The missing DevTools for multi-agent AI systems.
You wouldn't ship a backend without logs. Why are you shipping agents blind?
Multi-agent systems are the future of AI — but right now, debugging them feels like reading smoke signals. MAVT gives you full observability: every agent call, every decision step, every inter-agent message, visualized in real time.
You build a multi-agent workflow. Something breaks. You ask yourself:
- Which agent failed — and why?
- What did agent A actually say to agent B?
- Where in the chain did the task go wrong?
- Why is this running so slow?
You open your terminal. You see... nothing useful.
MAVT fixes this.
| 🔁 Agent-to-agent traces | See every message passed between agents, in order |
| 🧠 LLM turn inspector | Full prompt/response history with token counts and latency |
| 📊 Live topology graph | Visual execution graph, updating in real time |
| 🛠 Tool call traces | Every tool invocation, input, output, and duration |
| 🧩 Memory panel | Watch agent memory reads and writes as they happen |
| 📜 Run history & replay | Browse past runs and replay them step by step |
| 🔔 Webhook alerts | Get notified on token budget exceeded, agent stuck, critic failures |
npm install -g agent-visibility
agentscopeOpen your browser → http://localhost:4242
Your agents are now fully observable.
Drop-in Python adapters — two lines of code, full visibility.
from adapters.langchain import AgentscopeCallback
AgentExecutor(agent=agent, tools=tools,
callbacks=[AgentscopeCallback(goal="My task")])from adapters.autogen import track
scope = track(agents=[orchestrator, researcher, coder], goal="My task")
# ... run your agents ...
scope.finish()from adapters.crewai import AgentscopeListener
listener = AgentscopeListener(goal="My task")
crew = Crew(agents=[...], tasks=[...], step_callback=listener)
result = crew.kickoff()
listener.finish()Configure alerts directly from the dashboard sidebar:
| Alert | Trigger |
|---|---|
| 💸 Token budget | Agent uses ≥ X% of its token budget |
| ⏳ Agent stuck | Agent silent for more than N seconds |
| ❌ Critic fail rate | Critic failure rate exceeds X% |
Alerts appear as toast notifications in the UI and are POSTed to any webhook URL you configure.
"If you can't measure it, you can't manage it."
AI agents are making real decisions in production systems today — in customer service, in code generation, in enterprise workflows. Without visibility:
- You can't debug failures
- You can't trust outputs
- You can't scale safely
- You can't explain decisions to stakeholders
MAVT is the foundation layer your agent stack is missing.
- Live topology graph
- LLM turn inspector
- Tool call traces
- Memory panel
- Run history & replay
- LangChain adapter
- AutoGen adapter
- CrewAI adapter
- Webhook alerts
- OpenTelemetry export
- Cloud-hosted dashboard
- Cost tracking per agent
Issues, PRs, and framework integrations are very welcome. If you're using MAVT with a framework not listed above — open an issue and let's add it.
If MAVT saves you a debugging session, consider leaving a ⭐ — it helps other developers find the tool.
Built by Hitarth Bhatt — AI product leader with 10+ years shipping AI systems at scale. MAVT grew out of a real frustration: the more powerful multi-agent systems become, the harder they are to see inside.