Stoneforge vs Shep vs Mrrlin: AI Agent Workflow Comparison
Compare Stoneforge, Shep, and Mrrlin for AI coding agents, workflow orchestration, team visibility, review gates, and evidence.
Chat tools vs Mrrlin
The execution layer beats another blank chat window.
Chat tools make you re-send context and manage the work. Mrrlin keeps project memory and spends tokens deliberately.
Why it matters
Run one bake-off across three tools and score what they leave behind.
Mrrlin is the choice when the team wants the execution workspace around agents: tasks, specs, inbox, runs, evidence, and approvals.
Workflow
The same two workflows, run through each option in turn.
Choose two real workflows
Pick one coding task and one marketing task you would ship anyway: add CSV export to the reports screen, then write the release note and the customer email about it. Using real work keeps every option honest about context, review, and follow-up.
Run each option the same way
Give each tool the same brief, the same repository, and the same acceptance checks. In Mrrlin, the Director turns the brief into tasks and questions, reviewers converge on the spec, and Claude Code or Codex CLI executes in an isolated worktree.
Score what each leaves behind
After each run, ask a teammate who was not involved to answer three questions from the record alone: what was the goal, what was checked, and what is still open. Wherever the record is thin, the gap shows up within minutes.
Test the approval moment
The release email is the real test. In Mrrlin, sending to a real contact is an always-ask action, so the draft waits in the inbox with reviewer notes on its claims. Check how each option handles that moment before you adopt anything.
Use cases
Where it fits.
Tool comparison
A shared scorecard keeps the comparison fair: the same two workflows, the same acceptance checks, and the same questions for each tool. Mrrlin's column gets filled from its runs and artifacts, and every other column from whatever that tool records.
Team adoption planning
Before rolling out, decide who sets model routing, which tasks run on auto and which need human review, and which actions always ask first. In Mrrlin those rules live in Settings and on each task, so a new teammate inherits them on day one.
Coding-agent operations
For repository work, look at how each run is contained and recorded. Mrrlin gives each run its own worktree and records branch, base commit, provider, test results, and a diff summary, so parallel agents work apart from your checkout.
Workflow governance
Governance means knowing what agents may touch outside the workspace. Mrrlin reaches your repository, site, or messaging only through grants, one per destination, with reversibility recorded and every call logged, so an audit after rollout starts from a record instead of recollection.
Comparison
Picking a tool from a demo versus from a real bake-off.
Keep reading
More on choosing the right tool.
Start from the all tool comparisons overview, or go deeper with the guides next to this one.
FAQ
Before you start.
Which should we choose in a Stoneforge vs Shep vs Mrrlin decision?
It depends on the shape you need. Stoneforge-style tooling may fit if the priority is the AI coding or execution tool itself. Shep may fit a team that lives in the terminal and wants CLI-first agent workflows. Mrrlin fits when the team wants tasks, specs, inbox, runs, evidence, and approvals in one workspace around the agents.
Do we need to drop our current agents to try Mrrlin?
No. Mrrlin runs Claude Code and Codex CLI on your machine through a local bridge, under your own subscriptions, and never holds a model credential. Reviewers come from other providers, such as Gemini. A trial mostly changes where the plan, the review, and the approvals live, not which agents write the code.
What makes a Stoneforge vs Shep vs Mrrlin bake-off fair?
Use identical inputs: the same brief, repository, acceptance checks, and reviewer. Score only what you can inspect afterward, such as the spec, run records, review notes, and approval trail. Leave out anything you would have to take on trust, including claims about roadmaps, pricing, or speed.
Tell us the outcome you want AI to execute.
Share the workflow you want to automate. We’ll map the first Mrrlin run — plan, agent routing, review loops, and approval checkpoints.
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