Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Local-first, zero-knowledge AI memory across Claude, Cursor, and every MCP client. 0 RTT reads.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"sovseal-memory": {
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add sovseal-memory -- npx -y @sovseal/mcp-serverPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"sovseal-memory": {
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"sovseal-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@sovseal/mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
Quien lo publicó (bobybarack) no es el dueño del repo (sovseal); puede que otra persona lo haya listado
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 0 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.3.8) llegó al registro hace 3 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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?:
Solo se muestra el principio; lee el resto en el repo