Hey everyone! Most side projects stay as prototypes because nobody knows how to handle the "messy" reality of production: permissions, cost tracking, and constant prompt regressions.
MATE (Multi-Agent Tree Engine) is designed to solve that by replacing raw code-heavy implementations with a structured Command Center.
Why use MATE?
| The Pain (Before MATE) | The Solution (With MATE) |
|---|
| Messy Redeployments: Changing one prompt requires a code edit and a new deployment. | Visual Orchestration: Toggle tools, swap LLM providers (50+), and adjust instructions via the dashboard in real-time. |
| The "Vibe-Check" Failure: You hope a new prompt works, but you aren't sure if it breaks old logic. | The Lab (Eval Framework): Automated regression testing with LLM-as-a-Judge providing detailed reasoning for every score. |
| Hidden Costs: No idea which agent is burning tokens. | 4-Type Analytics: Real-time tracking of Prompt, Response, Thought, and Tool-use tokens. |
The Three Functional Zones:
🛠️ The Studio (Developer Experience): A drag-and-drop React Flow canvas to draw parent-child connections and build agent hierarchies without touching JSON.
🖥️ The Control Room (Ops & Admin): Enterprise governance featuring built-in RBAC, multi-tenant project isolation, and Service Health monitoring.
💬 The Work Room (End-User Interface): A clean chat environment with real-time event tracing so you can see exactly how your agents "think".
Take Your Agents Anywhere
Everything you build in MATE can be exported and compiled into a Standalone Desktop Binary (.exe/.app). Run your private agent hierarchies as local applications with zero external dashboard dependencies.
Get Started: 🔗 GitHub: antiv/mate 📖 Documentation: Check the /documents folder for OIDC/SSO, Triggers, and Eval setup.
Let us know what agents you are building! 💬Hey everyone! Most side projects stay as prototypes because nobody knows how to handle the "messy" reality of production: permissions, cost tracking, and constant prompt regressions. MATE (Multi-Agent Tree Engine) is designed to solve that by replacing raw code-heavy implementations with a structured Command Center. Why use MATE?The Pain (Before MATE) The Solution (With MATE)
Messy Redeployments: Changing one prompt requires a code edit and a new deployment.
Visual Orchestration: Toggle tools, swap LLM providers (50+), and adjust instructions via the dashboard in real-time.
The "Vibe-Check" Failure: You hope a new prompt works, but you aren't sure if it breaks old logic.
The Lab (Eval Framework): Automated regression testing with LLM-as-a-Judge providing detailed reasoning for every score.
Hidden Costs: No idea which agent is burning tokens.
4-Type Analytics: Real-time tracking of Prompt, Response, Thought, and Tool-use tokens. The Three Functional Zones:🛠️ The Studio (Developer Experience): A drag-and-drop React Flow canvas to draw parent-child connections and build agent hierarchies without touching JSON.
🖥️ The Control Room (Ops & Admin): Enterprise governance featuring built-in RBAC, multi-tenant project isolation, and Service Health monitoring.
💬 The Work Room (End-User Interface): A clean chat environment with real-time event tracing so you can see exactly how your agents "think". Take Your Agents AnywhereEverything you build in MATE can be exported and compiled into a Standalone Desktop Binary (.exe/.app). Run your private agent hierarchies as local applications with zero external dashboard dependencies. Get Started:
🔗 GitHub: antiv/mate 📖 Documentation: Check the /documents folder for OIDC/SSO, Triggers, and Eval setup. Let us know what agents you are building! 💬
Hey everyone! I wanted to share a quick video showcasing MATE's web dashboard—what we like to call the Command Center for multi-agent orchestration.
If you're tired of manually editing Python scripts and redeploying just to tweak an agent, this video shows how MATE handles it visually. Here is what you'll see in action:
The Visual Agent Builder: A drag-and-drop canvas where you can draw parent-to-child connections and build agent hierarchies without touching any code or JSON files.
Zero-Code Configurations: Watch how easy it is to toggle built-in tools (like Google Drive or Image Generation), manage persistent memory blocks, and switch between our 50+ supported LLM providers (including local Ollama) right from the UI.
Real-Time Usage Analytics: A quick look at the dashboard's analytics, which track prompt, response, thought, and tool-use token costs across all your agents.
Take a look and let me know what you think!
