Desktop app for Codex, Claude Code and routers
A free desktop app that works with Codex, Claude Code and any model router, with live diffs, a terminal and memory.
guides · practical context
Muster Agent guides and comparisons for open source AI agent frameworks.
Short, engineering-first notes on agent memory boundaries, MCP tool safety, and ERPNext context — plus honest side-by-sides against OpenClaw, Hermes Agent, and QM.
Muster Agent desktop app
Using Muster Agent with your tools.
Practical pages on running Codex, Claude Code, Paperclip, OmniRoute and long-term memory in the free, open-source Muster Agent desktop app.
Run Claude Code and Codex in one app
Pick the engine per chat, keep one memory and review every edit in the same diff view.
Work locally on Paperclip tasks
Check a Paperclip task out to your computer, do the work in your folders and hand back a receipt.
AI coding agent with long-term memory
Personal and per-folder notes, a recall preview before you send, and full control to edit or delete.
OmniRoute desktop client
Use an OpenAI-compatible router as a provider, with its models and routes available per chat.
Start here
Four useful entry points.
What is an agent harness?
Why production agents need memory scope, token accounting, tool policy, and evals.
MCP agent harness with token visibility
How MCP servers become safer when routed through policy, result caps, and ledgers.
Frappe AI with DocType-aware retrieval
Why ERP agents need DocTypes, fields, workflows, roles, and permissions in context.
Building an AI agent with memory
How tenant, workspace, user, and session boundaries prevent useful memory from becoming risky.
Comparisons
Choosing between agent frameworks.
Written to be useful rather than flattering — each one names what the other project does better before making Muster's case.
Muster vs OpenClaw
387.8k stars and a plugin economy against a receipted, observed edit trail. Both MIT, both self-hosted.
Muster vs Hermes Agent
Seven execution backends and 40+ tools against scoped memory boundaries and eval-gated learning.
Muster vs QM
The nearest neighbour: grant-based sharing and durable sandboxes against deterministic receipts.
Live inline diff evidence
The measurement behind the claim: 0 of 5 self-reported edits versus an 86ms observed diff.