Run Multiple AI Coding Agents With Shared Context
Run multiple AI coding agents with Mrrlin task orchestration, worktree-aware planning, evidence capture, and approval gates.
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.
Your agents, under one harness
One coding agent gives you output. Mrrlin gives you a harness.
Codex, Claude Code, and other agents can write. Mrrlin keeps the brief, routes each task, challenges weak work, and brings you in only for decisions that need you.
Read the workflow transcript
Without a harness, a person carries context between prompts, checks each output, follows up, and decides what happens next. With Mrrlin, one goal becomes a visible plan: work is routed, reviewed, retried when weak, and brought back only for the decisions that need a human.
Why it matters
A fourth agent does not help if every result still waits on you.
Mrrlin makes multiple coding agents easier to coordinate by keeping task scope, branch status, artifacts, and operator decisions in one place.
Workflow
Scaling past one agent without turning yourself into the queue.
Queue the work as tasks
Break the goal into tasks sized for one run each, with acceptance checks and dependencies. The Director can make the split from one sentence, and every task reports back into the same goal, so progress is measured on the plan rather than terminal by terminal.
Set the Autopilot policy
Choose a cadence, a daily job budget and the maximum number of runs allowed at once. Autopilot then advances work unattended inside those limits, halts anything that fails again and again, and leaves human-review tasks parked until you get to them.
Let runs proceed side by side
Every agent works in a worktree of its own, so four agents means four isolated changes rather than four hands in one directory. Artifacts accumulate on each run, and results that fall short go back to their agent with review notes.
Read results by exception
Your inbox receives only what needs a person: questions, approvals and handoffs. Work that passed review arrives with its receipt, work that failed is already back with its agent, and so adding agents does not automatically mean adding reading for you.
Use cases
Where it fits.
Parallel feature delivery
Queue one agent for the new content pages, one for analytics tracking events and one for QA, all under the same launch goal. Each reports into that goal's plan, so the progress view shows which of the three is done and which is waiting on another.
Regression testing
Pair every feature task with a QA task that it blocks. When a feature branch is ready, the QA agent runs the regression suite against it, attaches the test results to its run, and returns failures to the implementing agent along with the output.
Documentation refresh
Refreshing reference docs for twenty API endpoints splits cleanly into parallel tasks: each agent drafts one section from the code in its own worktree, a reviewer on another model checks the examples against the source, and the updated docs still wait for your approval before they publish.
Codebase migration
Moving a large component library from one styling approach to another splits into batches of a dozen components, every batch getting a worktree and pull request of its own. The migration rules sit in project memory, and a batch that keeps failing stops instead of consuming the night.
Comparison
Many agents on your attention, or many agents on a policy.
Keep reading
More on coordinating coding agents.
Start from the AI coding agent orchestration overview, or go deeper with the guides next to this one.
FAQ
Before you start.
Do we need a separate subscription for each agent we run?
Mrrlin runs agents on the Codex CLI and Claude Code plans your team already pays for, on your own hardware. How much parallel work a plan supports depends on that plan's own limits, which Mrrlin does not change. The Autopilot budget and concurrency cap let you run multiple AI coding agents within the usage you choose.
Do all the agents stall when several need my answer at once?
Only the tasks that are waiting on you. Their questions collect in one inbox, and you answer them when it suits you, across as many tasks as are waiting, while everything else keeps moving. You can also let the Director decide a question instead of answering it yourself, and the decision is recorded either way.
Can we run multiple AI coding agents overnight?
Overnight is where the policy earns its keep. Before leaving, set how many runs may execute at once and how many jobs the day may use; Autopilot works through the unblocked queue within those numbers and stops work on anything that fails repeatedly. Human-review tasks and deploys hold until you are back, so morning brings receipts plus a short list of decisions.
Orchestrate Claude Code & Codex CLI without terminal lockups.
Stop juggling disconnected CLI windows and resolving manual merge collisions. Mrrlin coordinates your agents in isolated git worktrees, collects passing test artifacts, and opens verified pull requests.
No credit card · No migration · One goal