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Meldwork
Set the Goal. Let Agents Work Together.
Meldwork is a local-first multi-Agent collaboration workspace for macOS. Selected Agents share context, discuss approaches, carry out authorized work, and check results with less manual coordination. Users set goals and permissions and step in when information or key decisions are needed.
Hi Product Hunt — I’m Ryder, the maker of Meldwork.
Meldwork is a local-first desktop workspace for bringing the agent tools you already use into a shared task.
The question behind the product is simple: when several agents contribute to a decision, how do you keep the context, the evidence, and your own judgment together?
There are three ways to work:
Direct conversations for focused work with one agent.
Concurrent Responses for independent suggestions based on the same task snapshot.
Auto Discussion for proposals, cross-agent challenges, coordinated work, synthesis, and verification.
You choose the participants, provide the context, and review the result. The task keeps its contributions, supporting material, run state, and adoption decision together. Workspace writes are opt-in.
The demo uses a fictional launch plan so you can see the inputs and inspect the recommendations. Multiple agents can still miss things or agree on a weak assumption; review remains part of the work.
A few practical details: this is an Apple silicon macOS preview and requires compatible agent CLIs. Conversations and run records stay local, but the agents may send inputs to their configured model providers. The source is available for non-commercial use under the project license; commercial use needs prior written permission.
I’d love to hear where a second perspective is useful in your work. What would you want another agent to challenge before you adopt a result?
About Meldwork on Product Hunt
“Set the Goal. Let Agents Work Together.”
Meldwork was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #150 on the daily leaderboard. Meldwork is a local-first multi-Agent collaboration workspace for macOS. Selected Agents share context, discuss approaches, carry out authorized work, and check results with less manual coordination. Users set goals and permissions and step in when information or key decisions are needed.
On the analytics side, Meldwork competes within Productivity, Developer Tools, GitHub and Apple — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Meldwork performed against the three products that launched closest to it on the same day.
Who hunted Meldwork?
Meldwork was hunted by Ryder Sun. 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.
For a complete overview of Meldwork including community comment highlights and product details, visit the product overview.
Ryder Sun
Hi Product Hunt — I’m Ryder, the maker of Meldwork.
Meldwork is a local-first desktop workspace for bringing the agent tools you already use into a shared task.
The question behind the product is simple: when several agents contribute to a decision, how do you keep the context, the evidence, and your own judgment together?
There are three ways to work:
Direct conversations for focused work with one agent.
Concurrent Responses for independent suggestions based on the same task snapshot.
Auto Discussion for proposals, cross-agent challenges, coordinated work, synthesis, and verification.
You choose the participants, provide the context, and review the result. The task keeps its contributions, supporting material, run state, and adoption decision together. Workspace writes are opt-in.
The demo uses a fictional launch plan so you can see the inputs and inspect the recommendations. Multiple agents can still miss things or agree on a weak assumption; review remains part of the work.
A few practical details: this is an Apple silicon macOS preview and requires compatible agent CLIs. Conversations and run records stay local, but the agents may send inputs to their configured model providers. The source is available for non-commercial use under the project license; commercial use needs prior written permission.
I’d love to hear where a second perspective is useful in your work. What would you want another agent to challenge before you adopt a result?