Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Token-lean memory for AI agents: a knowledge graph searched cheaply. Drop-in for server-memory.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
MEMORY_FILE_PATHYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}Run this in your terminal
claude mcp add memory-lean --env MEMORY_FILE_PATH=your-value -- npx -y memory-leanFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"memory-lean": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
MEMORY_FILE_PATH envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account zamansheikh, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (1.5.0) reached the registry 21 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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
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