Use cases
How teams put Lanes to work: run coding agents in parallel, provision form backends on the fly, and spin up GPUs on demand.
Lanes Desktop
Manage multiple Claude Code sessions
Run several Claude Code sessions at once, each on its own branch, tracked on one board instead of a wall of terminal tabs.
Run AI coding agents in parallel
Give each task its own agent and run them at the same time, on isolated branches, from one board.
Git worktrees for AI agents
Why every agent should get its own git worktree, and how Lanes creates and cleans them up for you.
Run Claude Code and Codex side by side
Use the right agent for each task, or race them on the same one, without leaving your workspace.
Multi-agent coding orchestration
Plan work, dispatch agents, and let one agent spin up more, all from a single board with a local MCP server.
An issue board for AI agents
Plan, run, review, and ship agent work as cards moving through a board built for the full coding loop.
Run agent fleets on your Claude subscription
Lanes runs the official CLI in a real terminal, so parallel sessions draw from the plan you already pay for, not a separate meter.
Run agents on GitHub and Linear issues
Pull tickets straight onto the board, run an agent on each, and write results back as pull requests and comments.
A local, private AI coding workspace
Agents run on your machine with your own CLI auth. Nothing is proxied through our servers.
Loop engineering
Design loops that prompt your agents for you: drive the board over MCP, wait on session status, verify with diffs and tests, and drain a backlog into PRs.
Run coding agents on a local model
Point a session at a model running on your own machine. Lanes manages Ollama end to end and sizes the context window so a real agent prompt actually fits.
Point sessions at any model provider
Run Claude Code or Codex against a provider you host or buy: a box on your network, OpenRouter, z.ai GLM, or anything serving the same wire format. Lanes is never in the request path.
Run a fleet on mixed models
Provider and model are per-session settings, so one board can run a local model on the mechanical work and a frontier model on the hard issue, with the same isolation and the same review.
Keep your coding CLIs current
See whether Claude Code and Codex are installed, which version you are on, and where they came from. Updates run through the package manager that actually installed them.
Lanes Forms
Add a waitlist form without a backend
Launch a waitlist in minutes. One request gives you a live endpoint that captures signups, with no server to build or host.
Contact and support forms
Add a contact or support form that emails you every submission, with spam defense built in.
Booking and inquiry request forms
Capture booking and inquiry requests on any site, route them where your team works, and keep spam out.
Forms for prototypes and side projects
Ship the form part of a prototype in seconds. No backend, no signup, and claim it later if the idea sticks.
Feedback and survey forms
Collect feedback and survey responses into one place, with export, without building a backend.
Lead capture and newsletter signups
Turn any page into a lead capture or newsletter signup form, and route new contacts to your stack.
Let your AI agent provision a form
Your coding agent can create a live form endpoint mid-task over MCP, with no signup, and hand you a link to claim it.
Route submissions to email, Supabase, or webhooks
Send submissions where you work: email and Lanes-hosted storage today, with webhook and Supabase delivery on the roadmap.
Lanes Compute
Train models on on-demand GPUs
Spin up the GPUs a training run needs, from a single A100 to multi-node H100 clusters, and pay only while it runs.
Fine-tune LLMs
Fine-tune open models on the right GPU with your own stack, from a quick LoRA run to a full multi-node job.
Run inference and model serving
Serve models on GPUs sized to your traffic, with your own serving stack, close to your data.
Spin up multi-node H100 clusters
Get a multi-node H100 cluster with InfiniBand and NVLink for large training and fine-tuning runs.
Batch processing and experiments
Run batch jobs and one-off experiments on GPUs you spin up for the task and shut down after.
GPUs for agent workloads
Give your agents GPU compute they can call on: run models, tools, and pipelines on hardware that scales with the work.
Bring your own stack
Run PyTorch, JAX, vLLM, and your own containers on Lanes Compute. No forced framework, no lock-in.
Right-size every job and pay per second
Match the GPU to the job, from L40S to H200, and pay per second so you never fund idle hardware.