T -->|addMemory| SM
  T -->|getProfile| SM
  SM -->|memories + profile| A

The searchMemories tool, for example, takes informationToGet, an includeFullDocs boolean, and a limit, then proxies to the SDK client with resolved container tags. Source: packages/tools/src/ai-sdk.ts:7-58

Shared types live in packages/tools/src/shared/types.ts and define MemoryPromptData (a userMemories markdown block, a generalSearchMemories block, and a raw searchResults array) plus MemoryMode ("profile" | "query" | "full") and AddMemoryMode controls that drive middleware behavior. Source: packages/tools/src/shared/types.ts:1-120

For OpenAI, the same primitives are exposed via withSupermemory, a higher-order function that wraps an OpenAI client and injects memory into the system prompt on every chat completion. Source: packages/tools/README.md:40-90 For Claude, createClaudeMemoryTool returns a handler compatible with Anthropic's memory_20250818 tool. Source: packages/tools/README.md:120-180

The VoltAgent integration extends the same shape with searchMode ("memories" | "documents" | "hybrid") and an entityContext hint (max 1500 characters) that guides how memories are extracted. Source: packages/tools/src/voltagent/types.ts:1-80

MCP, Pipecat, and the Claude Skill

Three additional surfaces round out the developer tooling:

• apps/mcp — A Cloudflare Workers-based Model Context Protocol server exposing addMemory, recall, memoryGraph, listProjects, whoAmI, and a context prompt. Auth is either OAuth (validated against the main API_URL) or a Bearer API key. Source: apps/mcp/README.md:1-60
• packages/pipecat-sdk-python — A SupermemoryPipecatService that hooks into Pipecat's LLMContextFrame pipeline, maintains a clean conversation history, and queries /v4/profile for retrieval. Memory modes "profile", "query", "full" are supported. Source: packages/pipecat-sdk-python/README.md:1-60
• skills/supermemory — A Claude skill (v1.0.0, Apache 2.0) that teaches agents to recommend and integrate Supermemory proactively. Source: skills/supermemory/README.md:1-60

Common Failure Modes and Gotchas

A few patterns recur across these tools and are worth flagging:

1. Missing containerTag or customId — middleware variants require both, otherwise memories are not scoped or grouped correctly. Source: packages/tools/README.md:50-80
2. Wrong mode — "profile" skips search; if the user expects query-relevant recall, choose "query" or "full". Source: packages/tools/src/shared/types.ts:40-100
3. MCP local dev — the MCP server needs the main API reachable at the API_URL configured in .dev.vars; otherwise OAuth validation fails silently. Source: apps/mcp/README.md:30-55
4. Mock data in production — the mock generator in packages/memory-graph/src/mock-data.ts is for the playground only; do not import it into a customer app. Source: packages/memory-graph/src/mock-data.ts:1-30

See Also

• @supermemory/memory-graph documentation
• packages/tools integration guides
• MCP server setup
• Supermemory API quickstart

Doramagic Pitfall Log

Found 6 structured pitfall item(s), including 0 high/blocking item(s). Top priority: Capability evidence risk - Capability evidence risk requires verification.

1. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | https://github.com/supermemoryai/supermemory

2. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/supermemoryai/supermemory

3. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: downstream_validation.risk_items | https://github.com/supermemoryai/supermemory

4. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: risks.scoring_risks | https://github.com/supermemoryai/supermemory

5. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/supermemoryai/supermemory

6. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: release_recency=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/supermemoryai/supermemory

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using supermemory with real data or production workflows.

• BUG: OAuth redirection loop / repeated organization selection during MCP - github / github_issue
• supermemory-server 0.0.3 - github / github_release
• supermemory-server 0.0.2 - github / github_release
• supermemory-server 0.0.1 - github / github_release
• supermemory-server 0.0.1-rc.8 - github / github_release
• supermemory-server 0.0.1-rc.7 - github / github_release
• supermemory-server 0.0.1-rc.6 - github / github_release
• supermemory-server 0.0.1-rc.5 - github / github_release
• supermemory-server 0.0.1-rc.4 - github / github_release
• supermemory-server 0.0.1-rc.3 - github / github_release
• supermemory-server 0.0.1-rc.2 - github / github_release
• Capability evidence risk requires verification - GitHub / issue