Open source ·
github.com/antiv/mate
Mate — AI infrastruktura za produkcione agente
Multi-agent orkestracija sa Google ADK i LangGraph engine-ima, Slack integracijom, HITL odobrenjima i podrškom za 50+ LLM provajdera — bez pisanja engine-a ispočetka.
Mogućnosti
Šta Mate radi
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 with dual support for Google ADK and LangGraph. You deploy it once, switch runtimes via env var (AGENT_FRAMEWORK=langgraph), and configure agents, Slack bots, 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 and webhook triggers (with signed requests) run agents fully autonomously. Output goes 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.
Cost in US dollars & Budgets
The cost of every model call per agent, project and user, next to four token types. Budgets in tokens or dollars per day or month that warn or block. OpenTelemetry and Prometheus for monitoring.
Multi-Engine Runtime
Seamlessly run agents on Google ADK or LangGraph engines. Switch runtimes via environment variable (AGENT_FRAMEWORK=langgraph) with identical UI, agents, and widget.
Slack Channels & DMs
Native Slack bot integration for channels and direct messages, featuring interactive button callbacks and Slack Block Kit card formatting.
Human-in-the-Loop (HITL)
Interactive tool confirmation workflow requiring explicit human approval before sensitive actions, web searches, or system commands execute.
Playground
Run one prompt or a whole eval suite on 2–3 variants of an agent (another model, other instructions, an older version) and compare score, reply time and cost in US dollars, without saving anything.
Alerts
Rules for agent errors, guardrail bursts, fallback-model use and budget thresholds, sent to a webhook, email, Slack or Discord.
Fallback Model
When a provider fails (timeout, 5xx, 429), the request is re-run once on a backup model and the user gets an answer instead of an error. Every fallback is logged and can trigger an alert.
OpenAI-compatible API
Any root agent exposed as a model at /v1, with a Personal Access Token. Works with OpenCode, Continue and Cline. MATE can also govern an agent you already run elsewhere.
Docs & Help Agent
The documentation lives inside the dashboard, with search. A ? button on every page opens a chat with a built-in agent that reads the docs and knows which page you are on.
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 |
|---|---|---|---|---|
| ADK + LangGraph support | ⚡ LangGraph only | ✗ | ✗ | ✓ Both (1 env var switch) |
| 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 and dollar budgets | ✗ | ✗ | ✗ | ✓ Per user, agent, 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, webhook) | ✗ | ✗ | ✗ | ✓ Built-in |
| Cost in US dollars per agent | ⚡ Via LangSmith | ✗ | ✗ | ✓ Built-in |
| Model & prompt comparison | ⚡ Via LangSmith | ✗ | ✗ | ✓ Playground |
| Fallback model on provider outage | ⚡ DIY | ✗ | ✗ | ✓ Per agent |
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 an API key + AUTH_PASSWORD
docker-compose up -d # → open http://localhost:8000
git clone https://github.com/antiv/mate.git && cd mate
cp .env.example .env # set an API key + AUTH_PASSWORD
docker-compose up -d # → open http://localhost:8000
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)
OpenAI-compatible API /v1
↕
MATE Core — Auth Server
:8000
Auth + RBAC +
SSO/OIDC
Dashboard API
Widget Keys
MCP Manager
Audit Logger (EU AI
Act)
Rate Limits + Budgets ($ / tokens)
Guardrails + Hallucination
Check
Trigger Engine (cron/webhook)
Eval Framework
Alerts (Slack · Discord · email · webhook)
↕
Agent Runtime — ADK or LangGraph :8001
Agent Manager
Tool Factory
Session Manager
50+ LLM Providers via
LiteLLM
Memory Blocks + RAG
A2A Protocol
OTel Tracing
Code Executor
Fallback Model
↕
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
Documentation
Everything is documented
Guides for users and developers and a reference generated from the code, on GitHub and inside the dashboard.
User guides
Getting started, agents, tools and MCP, the Work Room, the widget, triggers, evals, guardrails, budgets, alerts, Slack and the EU AI Act.
Open on GitHub →Developer guides
Architecture, the agent runtime and LangGraph, writing a tool, MCP servers, A2A, the OpenAI API, SSO, deployment, migrations and the standalone build.
Open on GitHub →Reference
Configuration, HTTP API, tools and database — generated from the code, and CI fails when the reference goes stale.
Open on GitHub →Naručite agenta
Opišite proces koji želite da automatizujete — predlažemo arhitekturu agenta i procenu.