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Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.
| Date | Stars |
|---|---|
| 2026-07-31 | 309 |
| 2026-08-06 | 312 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<h1 align="center">MegaMemory</h1>
</p>
<p align="center">Persistent project knowledge graph for coding agents.</p>
<p align="center">
<a href="https://www.npmjs.com/package/megamemory"><img alt="npm" src="https://img.shields.io/npm/v/megamemory?style=flat-square" /></a>
<a href="./LICENSE"><img alt="license" src="https://img.shields.io/npm/l/megamemory?style=flat-square" /></a>
<a href="https://nodejs.org"><img alt="node" src="https://img.shields.io/node/v/megamemory?style=flat-square" /></a>
<a href="https://www.npmjs.com/package/megamemory"><img alt="npm downloads" src="https://img.shields.io/npm/dm/megamemory?style=flat-square" /></a>
<a href="https://x.com/M3ruH4sh"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/M3ruH4sh?style=social" /></a>
</p>
<p align="center">
<img src="./assets/preview.png" alt="MegaMemory web explorer" width="800" />
</p>
---
An [MCP](https://modelcontextprotocol.io/) server that lets your coding agent build and query a graph of concepts, architecture, and decisions — so it **remembers across sessions**.
The LLM is the indexer. No AST parsing. No static analysis. Your agent reads code, writes concepts in its own words, and queries them before future tasks. The graph stores **concepts** — features, modules, patterns, decisions — not code symbols.
### The Loop
<p align="center">
<img src="./assets/how-it-works.svg" alt="How MegaMemory works" width="800" />
</p>
`understand → work → update`
1. **Session start** — agent calls `list_roots` to orient itself
2. **Before a task** — agent calls `understand` with a natural language query (or `get_concept` for exact ID lookup)
3. **After a task** — agent calls `create_concept` or `update_concept` to record what it built
Everything persists in a per-project SQLite database at `.megamemory/knowledge.db`.
---
### Installation
```bash
npm install -g megamemory
```
> [!NOTE]
> Requires Node.js >= 18. The embedding model (~23MB) downloads automatically on first use.
### Quick Start
```bash
megamemory install
```
Run the interactive installer and choose your editor:
> [!NOTE]
> The installer only updates config files after a successful read/merge, and it will not overwrite existing plugin/command files unless they are already marked as MegaMemory-managed.
#### With [opencode](https://opencode.ai)
```bash
megamemory install --target opencode
```
One command configures:
- MCP server in `~/.config/opencode/opencode.json`
- Workflow instructions in `~/.config/opencode/AGENTS.md`
- Skill tool plugin at `~/.config/opencode/tool/megamemory.ts`
- Bootstrap command `/user:bootstrap-memory` for initial graph population
- Save command `/user:save-memory` to persist session knowledge
Restart opencode after running install.
#### With [Claude Code](https://code.claude.com)
```bash
megamemory install --target claudecode
```
Configures:
- MCP server in `~/.claude.json`
- Workflow instructions in `~/.claude/CLAUDE.md`
- Commands in `~/.claude/commands/`
#### With [Antigravity](https://idx.google.com)
```bash
megamemory install --target antigravity
```
Configures:
- MCP server in `./mcp_config.json` (workspace-level)
#### With [Codex](https://openai.com/codex)
```bash
megamemory install --target codex
```
Configures:
- MCP server in `~/.codex/config.toml`
- Workflow instructions in `~/.codex/AGENTS.md`
#### With other MCP clients
Add megamemory as a stdio MCP server. The command is just `megamemory` (no arguments). It reads/writes `.megamemory/knowledge.db` relative to the working directory, or set `MEGAMEMORY_DB_PATH` to override.
```json
{
"megamemory": {
"type": "local",
"command": ["megamemory"],
"enabled": true
}
}
```
---
### MCP Tools
| Tool | Description |
|------|-------------|
| `understand` | Semantic search over the knowledge graph. Returns matched concepts with children, edges, and parent context. |
| `get_concept` | Look up a concept by its exact ID. Returns fullExcerpt of 9,017 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0e1531960065a1ea, topic:mcp, topic:mcp-server, desc:mcp server
matched fp:0e1531960065a1ea, topic:knowledge-graph, desc:knowledge graph