Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Token-lean memory for AI agents: a knowledge graph searched cheaply. Drop-in for server-memory.
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.
MEMORY_FILE_PATHNecesitas
¿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": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add memory-lean --env MEMORY_FILE_PATH=your-value -- npx -y memory-leanCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}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": {
"memory-lean": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}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.
MEMORY_FILE_PATH envEsta 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
El nombre en el registro se verificó con la cuenta de GitHub zamansheikh, dueña del repo
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 1 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 (1.5.0) llegó al registro hace 21 días.
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.
memory-lean
Long-term memory for AI coding agents that doesn't burn the context window.
A knowledge graph your agent searches cheaply, and a web UI where you can see and edit all of it.
Quick start · Web UI · How it works · Tools · Setup for 12 agents · Contributing
memory-lean is an MCP server that gives Claude Code, Codex, Cursor, MiMo Code and any other MCP client a memory that lasts between sessions: which projects exist, what depends on what, which version shipped, what is still pending, and which rules were learned the hard way. It is a drop-in replacement for the reference memory server (@modelcontextprotocol/server-memory), with the same file and tool names, but its answers are about 40 times smaller.
About 40× smaller answers. Search returns a ranked list of names and the lines that matched, not every entity in full, so the agent can afford to check its memory before every task.
A web UI for people. See the whole graph grouped by type, follow relations, and add, edit or delete anything. Agent writes show up live.
Drop-in replacement. Same memory.jsonl file and the same nine tools as the reference server. Switch in, and switch back whenever you like.
Works with every agent. Copy-paste setup for Claude Code, MiMo Code, Codex, Cursor, Copilot, Gemini CLI, Zed, JetBrains and more, plus the instructions that teach the agent to use it.
Stays small by itself. Short facts, a cap per entity, and an archive for the overflow, so the graph never grows into a wall of text.
Private and safe. Plain local files, daily backups, a write lock for parallel sessions, and no network access. One server file, zero dependencies.
Measured on a real graph of 237 entities, 355 relations and 1,160 facts (tokens ≈ characters ÷ 4):
| Call | Reference server | memory-lean | |
|---|---|---|---|
read_graph | ~77,800 tokens | ~2,000 tokens | 40× smaller |
search_nodes, average of 5 common words | ~31,000 tokens | ~770 tokens | 40× smaller |
A narrow search that matches one or two entities saves less, because there is little to trim. To measure your own graph, run node tools/bench.mjs from a clone; it is read-only and prints sizes, never content.
Claude Code: install the plugin. It adds the server, the instructions as a skill, a short reminder at the start of each session, and /memory-ui.
/plugin marketplace add zamansheikh/memory-lean
/plugin install memory-lean@memory-lean
Any other agent: add the server to its MCP config, then give it the instructions.
{
"mcpServers": { "memory": { "command": "npx", "args": ["-y", "memory-lean"] } } }
npx -y memory-lean --protocol >> AGENTS.md # into the rules file your agent reads
Just looking? Open the web UI on a made-up example graph:
npx -y memory-lean --ui --demo
Setup for each agent
| Agent | Server config | Instructions |
|---|---|---|
| Claude Code | plugin | included in the plugin |
| Xiaomi MiMo Code | mimocode.jsonc | skill or AGENTS.md |
| opencode | opencode.json | AGENTS.md |
| OpenAI Codex CLI | codex mcp add | AGENTS.md |
| Cursor | .cursor/mcp.json | AGENTS.md or a rule |
| VS Code (Copilot) | .vscode/mcp.json | copilot-instructions.md |
| Gemini CLI | settings.json | GEMINI.md |
| Windsurf | mcp_config.json | a rule |
| Cline | cline_mcp_settings.json | .clinerules/ |
| Zed | settings.json | AGENTS.md or .rules |
| JetBrains IDEs | AI Assistant settings | a rule or guidelines |
| Claude Desktop | claude_desktop_config.json | project instructions |
The graph is created on the first write at ~/.claude/memory-graph/memory.jsonl, whichever agent writes it, so all your agents share one memory. Set MEMORY_FILE_PATH to keep it somewhere else.
npx -y memory-lean --ui
This opens your memory in the browser. Everything an agent can do, you can do here:
Follow the connections. Select an entity to see its facts and relations. Find anything. Search names, types and facts at once.
Edit in place. With the same 300-character limit the agents have. Light or dark. It follows your system, or pick one.
The page runs on your machine only (127.0.0.1) and needs the one-time key in the link it prints, so other websites cannot read or change your memory. Nothing is uploaded anywhere.
The reference server answers a search with every matching entity in full, as pretty-printed JSON. memory-lean answers with a ranked list of names and the lines that matched:
> search_nodes { "query": "deploy" }
57 match "deploy", top 10 shown (narrow the query or raise limit). open_nodes for full entities.
The agent then opens only what it needs:
> open_nodes { "names": ["shop-backend"] }
shop-app -talks_to-> shop-backend PENDING shop-backend 2.4.0 -blocks-> shop-app
| Limit | What it means |
|---|---|
| Small answers | search_nodes returns at most 10 names with snippets, read_graph returns an index of names, and only open_nodes returns full entities. |
| Short facts | An observation longer than 300 characters is rejected, with a message telling the agent to split it or put the detail in the repo. |
| A cap per entity | An entity keeps 15 observations. Past that, the oldest dated ones move to archive/ .md. Nothing is deleted: they can be listed and restored, and undated facts (the identity facts) always stay. |
| Search that narrows | Every word must match somewhere in the entity, so adding a word narrows the result. The reference server looks for the query as one exact phrase. |
| Safeguard | What it means |
|---|---|
| Safe to share | Writers take a lock file, so several agent sessions can use one graph without overwriting each other. Every write replaces the file atomically. |
| Daily backups | A copy goes to backups/ once a day; the last 7 are kept. |
| Damaged lines are kept | A line that cannot be read is moved to memory.jsonl.unreadable on the next write and reported, never silently dropped. |
| Plain files | The graph is JSON lines, archives are Markdown, backups are copies. You can read, grep and edit all of it by hand. |
| Tool | What it returns |
|---|---|
search_nodes | Ranked list of entity names with counts and up to two matching snippets each. Every word must match the name, type, an observation, or the type of a relation touching the entity. |
open_nodes | The full entities you name, as compact text, with every relation touching them and their archive count. Unknown names come back with "did you mean" suggestions. |
read_graph | An index: entity names grouped by type, with observation counts. Never the observations. |
create_entities | Creates entities; existing names are skipped. |
create_relations | Creates relations between existing entities; duplicates are skipped. |
add_observations | Adds facts to an entity, enforcing the length limit and the per-entity cap. |
delete_observations | Removes observations by exact text. |
delete_relations | Removes specific relations. |
delete_entities | Removes entities and every relation touching them. |
rename_entity | Renames an entity, keeping its observations, relations and archive. *Not in the reference server.* |
| archived_observations | Lists what was archived for an entity, optionally filtered, and restores chosen fa
Solo se muestra el principio; lee el resto en el repo