Murmell

Munder Difflin is a free, open-source desktop multi-agent harness that runs multiple terminal-based AI coding agents as coordinated local agents. It wraps agents including Claude Code, OpenAI Codex, Gemini CLI, Qwen, OpenCode, GitHub Copilot CLI, and custom commands, working with users' existing subscriptions and hourly usage limits. Each agent runs as a real node-pty process and is rendered through xterm.js, while an Electron and React interface displays sessions as avatars on a Pixi.js office floor. A GOD orchestrator called Michael assigns and routes work, adjudicates agent messages, and escalates spending, destructive operations, scope changes, and other configured approvals. Agents coordinate through a local Git-backed hive of plain-file memory, atomic mailboxes, a shared blackboard, and an append-only event log; a markdown-first semantic memory layer supports recall across sessions. Optional Git worktrees isolate parallel agents.

ai-memory is an open-source local server for long-term memory and session handoff among AI coding agents. It captures prompts, tool calls, and agent context through MCP integrations and lifecycle hooks, storing the material in a searchable, Git-backed Markdown wiki. When a session ends or is explicitly finalized, it generates handoff context for a subsequent session, including the project architecture, failed approaches, and open questions, so work can continue across agent vendors without restating the context; clients without a true session-end hook use explicit finalization commands. The project documents integrations for Claude Code, Codex, Command Code, Devin CLI, OpenCode, Cursor, Gemini CLI, Oh My Pi, Pi, and Crush, with agent-specific configuration, generated plugins or extensions, and capture exclusions. It runs on Linux, macOS, and Windows through WSL2, with experimental native Windows support, and is distributed through Docker images and native release binaries.

mu (μ) is a coding agent with a judgment kernel, built on pi and AionUi. A small judge handles bounded decisions around context admission, command risk and approval, task framing, memory, completion, drift, browser actions, and multi-agent coordination, while the main model performs the coding work. Each decision can be active, shadow, or off and can use Jev, the local Laya judge, or another LLM; verdicts, probabilities, and timings are recorded in a local ledger that can be inspected from the command line or desktop app. The system admits tool output chunk by chunk, archives or removes stale context, applies rule-based safety checks before judgment, and uses a shared board to route findings among non-editing sub-agents. It provides an npm-installed command-line interface and a native desktop app, supports model-provider connections and importing Claude Code or Codex conversations, and states that it is in early development with no release yet; names, settings, and formats may change.