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firstmate vs Preseason.ai

Which one should you try?

firstmate

Free

Best for Developers managing multiple simultaneous agent tasks

Visit firstmate See full review

Preseason.ai

Free

Best for Teams building with multiple AI coding agents who need objective comparison

Visit Preseason.ai See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

firstmate

firstmate

firstmate puts a single orchestrating agent — the 'first mate' — in front of you, while it spawns a crew of autonomous coding agents behind the scenes, each isolated in its own git worktree. You describe what needs doing; the crew splits the work in parallel and keeps collisions out of your main branch. The visible session backend means you can watch what each agent is doing without switching tabs. The architecture works cleanly for investigation tasks, parallel fixes, or supervised PR generation — the constraint is that there is no API surface, so anything requiring programmatic integration into an existing CI pipeline has to wire around the tool manually.

Preseason.ai

Preseason.ai

Orbit sits between your backlog and your coding agent, selecting one dependency-ordered task at a time, running the agent, then forcing the result through tests, lint, and type checks before marking the task done. Every run writes structured JSON artifacts — what the agent returned, how the output scored against a rubric, whether a human should accept or iterate — so you are reviewing evidence, not trusting a diff. The agent-neutral contract means you can run Claude, Codex, and Cursor against the same task and compare artifacts instead of impressions. The harness is intentionally minimal; it does not schedule, it does not host, and it does not manage secrets — which means the moment your workflow needs cross-repo coordination or cloud execution, you are writing the glue yourself.

AttributefirstmatePreseason.ai
PricingFreeFree
PriceFree (MIT); you pay for the coding agent it runs on—
Pricing checkedOct 19, 2026Not checked yet
Free trialNot statedNot stated
Open sourceYesYes
Has APINoNo
Self-hosted optionYesYes
PlatformsmacOS, Linux; runs inside Claude Code, Codex, Grok, Pi, OpenCode or Cursor Agent CLI (tmux by default)Linux, macOS, Windows (CLI/Python-based)
Pros
  • Crew-based parallel dispatch, so three investigation or fix tasks run simultaneously instead of sequentially — cutting the wall-clock time you'd spend babysitting separate agent sessions.
  • Per-agent git worktree isolation, which means parallel agents working on adjacent code do not produce mid-run merge conflicts that you have to untangle before any output is usable.
  • Visible session backend for the whole crew, so you can monitor what each agent is doing without switching terminals or losing track of which session held the failing test.
  • Self-hosted under MIT license with no paid features gated behind a tier, so teams with data-residency or audit requirements can deploy it without a vendor conversation.
  • Supervised agent loops with PR or report output as the end state, which means you review finished work rather than raw agent traces — keeping you in the loop at the decision point that matters.
  • Validation gates enforce proof before task completion, so a coding agent cannot mark a fix done while tests are still failing — which eliminates the silent regression problem that plagues unguarded agent loops.
  • Agent-neutral adapter contract means you can run Claude, Codex, and Cursor against identical tasks and compare structured evaluation artifacts, so you stop arguing about which agent is better and start looking at data.
  • Four machine-readable artifacts per orbit (agent result, evaluation, recommendation, progress log) give audit teams a complete, inspectable record of what the agent returned and how validation scored it — without relying on anyone's memory of what happened.
  • Dependency-ordered backlog selection keeps each agent run focused on one unblocked task, which means agents cannot start work that depends on incomplete prior steps — a failure mode that costs hours of untangling in unconstrained agent loops.
  • Deterministic replay with no API key required means you can verify the harness behavior itself in isolation, so debugging a broken validation run does not require burning API credits or standing up a live agent.
Cons
  • No API surface exists in the architecture, so teams that need to trigger agent crews from a CI system or external scheduler have to build shell-level integrations against a tool not designed for that pattern — and maintain that glue code themselves.
  • The crew model requires a human interacting with the first mate agent as the starting point; fully unattended, scheduled agent runs with no human in the dispatch loop are not a supported workflow, which is the condition under which teams move to an orchestration framework that exposes a programmatic entry point.
  • Community support through GitHub issues is the primary support channel — with 29 open issues noted on the repo — so teams encountering edge-case failures in production have no escalation path beyond the open-source community.
  • Orbit has no scheduler, no cloud execution layer, and no cross-repo awareness — the moment your workflow requires tasks that span more than one repository or need to run on remote infrastructure, you are assembling that plumbing yourself on top of the harness.
  • The adapter contract requires agents to speak JSON over CLI, so agents with browser-only or proprietary API interfaces need a wrapper built before they can run inside an orbit — that wrapper is not provided and is the team's responsibility to maintain.
  • Orbit has no built-in backlog management UI or integration with issue trackers; the backlog is whatever structured input you feed it, which means teams used to Jira or Linear-driven workflows will spend setup time before the first orbit runs.
  • Teams that need parallel agent execution — running multiple tasks simultaneously to cut wall-clock time on large backlogs — will hit the single-orbit-at-a-time model as a hard ceiling and switch to a purpose-built agent orchestration platform rather than extending Orbit.

Made up your mind?

firstmate

Free

Best for Developers managing multiple simultaneous agent tasks

Visit firstmate See full review

Preseason.ai

Free

Best for Teams building with multiple AI coding agents who need objective comparison

Visit Preseason.ai See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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