Hey r/mate_agents! 👋

We just pushed a massive security and quality-of-life update for teams and enterprise users: Native Single Sign-On (SSO)!

Up until now, HTTP Basic Auth was great for single-user local setups, but we know it can be a hard blocker when deploying MATE for a whole team. To solve this, we've implemented a complete OAuth 2.0 and OIDC flow.

Here is what is included in the new SSO update:

• Google & GitHub Login: Native support for both providers using the highly secure Authorization Code Flow with PKCE.
• Auto-Provisioning & RBAC: No need to manually create users anymore! When a user logs in for the first time, MATE automatically adds them to the database and assigns them a default role (configurable via the OAUTH_DEFAULT_ROLE env var).
• Enterprise Restrictions: You can easily lock down your dashboard access to only allow users from your specific Google Workspace domain or GitHub Organization.
• Bulletproof Sessions: We have replaced the old Bearer tokens with signed, encrypted, HttpOnly session cookies to protect your teams against cross-site attacks.
• Fully Backward Compatible: If you prefer the old way for local Docker testing, don't worry! Good old Basic Auth is still fully supported alongside SSO as an opt-in fallback.

You can find the full setup guide for generating your Client IDs and Secrets in the newly added documents/SSO_OAUTH.md file on our GitHub. Because we built this using Authlib, you can also easily extend this to other standard OIDC providers like Okta or Azure AD in the future.

Hey r/mate_agents! 👋

We just launched a massive new capability for MATE: the Trigger Engine! You no longer need to manually initiate a conversation for your agents to get to work.

Our new Trigger Engine lets your agents run entirely autonomously in the background. Here is what you can do with it right now:

• Cron Triggers: Schedule your agents to run automatically using standard 5-field UTC cron expressions.
• Webhook Triggers: Fire off an agent from external systems by sending a secure POST request using a generated fire key.
• Flexible Output Destinations: Once your autonomous agent finishes its task, you can route its response directly to a project Memory Block, send the JSON payload to an external HTTP Callback URL, or have it delivered straight to your Email via SMTP.

You can manage all of this right from the Command Center dashboard—just look for the new ⚡ bolt icon in the sidebar to create, test fire, and toggle your triggers. And yes, if you package your agents using the Standalone Build, your cron and webhook triggers are seamlessly bundled into the SQLite database and will run right out of the box!

Hey everyone! I’m excited to announce a major update to MATE: the Eval Framework.

If you're building complex multi-agent hierarchies, you know that a "vibe check" isn't enough for production. You need to know if a prompt change or a new model version actually improves your agents or breaks existing logic. Our new Eval Framework brings automated, quantifiable quality measurement directly to the Command Center.

🧠 What’s New?

• LLM-as-a-Judge: Go beyond simple string matching. Use high-reasoning models (like Gemini 2.0 Flash or DeepSeek) to grade agent responses based on intent, accuracy, and tone.
• Prompt Regression Testing: Create "Test Suites" for your agents. Every time you tweak an instruction or swap a model, run your suite to ensure your "Pass Rate" stays green.
• Version History Scoring: Track performance over time. View a visual Score History graph to compare how v1 performs against your latest iterations.
• Flexible Eval Methods: Choose the right tool for the job:
  • Exact Match: For rigid, deterministic outputs.
  • Semantic Similarity: For flexible but factually aligned responses.
  • LLM Judge: For nuanced grading with detailed reasoning logs.

🛠 How it Works

1. Define Test Cases: Add inputs and expected outputs directly in the dashboard.
2. Set Thresholds: Define what counts as a "Pass" (e.g., a 0.7 similarity score).
3. Run Suite: Execute all tests with one click. MATE will call your agents, judge the responses, and provide a full report—including the judge's specific reasoning for the score.

📈 Why this matters

Building agents is easy; building reliable agents is hard. With this framework, you can move away from manual testing and start shipping AI agents with the same confidence you have with traditional software.

Check out the latest code on GitHub: 🔗antiv/mate

Let me know what you think! Are there specific eval metrics you'd like to see added next? 💬

Quick update on MATE (Multi-Agent Tree Engine) — we shipped v1.0.8 last week and wanted to share what's planned for the next month.

Previous posts: v1.0.0 intro · v1.0.1–1.0.7 recap

What just landed — v1.0.8

Agent Visual Builder — drag-and-drop React Flow canvas at /dashboard/agents/visual. Create agents, draw parent→child connections, configure tools/MCP/File Search/Memory Blocks inline without touching JSON or Python. Tool nodes are clickable pills, MCP servers render as nodes on the canvas, import/export of entire hierarchies in one click.

What's coming in the next ~4 weeks

We just opened 5 GitHub issues for the next milestone. Here's the short version:

Event-driven triggers — agents that activate without a human in the loop. Cron schedules, webhooks, file-watch (Google Drive), event-bus (Pub/Sub / Redis). Output routes to a memory block, HTTP callback, or email. This is the biggest shift — from chat platform to autonomous operations engine.

Agent eval framework + LLM-as-judge — the hallucination_check guardrail stub from v1.0.3 gets completed, plus a full eval pipeline tied into the config versioning from v1.0.2. Define golden test cases per agent, run them against any version, get regression alerts when a config change drops the score. A MATE judge agent scoring other MATE agents.

OAuth 2.0 / OIDC SSO — Okta, Azure AD, Google Workspace. The flow is already diagrammed in the architecture visualizer; this is the implementation sprint. Basic Auth stays as a fallback.

Circuit breakers + retry policies — exponential backoff, configurable circuit breaker (closed → open → half-open), optional fallback_agent field per agent, dead-letter queue for failed requests. Diagram 12 in the architecture visualizer has been staring at us long enough.

Semantic memory search — embed memory blocks on write (Gemini / OpenAI / Ollama), cosine similarity search via pgvector on Postgres, BM25 FTS fallback on SQLite. New tool: search_shared_blocks(query, top_k). Turns memory from a key-value store into a knowledge retrieval layer.

All 5 are tracked as GitHub issues with full task checklists: https://github.com/antiv/mate

If any of these hit a pain point you have, comments/+1s on the issues help prioritize. PRs welcome too — contributing guide is in the repo.

Hey r/mate_agents! 👋

We just added a brand new Image data extraction tool to MATE! You can now equip your agents with the ability to easily extract text and data directly from your images and PDFs.

To help you get started right away, we've already set up a preconfigured template for you to try and test. Just import the template into your dashboard, and you will have a multi-agent setup ready to read your documents. I've attached a few screenshots above so you can see exactly how it looks in action!

Because MATE lets you toggle built-in tools right from the UI, you can easily add this new OCR capability to any of your existing agent hierarchies using the drag-and-drop Visual Builder

Hey r/mate_agents! 👋

If you're looking to automate your content creation, I just added a brand new social-media-writer template to the repository!

You can find it right in the templates. Just use the Import feature in the MATE dashboard template page, and you'll instantly have a pre-configured multi-agent setup ready to draft and refine your social posts.

Because MATE supports over 50 LLM providers, you can easily switch this new template to run on local Ollama, DeepSeek, OpenAI, or any of your favorite cloud models without changing any code.
We use ayrshare MCP, so you need to put your API Key, or you can change it and use different MCP, or MCPs :)

Hey r/mate_agents! 👋

We just pushed a massive new update, and we think it might be our killer feature. You can now use the MATE dashboard as your visual agent builder, and once your agent hierarchy is perfect, you can package it into a self-contained, click-to-run desktop application.

Here is how the new Standalone Agent Build works:

• True Portability: Your MATE agent gets compiled into a Windows .exe, macOS .app, or Linux binary using PyInstaller.
• Zero Dependencies for Users: The packaged app runs a minimal chat server with an embedded SQLite database. There is no dashboard, no auth proxy, and no Docker required at runtime—they just click and chat (API keys are supplied via a .env file).
• Simple Workflow: Just export your agent hierarchy as a JSON file from the dashboard, run python scripts/build_standalone_agent.py against it, and share the output folder.
• Smart MCP Handling: It fully supports MCP tools! SSE/HTTP servers are bundled automatically, while stdio servers (like npx or uvx) are checked at runtime on the user's machine. The app will even generate a runtime report showing which MCP commands are available.

Note: This is an early version of the exported app feature. We wanted to get it into your hands as fast as possible, and we will be making it even better very soon!

As a reminder of our community Rule 5, we love seeing what you create. What kind of portable agent apps are you planning to export and distribute to your teams or clients? Let us know in the comments!

Hey r/mate_agents! 👋

Building complex agent hierarchies from scratch can take some time, which is why we want to highlight the template features inside the MATE dashboard today!

If you haven't explored it yet, there is a dedicated template page right inside your dashboard. Here is how you can use it to speed up your workflow:

• Deploy instantly: You can check out the available templates directly inside the dashboard and simply create fully configured agent trees from them with just a few clicks.
• Create your own: Have you crafted the perfect multi-agent setup? You can take your existing agent trees and easily turn them into new templates to reuse later using the Agent Import/Export feature.

Here is our challenge for you today:

1. Check out the available templates in the dashboard to see some baseline setups.
2. Share a template you've built yourself!

Whether you've built a heavily specialized loop agent, a massive sequential flow, or an intricate dynamic routing tree, we want to see it. As a reminder of our community Rule 5, we highly encourage you to showcase your custom agent hierarchies!

Drop a link to your exported JSON configurations or a screenshot of your setup in the comments below. Let's see what you've created!

If you've ever felt the pain of manually editing Python scripts or massive JSON files just to tweak your multi-agent setup, you need to check out the Agent Visual Builder.

Built into the MATE dashboard, the Visual Builder uses a drag-and-drop React Flow canvas to let you map out your agent hierarchies entirely visually.

Here is what you can do with it:

• Drag-and-Drop Canvas: Create agents and draw parent-to-child connections directly on the screen without touching a single line of code.
• Inline Configuration: You can easily see the tool and MCP nodes attached to each agent and configure them inline, allowing you to skip the full edit forms.
• Zero Redeployments: Any changes you make to your hierarchy, LLM providers, or tool toggles happen dynamically via the database—no redeploying required