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MCP verified MIT Self-run

MegaMemory

mcp-0xk3vin-megamemory · by 0xK3vin

Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.

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Install

$ agentstack add mcp-0xk3vin-megamemory

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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3mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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How agent discovery & health will work →
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About

MegaMemory

Persistent project knowledge graph for coding agents.


An MCP 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

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

npm install -g megamemory

> [!NOTE] > Requires Node.js >= 18. The embedding model (~23MB) downloads automatically on first use.

Quick Start

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
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
megamemory install --target claudecode

Configures:

  • MCP server in ~/.claude.json
  • Workflow instructions in ~/.claude/CLAUDE.md
  • Commands in ~/.claude/commands/
With Antigravity
megamemory install --target antigravity

Configures:

  • MCP server in ./mcp_config.json (workspace-level)
With Codex
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.

{
  "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 full context including children, edges, incoming edges, and parent. | | create_concept | Add a new concept with optional edges and file references. | | update_concept | Update fields on an existing concept. Regenerates embeddings automatically. | | link | Create a typed relationship between two concepts. | | remove_concept | Soft-delete a concept with a reason. History preserved. | | list_roots | List all top-level concepts with direct children. | | list_conflicts | List unresolved merge conflicts grouped by merge group. | | resolve_conflict | Resolve a merge conflict by providing verified, correct content based on the current codebase. |

Concept kinds: feature · module · pattern · config · decision · component

Relationship types: connects_to · depends_on · implements · calls · configured_by

Knowledge Graph


Web Explorer

Visualize the knowledge graph in your browser:

megamemory serve
  • Nodes are colored by kind and sized by edge count
  • Dashed edges show parent-child links; solid edges show relationships
  • Click any node to inspect summary, files, and edges
  • Search supports highlight/dim filtering
  • If port 4321 is taken, you'll be prompted to pick another
megamemory serve --port 8080   # custom port

How It Works

src/
  index.ts       CLI entry + MCP server (9 tools)
  tools.ts       Tool handlers (understand, get_concept, create, update, link, remove, list_conflicts, resolve_conflict)
  db.ts          SQLite persistence (libsql, WAL mode, schema v3)
  embeddings.ts  In-process embeddings (all-MiniLM-L6-v2, 384 dims)
  merge.ts       Two-way merge engine for knowledge.db files
  merge-cli.ts   CLI handlers for merge, conflicts, resolve commands
  types.ts       TypeScript types
  cli-utils.ts   Colored output + interactive prompts
  install.ts     multi-target installer (opencode, Claude Code, Antigravity, Codex)
  web.ts         HTTP server for graph explorer
plugin/
  megamemory.ts  Opencode skill tool plugin
commands/
  bootstrap-memory.md  /user command for initial population
  save-memory.md       /user command to save session knowledge
web/
  index.html     Single-file graph visualization (d3-force + Canvas)
  • Embeddings — In-process via Xenova/all-MiniLM-L6-v2 (ONNX, quantized). No API keys, and no network calls after the first model download.
  • Storage — SQLite with WAL mode, soft-delete history, and schema migrations (currently v3).
  • Search — Brute-force cosine similarity over node embeddings; fast enough for graphs with --keep left # keep the left version

megamemory resolve --keep right # keep the right version megamemory resolve --keep both # keep both as separate concepts


#### AI-assisted resolution

When an AI agent runs `/merge`, it calls `list_conflicts`, verifies both versions against current source files, then calls `resolve_conflict` with `resolved: {summary, why?, file_refs?}` plus a verification `reason`. It does not pick a side blindly; it resolves to what the codebase currently reflects.

---

### License

[MIT](./LICENSE)

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [0xK3vin](https://github.com/0xK3vin)
- **Source:** [0xK3vin/MegaMemory](https://github.com/0xK3vin/MegaMemory)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.