Traycer is a free, open-source (MIT) desktop app for running coding agents in parallel on Mac, Windows and Linux, with iOS and Android apps. Run Claude Code, Codex, Cursor, OpenCode and 18 more agents on the subscriptions or API keys you already have, each in its own git worktree. Agents share a Task's specs and context, message and review each other, and you review every diff in one window. Free for individuals; Sync adds team sharing. Optional cloud sandboxes. SOC 2 Type 2.
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Hey Product Hunt! 👋 We’re building Traycer, the spec layer for coding agents.
We built Traycer after watching LLM‑assisted code “work once” and then fall apart in real repos. Traycer plans like a senior engineer, implements with your favorite agent, and verifies changes so you can ship with confidence.
Why we’re building this
Most agentic coding tools jump straight to “code.” That works for small snippets, but in real repos it’s easy to lose context, skip steps, or ship regressions. Traycer separates planning from execution, so your agents stay aligned with what actually needs to happen.
How it works
1. Specification first. You describe the change; Traycer creates phases and a concrete plan (what to touch, why, and in what order).
2. Execute anywhere. Hand off the plan to Cursor, Claude Code, Windsurf—whatever you already use. We don’t replace your agent; we upgrade it with a better spec.
3. Verify & iterate. When the agent proposes changes, Traycer verifies them against the plan, highlights gaps, detects regressions, and suggests corrections. Rinse and repeat until it’s solid.
What this means for you
* Less prompt wrangling, more predictable outcomes.
* Safer changes in large or unfamiliar codebases.
* A clear path from intent -> plan -> diff -> verification.
Who it’s for
Built for developers who want precision and control while harnessing the power of agentic coding.
We keep our roadmap public and would love your feedback. Drop ideas in the comments; we’re around to answer questions and ship improvements. 🚀
— The Traycer team
@tanveer_gill1 cool launch! I am a heavy user of @Claude Code. Would love to learn why I should consider Traycer! Claude Code has plan vs coding separation with Plan Mode.
@tanveer_gill1 Great product but I would like to understand how is it different from Kiro, because Kiro was published with the exact same intent. But the only problem I face with Kiro is that it is unnecessarily takes too much time to plan and then execute. Even after waiting for so long if it doesn't do the required thing, it feels like a big waste of time.
"AI code is fast; AI planning makes it deliberate."
Remember this discussion on the vibe coding process with @gabe: Is it best to jump straight to code or plan it out first?
It feels like @Traycer AI fixed it and pushed vibe coding to the next level. We're no longer supervisors using coding agents as junior developers, enhancing prompts and fine-tuning outputs. We're now conductors, guiding AI agents that scope, execute, review, and shape production-ready apps.
S/O to the team, keep up the great work 👏👏
@fmerian Incredible reflection—thank you!
We love how you put it: from supervisors to conductors. That shift is precisely what we hope to unlock: AI that collaborates with intent, not just speed.
Very cool. Sounds like a real safeguard for big projects. Does it integrate with GitHub PRs for validation?
@roozbehfirouz Thanks so much for the kind words! 🙏
You’re spot on — one of our main goals was to make collaboration on large projects smoother and safer. Right now, we support in-IDE review and validation workflows, but not GitHub PR reviews yet. Tools like @CodeRabbit are already doing an amazing job in that space.
@roozbehfirouz spot on. there are great products for rapid prototyping and small codebases. however, larger codebases are still a challenge for most of them.
@Traycer AI just fixed it.
Love this idea. I’ve been working on a similar problem. How are you handling context across multiple files or refactors? Is it persistent or regenerated per run?
@lak7 Glad to know that you liked the idea and have also been working around the same problem.
We have used different techniques to include only relevant files in the context and to make iterations on the proposed plans easier, enabling users to quickly generate a new version based on their changes.
@masikh yes, you can. In fact you can use any AI tool. Because they offer to simply click and run in claude in the terminal (or codex, gemini, etc) but also you can copy the prompt and execute it anywhere.
Finally, something that actually keeps agents on track! Separating planning from execution is superb..... my last LLM project broke because it skipped steps. Really curious how Traycer handles complex refactoring?
@cruise_chen Traycer plans in layers, keeping the user involved at every step so you can guide the agent in the right direction. The earlier you intervene, the smaller the drift from your intended outcome; that’s why Traycer’s workflow is built around tight feedback loops.
It begins with a conversational requirements-gathering phase, followed by a high-level task breakdown. From there, Traycer collaborates with you on each Phase, diving deep to generate detailed tactical specs, down to the exact files, classes, and functions that need to be added or modified.
Once the spec is finalized, Traycer hands it off to your execution agent (Claude Code, Cursor, etc.) to produce a predictable, structured changeset. Then Traycer takes back control to run a Verification flow, ensuring the implementation matches the spec and introduces no regressions. Any discrepancies found are automatically fed back to the agent for correction. Verification can be repeated as many times as needed until everything aligns perfectly.
Behind the scenes, Traycer ensures each Phase starts with the most relevant context and minimal noise. The prompts it sends to your agent include precisely what’s needed for the current sub-task and nothing extraneous. Large tasks often fail when crammed into a single context window, where recall deteriorates as the prompt fills with unrelated tokens. Traycer prevents that by breaking the work into clean, context-rich segments, preserving focus and quality throughout.
Love the plan-first approach! How does Traycer handle complex dependency updates in large codebases?