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Iron-mem

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SUMMARY

Persistent session memory for AI coding assistants. IronMem silently records your coding sessions, compresses them into concise memories via Claude API, and injects context into your next session automatically. Works with Claude Code, Cursor, Copilot, and Windsurf. Local-first single Rust binary.

README.md

IronMem
IRON-MEM

Persistent memory for AI coding assistants.

Stop re-explaining your project every time you start a new session.

Install • How It Works • CLI • Multi-Provider • Contributing

Built with Rust MCP Native SQLite + Postgres Docker Ready License Stars Issues CI

Claude Desktop Claude Code Cursor ChatGPT Desktop Copilot Windsurf Zed

What's New in v0.3.0

Now multi-provider, cross-platform, with a built-in web UI.

  • Multi-provider compression — use OpenAI, Google Gemini, or Anthropic as your LLM. Set "provider": "openai" in settings.
  • Neovim plugin — native Lua plugin with auto session lifecycle, :IronMemSearch, :IronMemStatus
  • Windows support — install.ps1, platform-aware messages, robust home directory detection
  • Web UI — browse, search, and delete memories at http://localhost:37778/ui
  • Discovery tools — list known projects, search across all projects, and inspect per-project session history
  • Still zero telemetry. Still local-first. Your data stays yours.
v0.2.0
  • 13 MCP tools — session_start, session_end, record_event, compress_session, get_context, get_status, list_memories, search_memories, search_global, list_projects, list_sessions, inject_context, wipe_project
  • Dual database — SQLite (local, FTS5 full-text search) + Postgres (self-hosted, tsvector) via DATABASE_URL
  • Every MCP client — Claude Desktop, Claude Code, Cursor, Windsurf, ChatGPT Desktop, Zed, and more
  • Docker deployment — docker-compose up for remote/team setups with Postgres
  • ironmem mcp — new subcommand for direct MCP stdio transport (Claude Desktop/Code)
  • REST server still works — existing hooks and curl-based workflows unaffected

IronMem gives AI coding tools persistent memory across sessions.
It silently records what happened during your session, compresses it into concise memory, and injects that context into your next session automatically.

No copy-pasting.
No rebuilding context from scratch.
No "remember when we refactored auth yesterday?"

Works with every major AI coding tool — Claude Code, Claude Desktop, Cursor, Windsurf, ChatGPT Desktop, GitHub Copilot, Zed, VS Code, and any MCP-compatible client.

Compress with the LLM you already pay for — Anthropic Claude, OpenAI GPT-4o, or Google Gemini. Switch providers with one config change.

Free and open source. Runs locally or on your own infrastructure. No telemetry. No cloud dependency. No subscription. SQLite or Postgres storage. Plain markdown output. Single Rust binary.

IronMem in action

Why this exists

AI coding tools are great inside a session and terrible across sessions.
They help you ship faster, but every fresh session forgets your architecture decisions, debugging trail, and what changed yesterday.

IronMem fixes the handoff.

Before vs after

Without IronMem:

"We already changed the auth middleware, switched to JWT, updated the migration, and fixed the failing tests in billing. Let me explain the whole thing again."

With IronMem:

Open a new session. Your assistant already has the recent project context.

Quick Start

  1. Install IronMem:
    curl -fsSL https://raw.githubusercontent.com/BMC-INC/Iron-mem/main/install.sh | bash
  2. Add your API key (in your ~/.zshrc or ~/.bashrc):
    export ANTHROPIC_API_KEY="your-key-here"
  3. Start coding! IronMem handles the rest silently in the background.

Table of Contents

The Problem

Every time you start a new session with Claude Code, Cursor, Copilot, or any AI coding assistant — it starts from zero. It doesn't know what you built yesterday. It doesn't know what broke. It doesn't know what you decided.

You end up re-explaining context every single session.

The Fix

IronMem silently records what happens during your coding session, compresses it into a concise memory using your LLM provider of choice (Anthropic, OpenAI, or Gemini), and injects that context into your next session automatically.

No setup per session. No copy-pasting. No "remember when we..."

IronMem in action

Without IronMem: "Hey Claude, remember yesterday we refactored the auth middleware and switched to JWT? And the database migration for the users table? And..."

With IronMem: You open a new session. It already knows.

Who Should Use This?

IronMem is designed for:

  • Developers frustrated with re-explaining context to AI tools every single session.
  • Teams working on large, multi-session projects where context gets easily lost.
  • Developers frequently switching between multiple AI tools like Copilot, Claude Code, Windsurf, or Cursor.
  • Solo developers who want to maintain flow and continuity without manual effort.

How It Works

flowchart LR
    A["🟢 Session Start"] -->|inject memories| B["📄 IRONMEM.md"]
    B --> C["🤖 AI reads context"]
    C --> D["🔧 You code"]
    D -->|every tool call| E["🗄️ SQLite"]
    D --> F["🔴 Session End"]
    F -->|compress via LLM| G["🧠 Memory"]
    G -->|next session| A

    style A fill:#22c55e,color:#fff,stroke:none
    style F fill:#ef4444,color:#fff,stroke:none
    style G fill:#8b5cf6,color:#fff,stroke:none
    style E fill:#0ea5e9,color:#fff,stroke:none

Everything runs locally. Your data stays on your machine.

Install

curl -fsSL https://raw.githubusercontent.com/BMC-INC/Iron-mem/main/install.sh | bash

Or clone and build manually:

git clone https://github.com/BMC-INC/Iron-mem.git
cd Iron-mem
chmod +x install.sh
./install.sh

Windows:

git clone https://github.com/BMC-INC/Iron-mem.git
cd Iron-mem
powershell -ExecutionPolicy Bypass -File install.ps1

Add to your shell profile (~/.zshrc or ~/.bashrc):

export PATH="$HOME/.ironmem/bin:$PATH"
export ANTHROPIC_API_KEY="your-key-here"

Restart your terminal and Claude Code. That's it.

Requirements: Rust/Cargo (the installer will tell you if it's missing)

CLI

ironmem server              # Start REST + MCP SSE server
ironmem mcp                 # Start MCP stdio server (for Claude Desktop/Code)
ironmem serve               # Start SSE server with bearer token auth
ironmem serve --public      # Same + Cloudflare Tunnel for remote MCP clients
ironmem serve --public --no-auth  # Authless public tunnel for claude.ai personal use
ironmem status              # Health check + DB stats
ironmem projects            # All projects with stored memories
ironmem list                # Recent memories for current project
ironmem search "auth middleware"  # Hybrid (keyword + semantic) search across memories
ironmem search-global "auth middleware"  # Search across all projects
ironmem sessions            # Session history for current project
ironmem inject              # Manually rebuild IRONMEM.md (relevance-ranked)
ironmem compress <id>       # Manually compress a session
ironmem embed               # Backfill semantic embeddings for existing memories
ironmem wipe                # Delete all memories for current project
ironmem config              # Print current settings

ironmem list

ironmem search

Multi-Provider Support

IronMem works as an MCP server (native integration) or via IRONMEM.md (plain markdown, universal):

Platform MCP Native IRONMEM.md Setup
Claude Code Yes Yes Setup →
Claude Desktop Yes Yes Setup →
claude.ai Yes Yes Setup →
Cursor Yes Yes Setup →
Windsurf Yes Yes Setup →
ChatGPT Desktop Yes — Setup →
Zed Yes — Setup →
VS Code (Copilot/Continue/Cline) Yes Yes Setup →
Any MCP Client Yes — stdio or SSE transport
Any AI Tool — Yes Read IRONMEM.md as project context

MCP Setup

IronMem supports two MCP transports:

  • stdio — for local clients that launch the server themselves (Claude Code, Claude Desktop, Cursor)
  • Streamable HTTP — for remote/cloud clients that connect over HTTP. Uses standard request/response and bearer-token auth, so it works through tunnels and reverse proxies for clients that support static bearer tokens.

Once connected over MCP, IronMem now supports project discovery directly:

  • list_projects — show every project with stored memories
  • search_global — search across all projects
  • list_sessions — inspect session history inside a project

Claude Code MCP Setup

Claude Code connects via stdio — it launches ironmem mcp directly.

Option A: CLI (recommended)

claude mcp add ironmem -- ~/.ironmem/bin/ironmem mcp

Option B: Project .mcp.json (share with your team)

Create .mcp.json in your project root:

{
  "mcpServers": {
    "ironmem": {
      "command": "~/.ironmem/bin/ironmem",
      "args": ["mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here"
      }
    }
  }
}

Note: Claude Code hooks (installed by install.sh) and MCP can coexist. The hooks use the REST API for automatic observation recording; MCP gives you direct tool access. You can use both, or just one.

Claude Desktop MCP Setup

Claude Desktop also connects via stdio.

Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "ironmem": {
      "command": "/Users/YOU/.ironmem/bin/ironmem",
      "args": ["mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here"
      }
    }
  }
}

Replace /Users/YOU with your actual home directory path. Restart Claude Desktop after saving.

claude.ai (Web)

claude.ai runs in the cloud, so it cannot reach localhost.

IronMem is a local-first tool. The recommended setup for full MCP access is Claude Code or Claude Desktop using stdio.

Anthropic's current claude.ai custom connector UI supports authless and OAuth-based remote MCP servers, but not a manual static bearer-token field. For personal use, the honest compatibility path is an authless ephemeral tunnel:

ironmem serve --public --no-auth

That command does three things:

  1. Starts the SSE server with no auth
  2. Launches a Cloudflare Tunnel (free, no account needed) to expose it publicly
  3. Prints the public URL
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  IronMem SSE Server
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Local:  http://127.0.0.1:37779/mcp
  Auth:   Disabled (--no-auth)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Public URL: https://xxx-yyy-zzz.trycloudflare.com
  Remote MCP setup:
    URL:   https://xxx-yyy-zzz.trycloudflare.com/mcp
    Auth:  None
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

In claude.ai:

  1. Open Settings
  2. Open Integrations
  3. Choose Add custom connector
  4. Set Name to IronMem
  5. Paste the printed https://...trycloudflare.com/mcp URL into Remote MCP server URL
  6. Leave the OAuth fields blank

The trycloudflare.com URL is ephemeral and changes whenever you restart the public tunnel, so update your connector URL each time you relaunch ironmem serve --public --no-auth.

This is no longer local-only. The tunnel exposes your MCP endpoint over the internet for as long as it is running.

For a personal local tool, this tradeoff is often acceptable because the URL is short-lived and changes on each restart. Still, use --no-auth deliberately and only when you understand that you are trading security for compatibility.

Without --no-auth: ironmem serve and ironmem serve --public use bearer-token auth for clients that support static bearer tokens.

Requirements: Install cloudflared for best results (brew install cloudflared on macOS, winget install Cloudflare.cloudflared on Windows). Falls back to npx cloudflared if not installed.

Cursor / Windsurf MCP Setup

Both use stdio. Add to your MCP settings:

Cursor: Settings → MCP → Add Server

Windsurf: Settings → MCP → Add Server

{
  "ironmem": {
    "command": "~/.ironmem/bin/ironmem",
    "args": ["mcp"]
  }
}

Other MCP Clients

Any MCP client that supports stdio transport can use IronMem:

{
  "command": "~/.ironmem/bin/ironmem",
  "args": ["mcp"]
}

For clients that support Streamable HTTP, start the server and point the client at http://localhost:37779/mcp:

ironmem serve

Neovim Plugin

IronMem includes a native Neovim plugin that communicates via MCP stdio.

Install with lazy.nvim:

{
  "BMC-INC/Iron-mem",
  config = function()
    require("ironmem").setup({
      -- binary = "~/.ironmem/bin/ironmem",  -- default
      -- auto_start = true,   -- session_start on VimEnter
      -- auto_end = true,     -- session_end on VimLeavePre
      -- record_events = true, -- record buffer writes
    })
  end,
}

Commands:

Command Description
:IronMemStart Manually start a session
:IronMemEnd End session and compress
:IronMemStatus Show database stats
:IronMemSearch <query> Search memories in a split buffer

Web UI

When the REST server is running, a built-in memory browser is available at:

http://localhost:37778/ui

The UI shows sessions, memories, and database stats. You can browse, search, and delete memories directly from the browser.

Configuration

~/.ironmem/settings.json:

{
  "port": 37778,
  "provider": "anthropic",
  "model": "claude-sonnet-4-6-20250627",
  "inject_limit": 5,
  "max_observation_bytes": 2048,
  "db_path": "/Users/you/.ironmem/mem.db",
  "database_url": null,
  "mcp_transport": "stdio",
  "mcp_sse_port": 37779,
  "auth_token": null,
  "embedding": {
    "provider": "auto",
    "model": null,
    "ollama_url": "http://localhost:11434",
    "weights": { "relevance": 0.5, "recency": 0.3, "importance": 0.2 },
    "recency_half_life_days": 30
  }
}

All fields optional. Sensible defaults provided. auth_token is generated automatically the first time you run ironmem serve without --no-auth. The embedding block is optional — omit it entirely and IronMem behaves exactly as before (keyword-only search, recency injection).

Semantic Search & Embeddings

IronMem can blend keyword (FTS) and semantic (vector) retrieval using Reciprocal Rank Fusion, and rank session-start injection by a blend of relevance + recency + importance. Embeddings are stored locally in SQLite via sqlite-vec (or pgvector on Postgres); nothing is sent anywhere unless you explicitly choose an API provider.

Privacy posture: this is a governance tool, so the default is local-first / no data egress. The auto provider prefers a local embedder and silently degrades to keyword-only search if none is available — it never phones home and never hard-fails a command.

embedding.provider Behavior Data egress
"auto" (default) Use local Ollama if reachable, else the built-in ONNX model (if compiled in), else keyword-only None (unless only an API key is configured)
"ollama" Local Ollama embeddings None (localhost)
"onnx" In-process ONNX model (requires --features local-onnx build) None
"openai" OpenAI embeddings API Sends memory text to OpenAI
"google" Google embeddings API Sends memory text to Google
"none" Keyword-only FTS + recency injection (legacy behavior) None

Recommended local setup (no egress):

# Install Ollama, then pull an embedding model:
ollama pull nomic-embed-text
# IronMem's "auto" provider will detect and use it automatically.

Built-in ONNX (no Ollama, no network): compile with the optional feature so embeddings run fully in-process:

cargo install --path . --features local-onnx

Blend weights (embedding.weights) control session-start injection ranking: relevance (semantic match to your current git context), recency (true half-life decay set by recency_half_life_days), and importance (an LLM-assigned 1–10 score per memory). They need not sum to 1.

Backfill existing memories — after enabling embeddings, index memories created before:

ironmem embed              # embed memories missing a vector (all projects)
ironmem embed --project .  # scope to one project
ironmem embed --force      # rebuild the whole index from scratch

Provider

IronMem supports three LLM providers for session compression:

Provider provider value Default model API key env var
Anthropic "anthropic" claude-sonnet-4-6-20250627 ANTHROPIC_API_KEY
OpenAI "openai" gpt-4o OPENAI_API_KEY
Google Gemini "google" gemini-2.0-flash GOOGLE_API_KEY

To switch providers, set "provider" in ~/.ironmem/settings.json and ensure the corresponding API key is set. The model field overrides the provider's default model.

Environment Variables

Variable Default Description
DATABASE_URL (none) Postgres URL. Overrides db_path when set.
IRONMEM_MCP_TRANSPORT stdio MCP transport: stdio or sse
ANTHROPIC_API_KEY (none) Required when provider is anthropic (default)
OPENAI_API_KEY (none) Required when provider is openai
GOOGLE_API_KEY (none) Required when provider is google

API Key

IronMem needs an LLM API key to compress session observations into memories. Set the environment variable for your chosen provider (ANTHROPIC_API_KEY, OPENAI_API_KEY, or GOOGLE_API_KEY).

For the default Anthropic provider, IronMem also checks ~/.ironmem/api_key as a fallback. This file is auto-created by the session-start hook because the IronMem server runs as a background process via nohup, and some environments strip environment variables from child processes. The hook persists your API key with chmod 600 permissions so the server can always access it.

Troubleshooting

Server not starting:

ironmem status                           # Check if server responds
cat ~/.ironmem/server.log                # Check server logs
~/.ironmem/bin/ironmem server            # Run manually to see errors

Observations not being recorded:

ironmem status                           # Check observation count
sqlite3 ~/.ironmem/mem.db "SELECT count(*) FROM observations;"

If count stays at 0, your hooks may not be installed. Re-run ./install.sh or check that ~/.claude/hooks/post-tool-use.sh exists and is executable.

Compression failing (memories always 0):

# Check if the API key is accessible
cat ~/.ironmem/api_key                   # Should contain your key
echo $ANTHROPIC_API_KEY                  # Should be set

# Try manual compression
ironmem compress <session-id>            # Get session ID from server.log

Hooks not firing:
Check that ~/.claude/settings.json has the hooks registered under the "hooks" key. Re-running ./install.sh will fix this.

Architecture

~/.ironmem/
├── bin/ironmem          # Single compiled binary
├── mem.db               # SQLite database (FTS5 full-text search)
├── settings.json        # Configuration
├── api_key              # Anthropic API key (auto-persisted, chmod 600)
├── current_session      # Active session ID (ephemeral)
└── server.log           # Worker logs

~/.claude/hooks/         # Auto-installed Claude Code hooks
├── session-start.sh     # Injects memories on session start
├── post-tool-use.sh     # Records every tool call
├── stop.sh              # Triggers compression
└── session-end.sh       # Cleanup

~2,400 lines of Rust. MCP-native. SQLite or Postgres. One binary. No external runtimes.

Why Rust?

Rust was chosen for IronMem to deliver:

  • Maximum Performance: Minimal overhead and lightning-fast execution, essential for a tool that hooks into every single CLI command.
  • Zero Dependencies: Compiles down to a single binary. No need to install Python, Node.js, or complex runtime environments.
  • Memory Safety & Reliability: Guaranteed safety without a garbage collector ensures the background worker remains rock-solid and leak-free.

Design Principles

  • Zero friction — hooks run silently, never interrupt your workflow
  • Local-first — runs on your machine by default, your data stays yours
  • MCP-native — speaks the protocol every major AI client is adopting
  • Provider-agnostic — MCP for native integration, plain markdown for everything else
  • Self-hostable — Docker + Postgres for team deployments, still zero cloud dependencies
  • Fail-safe — if IronMem crashes, your coding session is unaffected

Who this is for

IronMem is for developers who use AI coding tools heavily and want continuity across sessions.

It is especially useful if you:

  • switch between Claude Code, Cursor, Copilot, or Windsurf
  • work on projects that span many sessions
  • are tired of re-explaining architecture, bugs, and recent changes
  • want local-first memory instead of a hosted service

Who this is not for

IronMem is not trying to be:

  • a generic memory platform for every kind of agent
  • a cloud sync product
  • a team knowledge base
  • a dashboard-heavy workflow tool

It solves one narrow problem well: session memory for AI coding workflows.

Why not just use CLAUDE.md?

CLAUDE.md is great for static project context — things like "use tabs not spaces" or "we use Axum for routing." But it's manual. You write it, you maintain it, and it doesn't know what happened last session.

IronMem is automatic and session-aware:

CLAUDE.md IronMem
Updates You write it manually Auto-generated from session activity
Scope Static project rules Dynamic session history
Rotation You manage it Old memories age out automatically
Search Ctrl+F Full-text search across all sessions
Effort High Zero — hooks handle everything

They work together. CLAUDE.md holds your project rules. IronMem holds what happened.

Docker Deployment

Run IronMem with Postgres for team/remote setups:

ANTHROPIC_API_KEY=your-key docker-compose up --build

This starts IronMem with Streamable HTTP on http://localhost:37779/mcp and Postgres 16, plus the REST server on http://localhost:37778.

Roadmap

Shipped in v0.2.0

  • MCP-native server (stdio + Streamable HTTP)
  • Dual database — SQLite (local, FTS5) + Postgres (self-hosted)
  • Docker deployment with Postgres
  • Bearer token authentication
  • ironmem serve --public with Cloudflare Tunnel for remote MCP clients
  • ironmem serve --public --no-auth for claude.ai personal use
  • Works with Claude Code, Claude Desktop, Cursor, Windsurf, ChatGPT Desktop, Zed, VS Code

Shipped in v0.3.0

  • Multi-provider compression — OpenAI, Google Gemini, or Anthropic (configurable via provider in settings)
  • Neovim plugin (nvim/lua/ironmem/) — auto session lifecycle, :IronMemSearch, :IronMemStatus
  • Windows native support — install.ps1, platform-aware install messages, robust home dir detection
  • Web UI memory browser — http://localhost:37778/ui when REST server is running

Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a PR.

TL;DR: Open an issue first. Bug fixes and provider compatibility improvements are always welcome. We don't accept changes that add runtime dependencies or complexity.

⭐ Support

If you find IronMem useful, please consider giving it a star! 🌟
This helps others discover the project and motivates further development.
Contributions, issues, and feature requests are also highly welcome.

License

Apache-2.0 © 2026 ExecLayer Inc

Maintainer: James Benton

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