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JubarteAI
One shared brain for your coding agents
One shared brain for your coding agents. When one agent learns something — a bug fix, a config quirk, an API gotcha — every agent on your team knows. Works with Cursor, Claude Code, Codex, Windsurf, Zed, and any MCP client.
I started building this because I was tired of explaining the same project context to AI coding agents over and over again. Every new session meant reloading the model with decisions, tradeoffs, architecture details, bugs, and “why we chose this path” context that had already been figured out during previous iterations.
The problem got even clearer when working with other people. My agent had no idea what a teammate’s agent had learned, decided, or discussed in their own session. Important reasoning stayed locked inside one person’s chat. That creates a kind of “one person per ticket” lock-in, where collaboration is limited not by the team, but by the agent’s missing context.
I’ve seen how powerful the right context can be for coding agents — and how much better they become when that context compounds over time.
So I built JubarteAI: a memory and coordination layer for AI coding agents.
With JubarteAI, agents can remember important decisions across sessions, share knowledge across teammates, and communicate with each other in real time. Instead of isolated agents working in separate silos, you get a connected fleet that can collaborate around the same repo, branches, and decisions.
A few things that make it different:
Shared agent memory. Decisions, context, bugs, and implementation logic do not disappear when a session ends.
Team-wide context. One teammate’s agent can benefit from what another teammate’s agent already learned.
Branch-aware knowledge. Lessons stay scoped to where they matter, and PR merges can promote branch knowledge to main.
Live agent coordination. See which agents are active, what they are touching, and let them collaborate in real time.
Works with your stack. Claude Code, Codex, Cursor, VS Code, Windsurf, Zed, or any MCP client. Setup takes about two minutes.
The free tier is 1 repo, 3 agents, no card required, ever. Pro is $50/year.
I’d love feedback on two things:
Where does agent coordination break down for you today?
What context do you wish your agents remembered or shared with each other?
I’ll be here all day replying to every comment. Thanks for checking it out. 🐋
— Alamo Saravali
About JubarteAI on Product Hunt
“One shared brain for your coding agents”
JubarteAI was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #16 on the daily leaderboard. One shared brain for your coding agents. When one agent learns something — a bug fix, a config quirk, an API gotcha — every agent on your team knows. Works with Cursor, Claude Code, Codex, Windsurf, Zed, and any MCP client.
On the analytics side, JubarteAI competes within Software Engineering, Artificial Intelligence and Tech — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how JubarteAI performed against the three products that launched closest to it on the same day.
Who hunted JubarteAI?
JubarteAI was hunted by Alamo Saravali. 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 JubarteAI including community comment highlights and product details, visit the product overview.
Alamo Saravali
Hey Product Hunt! 👋
I’m Alamo Saravali, the maker behind JubarteAI.
I started building this because I was tired of explaining the same project context to AI coding agents over and over again. Every new session meant reloading the model with decisions, tradeoffs, architecture details, bugs, and “why we chose this path” context that had already been figured out during previous iterations.
The problem got even clearer when working with other people. My agent had no idea what a teammate’s agent had learned, decided, or discussed in their own session. Important reasoning stayed locked inside one person’s chat. That creates a kind of “one person per ticket” lock-in, where collaboration is limited not by the team, but by the agent’s missing context.
I’ve seen how powerful the right context can be for coding agents — and how much better they become when that context compounds over time.
So I built JubarteAI: a memory and coordination layer for AI coding agents.
With JubarteAI, agents can remember important decisions across sessions, share knowledge across teammates, and communicate with each other in real time. Instead of isolated agents working in separate silos, you get a connected fleet that can collaborate around the same repo, branches, and decisions.
A few things that make it different:
Shared agent memory. Decisions, context, bugs, and implementation logic do not disappear when a session ends.
Team-wide context. One teammate’s agent can benefit from what another teammate’s agent already learned.
Branch-aware knowledge. Lessons stay scoped to where they matter, and PR merges can promote branch knowledge to main.
Live agent coordination. See which agents are active, what they are touching, and let them collaborate in real time.
Works with your stack. Claude Code, Codex, Cursor, VS Code, Windsurf, Zed, or any MCP client. Setup takes about two minutes.
The free tier is 1 repo, 3 agents, no card required, ever. Pro is $50/year.
I’d love feedback on two things:
Where does agent coordination break down for you today?
What context do you wish your agents remembered or shared with each other?
I’ll be here all day replying to every comment. Thanks for checking it out. 🐋
— Alamo Saravali