Multi-Agent Tree Engine
The AI infrastructure layer that lets your company ship production-grade AI services to clients — without rebuilding the engine from scratch.
72 ⭐ · SSO, Evals & Triggers — all
shipped.
01 — The Challenge
Your clients want AI. Building it right is hard.
Months to first deployment
Building multi-agent systems from scratch means months of infra work before the first real feature ships.
Enterprise requirements slow you down
Compliance, RBAC, audit trails, PII handling — each one adds weeks of custom engineering.
Code changes for every config
Every agent tweak means a code change, redeploy, and a new release cycle. Clients can't self-serve.
No visibility into AI costs
Token spend sprawls across agents with no per-project budget controls or usage analytics.
02 — Solution
MATE: Your AI orchestration platform
MATE is a production-ready multi-agent engine built on Google ADK. You deploy it once, then configure agents, tools, and workflows through a web dashboard — no code changes.
Hierarchical agent trees — route complex tasks automatically
Root agent delegates to specialized sub-agents
50+ LLM providers, switchable per agent
GPT-4, Gemini, Claude, Ollama — any model, any agent
Self-building agents
Agents create and update other agents at runtime via conversation
MATE Capabilities
Production features, out of the box
Work Room
Default landing page for chat with any agent. Persistent sessions, streaming responses (SSE), and markdown rendering.
Code Canvas
Built-in editor that automatically executes HTML/JS/CSS/SVG in-browser, Python via WASM, and Flutter/Dart via DartPad.
SSO - Google & GitHub
Native OAuth 2.0 / OIDC login with PKCE. Auto-provisions users, assigns RBAC roles, and enforces domain restrictions for enterprise.
Eval Framework
Test suites per agent: exact match, semantic similarity, and LLM-as-Judge. Automatic webhook regression alerts if score drops.
Trigger Engine
Cron, webhook, and file-watch triggers execute agents fully autonomously. Output routes to memory blocks, HTTP, or email.
Guardrails & Safety
PII redaction, prompt injection detection, and LLM-as-judge hallucination scoring with custom threshold and fail-open controls.
EU AI Act Audit Trail
Audit logs for all config changes, RBAC denials, and logins. Append-only structure and JSON/CSV export for compliance reporting.
Visual Agent Builder
Drag-and-drop React Flow canvas. Create hierarchies, connect tools, MCP, and memory blocks, all inline with JSON import/export.
Embeddable Widget
One script tag on any site. Widget Admin panel for non-technical teams: edit greetings, themes, colors, and RAG files.
Template Library
Pre-built configs: Customer Support, Research Assistant, Code Reviewer, Content Writer. One-click import creates full project.
Versioning & Rollback
Every agent change snapshots to history. Monaco diff editor to visually review differences and rollback to any version.
OTel Tracing & Budgets
OpenTelemetry trace visibility per step, LLM call, and tool. Per-user, agent, or project token budgets with warn/throttle controls.
04 — Business Example
Real-world scenario: SaaS customer support
🏢 Client: Mid-size SaaS company
Wants AI-powered customer support without hiring an AI team
01
You deploy MATE for the client
One Docker Compose. Done in a day. No custom code written.
02
Configure agents in the dashboard
Routing agent → support agent (with RAG on their docs) → escalation agent.
03
Embed widget or use Work Room
One script tag on client's site. Or teams use the Work Room directly in the dashboard. Both live in minutes.
04
Client manages it via widget admin panel
No-code: update agent instructions, manage docs, tweak behavior — no developer needed.
05
You monitor via OpenTelemetry + audit logs
SLA visibility, compliance ready, cost controlled per project.
Time to first working demo
1 day
vs. 3–6 months building agent infra from scratch
Enterprise compliance
Built-in
EU AI Act audit trail, PII redaction, RBAC — no custom work
Client self-service
100%
Clients update their AI agents without ever calling your dev team
Scalability
Multi-tenant
Same MATE instance serves N clients with full project isolation
Positioning & Comparison
Built for delivery teams, not just developers
| Capability | LangChain / LangGraph | CrewAI | AutoGen | MATE |
|---|---|---|---|---|
| No-code agent config | ✗ | ✗ | ✗ | ✓ Dashboard |
| Embeddable chat widget | ✗ | ✗ | ✗ | ✓ 1 script tag |
| EU AI Act audit trail | ✗ | ✗ | ✗ | ✓ Built-in |
| Multi-tenant RBAC | ⚡ DIY | ⚡ DIY | ⚡ DIY | ✓ Built-in |
| Token budget controls | ✗ | ✗ | ✗ | ✓ Per project |
| Config versioning + rollback | ✗ | ✗ | ✗ | ✓ Monaco diff |
| MCP + A2A protocols | ⚡ MCP only | ✗ | ✗ | ✓ Both |
| Standalone binary deploy | ✗ | ✗ | ✗ | ✓ .exe / .app |
| Google / GitHub SSO | ✗ | ✗ | ✗ | ✓ Built-in |
| Agent eval framework | ⚡ DIY | ✗ | ✗ | ✓ Built-in |
| Autonomous triggers (cron) | ✗ | ✗ | ✗ | ✓ Built-in |
From zero to running in one afternoon
STEP 01
Clone & Configure
Fork the open-source repo. Set your LLM API keys and database. Docker Compose up.
STEP 02
Design Agent Tree
Use the Visual Builder to create your agent hierarchy. Drag, connect, configure — no code.
STEP 03
Embed or Integrate
Add the widget script to client's site, or call the REST API / MCP endpoint from your app.
STEP 04
Monitor & Iterate
Use traces, audit logs, and token analytics. Clients update their own agents without calling developers.
# Full setup in 3 commands
git clone https://github.com/antiv/mate.git && cd mate
cp .env.example .env # set API keys
docker-compose up -d # → open http://localhost:8000 (admin/mate)
git clone https://github.com/antiv/mate.git && cd mate
cp .env.example .env # set API keys
docker-compose up -d # → open http://localhost:8000 (admin/mate)
System Architecture
Three layers, clean separation of concerns
Client Layer
Work Room (dashboard
chat)
Chat Widget
(iframe)
REST API
MCP Client (Claude,
Cursor)
A2A Agent
SSO (Google /
GitHub)
↕
MATE Core — Auth Server
:8000
Auth + RBAC +
SSO/OIDC
Dashboard API
Widget Keys
MCP Manager
Audit Logger (EU AI
Act)
Rate Limiter + Token
Budget
Guardrails + Hallucination
Check
Trigger Engine
(cron/webhook)
Eval Framework
↕
ADK Execution — ADK
Server :8001
Agent Manager
Tool Factory
Session Manager
50+ LLM Providers via
LiteLLM
Memory Blocks + RAG
A2A Protocol
OTel Tracing
Code Executor (Python
WASM)
↕
Data Layer
PostgreSQL / MySQL /
SQLite
Auto-migrations
(versioned)
agents_config · audit_logs ·
test_cases
eval_results · triggers ·
widget_keys
Docker Compose · Standalone
Binary
10 — Request Flow & Tech Stack
From user message to agent response
Request execution flow — every request
User / App
HTTP + Bearer Token / SSO Cookie
Auth Server :8000
Validate auth → check rate limit → proxy to ADK
ADK Server :8001
Get initialized agent from cache or build tree
Agent Manager
Load config from DB → recursive sub-agent init
Agent Instance
Execute with tools, memory, MCP, code executor
Execution Callbacks (every run)
Verify RBAC — role access per agent
Inject user profile into agent context
Track token usage (prompt / response / tools)
OTel trace span — LLM + tool calls + sub-agents
Guardrail check — PII, injection, hallucination score
Response
Streamed back via SSE / JSON — canvas opens for code
Let's build your AI product together
MATE is open-source, production-ready, and actively developed. You bring the clients — MATE brings the infrastructure.
Check the GitHub Repository