Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Local-first, zero-knowledge AI memory across Claude, Cursor, and every MCP client. 0 RTT reads.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"sovseal-memory": {
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add sovseal-memory -- npx -y @sovseal/mcp-server프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"sovseal-memory": {
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"sovseal-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
게시자(bobybarack)가 저장소 소유자(sovseal)가 아니에요. 다른 사람이 등록했을 수 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 0개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.3.8)은 3개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Zero-knowledge, local-first AI memory for work you can't send to the cloud.
One private memory across Claude, ChatGPT, Cursor, and every MCP client — plaintext physically never leaves your machine.
@sovseal/mcp-server · @sovseal on npm · Quickstart · How it compares · Changelog
If you work with code, clients, or data you're not allowed to upload — healthcare, legal, fintech, defense, or anything under an NDA — every AI memory option today asks you to break that rule. Platform memory keeps your context on their cloud, in their walled garden, one platform at a time. Cloud memory layers route your raw conversations through third-party servers and extraction APIs.
sovseal takes the other path: all capture, embedding, and recall run on your device. Plaintext never crosses the network. The optional sync tier ships only AES-256-GCM ciphertext — the server cannot read your memories even if it wanted to. And because it speaks MCP and rides a browser extension, it's *one* memory across every AI you use, not six silos.
Cloud-hosted memory layers force a tradeoff between latency, privacy, and cost. Every recall is a 200–800 ms round-trip to someone else's database. sovseal collapses the tradeoff. LanceDB and Transformers.js run *inside* your agent's process.
| Workload | Operation | p50 | p95 | p99 | Network |
|---|---|---|---|---|---|
| 10K records · 1K queries | recall_memory (warm) | 6.1 ms | 10.4 ms | 21.8 ms | 0 RTT |
| Cold start | recall_memory (first call) | ~1.2 s | — | — | 0 RTT |
| Single write | store_memory | 3.8 ms | 7.2 ms | 12.5 ms | 0 RTT (write-behind) |
All benchmarks reproduce with: pnpm --filter @sovseal/mcp-server run bench-v2 (10K pre-seeded memories, 1K sequential queries, CPU-bound ONNX embeddings on commodity hardware).
What makes it fast and private:
sha256(canonicalize(payload)) and fails closed on mismatch.sovseal gives AI assistants and autonomous agents persistent, portable context that you actually own. It drops into any MCP-compatible client, captures from the major chat platforms via a browser extension, and keeps your plaintext where it belongs: on your hardware.
Core Capabilities:
Who it's for:
An honest map, because you'll ask anyway:
| Platform-native memory (ChatGPT / Claude) | Cloud memory layers (mem0, Zep, hosted MCP memories) | sovseal | |
|---|---|---|---|
| Where plaintext lives | Provider's cloud | Provider's cloud, or your servers (self-host = your ops) | Your device only |
| Extraction pipeline | Provider-internal | Typically an LLM API call on your raw text | On-device embeddings; no LLM in the loop |
| Works across platforms | No — each platform is a silo | Yes (via their cloud) | Yes — locally, via MCP + extension |
| Offline | No | No (cloud) / partial (self-host) | Yes |
| Sync model | Provider-controlled | Server reads your data | Server sees ciphertext only |
| Cost per recall | Subscription-gated | API/hosting cost | $0, local compute |
What you give up with sovseal, stated plainly: there's no cloud LLM doing clever extraction on your behalf (by design — that's the leak we exist to prevent), and you are responsible for your own key (see Threat Model). If neither of those matters to you and you live inside one platform, its native memory may be all you need. If they do matter, nothing else in this table does what the right-hand column does.
sovseal exposes one protocol with three delivery shapes. Pick by where your code runs:
| MCP Server | Browser Extension | Node SDK / Self-Hosted | |
|---|---|---|---|
| Best for | Claude Desktop/Code, Cursor, Windsurf, Zed, agent frameworks | ChatGPT, Claude.ai, Perplexity, Grok, Gemini, DeepSeek in the browser | In-process use in a Node/TS service; full data residency |
| Install | npx -y @sovseal/mcp-server | Chrome Web Store + local host installer | npm install @sovseal/sdk |
| Recall latency | 0 RTT (local LanceDB) | 0 RTT (Native Messaging to local engine) | 0 RTT (local LanceDB) |
| Cost | Free (Hobby) | Free (Hobby) | Paid (Starter or above for self-hosted sync) |
The default for AI coding assistants and any agent framework that speaks MCP. One server binary, one config snippet, every client.
Claude Desktop, Cursor, Windsurf, Zed — add to mcp.json or claude_desktop_config.json:
{
"mcpServers": { "sovseal-memory": { "command": "npx", "args": ["-y", "@sovseal/mcp-server"] } } }
One-line install for Claude Code:
claude mcp add sovseal -- npx -y @sovseal/mcp-server
System Prompt Snippet (Claude Desktop / Cursor Project Instructions): To ensure the AI natively uses the memory server, paste this into your custom instructions:
This session has persistent memory via sovseal. At conversation start, check the sovseal://context/recent resource. Whenever the user shares preferences, plans, or personal context, store it via store_memory.
For always-on autonomous agents, switch to HTTP/SSE transport so the sovseal process outlives a single tool invocation:
SOVSEAL_TRANSPORT=sse SOVSEAL_PORT=4040 npx -y @sovseal/mcp-server
For the AI platforms that don't speak MCP. The extension bridges ChatGPT, Claude.ai, Perplexity, Grok, Gemini, and DeepSeek to the same on-device engine over Native Messaging — capture-on-send as you chat, ⌘/Ctrl+M to recall anywhere, with per-site toggles and one-click delete.
Everything the extension captures lives in the same local database the MCP server reads — tell Claude.ai something once, and Claude Code already knows it.
When you're building a backend service and want to manage agent state persistence programmatically, import the Node SDK client. It performs end-to-end AES-256-GCM encryption client-side and replicates checkpoints to a sync endpoint you choose.
npm install @sovseal/sdk
import { AgentStateClient, CryptoService } from "@sovseal/sdk";
const client = new AgentStateClient({ endpoint: "https://your-project.supabase.co/functions/v1/v2-agent-state", apiKey: "sov_proj_your_project_uuid", });
const key = await CryptoService.generateAESKey();
// Client-side AES-GCM encrypted snapshot upload const receipt = await client.snapshot({ key, payload: { agent_id: "agent_a1b2c3", sequence_number: 0, parent_snapshot: null, policy_hash: "0000000000000000000000000000000000000000000000000000000000000000", timestamp: new Date().toISOString(), active_context: { project: "acme-migration", decisions: ["Postgres over Mongo — team expertise", "Cut scope: no SSO in v1"], open_question: "Client prefers staged rollout; confirm dates with their ops lead." } } });
// Restore the latest state snapshot const { receipt: latestReceipt, ciphertextUrl } = await client.restore({ agentId: "agent_a1b2c3", });
The replication endpoint is a Deno edge function — open source, deployable to any Supabase project for full data-residency control.
# From the repo root
supabase functions deploy v2-agent-state supabase db push # applies the agent_state_snapshots schema
export SOVSEAL_ENDPOINT="https:// .supabase.co/functions/v1/v2-agent-state"
Source: supabase/functions/v2-agent-state/.
All of these consume the same MCP server through their first-class MCP support. No sovseal-specific adapter is required:
| Framework | How it consumes MCP |
|---|---|
| ElizaOS | @fleek-platform/eliza-plugin-mcp (config-only, stdio or SSE) |
| Hermes Agent | Native MCP, stdio + remote HTTP |
| CrewAI | mcps=[...] field on the agent |
| LangGraph · LangChain | langchain-mcp-adapters (npm + PyPI) |
| Microsoft Agent Framework | Built-in MCP workbench |
| OpenAI Agents SDK | Native MCPServerStdio / MCPServerSse |
| OpenClaw | Native openclaw mcp consumer |
| Tool | Args | Behavior |
|---|---|---|
store_memory | { content: string } | Embed (384-dim, on-device) → write to local LanceDB → return. Ciphertext replication runs write-behind; nothing blocks. |
| recall_memory | `{ query: string, topK?:
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요