This product was not featured by Product Hunt yet.
It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).

Product Thumbnail

Metis Agent

Metis is a coding agent that boosts performance by 50%

Open Source
Developer Tools
Artificial Intelligence
GitHub
Visit WebsiteSee on Product HuntGithub

Hunted byhu Oliverhu Oliver

Metis is an MIT-licensed coding agent runtime for terminal and desktop. Unlike thin model wrappers, it focuses on the agent harness: durable memory, Plan/Build workflows, recursive named agents, verification gates, and recoverable sessions. In a controlled Terminal-Bench 2.1 run using the same DeepSeek V4 Flash model and budget, Metis solved 73/89 tasks vs 60/89 with OpenCode.

Top comment

Hey Product Hunt ,I’m the maker of Metis. I started building it after noticing that the same capable model could produce very different coding results depending on the agent around it. Models were improving quickly, but long-running work still failed in familiar ways: context disappeared, execution started before the problem was understood, and a plausible answer was often treated as a finished, verified result. I wanted an agent runtime built around a simple loop: search, remember, execute, and verify. Metis began as a terminal-first tool. Over time it grew into explicit Plan and Build workflows, durable memory and recoverable sessions, recursive named agents with controlled tool access, and verification gates. I later added the React Desktop app so plans, subagents, questions, model usage, and execution progress are easier to inspect instead of being hidden inside a terminal log. The result that encouraged me most came from a controlled Terminal-Bench 2.1 run. Using the same DeepSeek V4 Flash model, version, environment, task inputs, and budget, Metis solved 73 of 89 tasks (82.02%), compared with 60 of 89 (67.42%) using OpenCode. That is one benchmark, not a claim that Metis universally makes every model better. I see it as evidence that agent architecture matters—and as a result worth reproducing, challenging, and breaking down feature by feature. Metis is MIT licensed, and I’d love feedback from people building with coding agents. What would help you evaluate it most: exact benchmark configs, per-task traces, cost and token comparisons, or orchestration ablations? Thanks for checking it out. Issues, independent benchmark runs, and contributions are especially welcome

Comment highlights

No comment highlights available yet. Please check back later!

About Metis Agent on Product Hunt

Metis is a coding agent that boosts performance by 50%

Metis Agent was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #27 on the daily leaderboard. Metis is an MIT-licensed coding agent runtime for terminal and desktop. Unlike thin model wrappers, it focuses on the agent harness: durable memory, Plan/Build workflows, recursive named agents, verification gates, and recoverable sessions. In a controlled Terminal-Bench 2.1 run using the same DeepSeek V4 Flash model and budget, Metis solved 73/89 tasks vs 60/89 with OpenCode.

Metis Agent was featured in Open Source (68.8k followers), Developer Tools (519k followers), Artificial Intelligence (478.1k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 246.4k products, making this a competitive space to launch in.

Who hunted Metis Agent?

Metis Agent was hunted by hu Oliver. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

Want to see how Metis Agent stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.