Multi-Agent Coding Agents Without Losing Context
Coordinate multi-agent coding agents with task boundaries, project memory, review checkpoints, and evidence in Mrrlin.
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
Several agents on one backlog multiply output and collisions alike.
Mrrlin helps teams split coding work across agents while keeping context, status, blockers, and approval points visible.
Workflow
Dividing one backlog among several agents without collisions.
Split by role, not by tab
The Director breaks the goal into tasks and gives each a single assignee: copy, implementation, QA or documentation. Status is on the board where every agent can read it, so a ticket that is already held looks taken before a second agent starts on it.
Wire the dependencies
QA is blocked by implementation, documentation is blocked by the merged API change, and the page build waits for approved copy. Nothing starts before its input exists, which removes the placeholder-text problem at planning time rather than in review.
Mix models on purpose
Give each role the model that suits it: Claude Code on components, Codex CLI on the API, a cheaper model on routine checks. Review comes from a vendor other than the author's, so one model's habits are not checked only by that same model.
Converge on one goal
Every task rolls up into one plan with visible progress. Each needs its own receipt to close, blockers surface as inbox questions, and the goal counts as finished when its tasks are, not when the most confident agent says so.
Use cases
Where it fits.
Parallel landing-page work
Rebuild a product landing page with three agents at once: one on hero and section copy, one on the feature grid component, one on signup form validation. Each gets a separate branch but shares the goal, and the plan shows which pieces are finished and which are waiting.
Design-to-code tasks
The spec in project memory describes the new settings screen: layout, breakpoints, empty states and error states. The implementing agent builds from it, a reviewer checks the result against those acceptance checks, and a missing error state goes back with a note instead of reaching you as done.
QA and regression checks
A QA task blocked by the implementation starts only when there is a change to test. It runs the regression suite on that branch, stores the test results as artifacts, and a failure sends the implementation back with the failing output rather than a vague report.
Documentation updates
After the API rename merges, the documentation task unblocks. Its agent reads the recorded decision, including why the endpoint changed, and updates the README and changelog to match, so the docs explain the reason as well as the new name.
Comparison
Several agents in separate tabs, or several agents on one plan.
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.
How many multi-agent coding agents should a small team start with?
Start with the roles that already cause handoffs, usually implementation plus QA, and add copy or documentation once those two run cleanly. The Autopilot policy caps concurrent runs and sets a daily job budget, so the setup can widen gradually instead of meeting the limits of your subscriptions all at once.
Do all the agents need to run the same model?
No, and there is a good reason not to. Mrrlin routes work by kind: repository changes to Codex CLI or Claude Code, routine checks to cheaper models, and review to a provider outside the executor's. Mixing models means one model's habits get checked by another's, which is much of the point of running multi-agent coding agents at all.
What keeps two agents from editing the same files at once?
Two layers. Every run gets a separate git worktree, so edits stay apart until review. When two tasks touch the same module, a blocked-by link makes the second wait for the first to land. Any remaining overlap shows up when the reviewer reads each diff, before anything reaches your main branch.
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