Waldiez is live · free · last verified 30 Oct 2026 · viability monitor
Waldiez
Open-source stack pairing a drag-and-drop multi-agent workflow builder with a Rust/Python actor-model runtime for edge and IoT deployments.
Waldiez is one of the few open-source projects that covers both halves of multi-agent work: design and runtime. The Waldiez builder gives you drag-and-drop flow authoring on AG2 with one-click export to Python or Jupyter, and Wactorz gives you an actor-model engine with MQTT messaging, runtime agent spawning, and on-every-tick persistence plus a live cost dashboard. If you're shipping agents in Python and Rust onto edge hardware and are fine self-hosting, this is a strong fit. If you need a managed cloud, multi-tenancy, or SLAs, look at CrewAI or LangGraph hosted offerings instead — Waldiez publishes no managed tier.
Verdict generatedVerified 1h ago · liveness 68/100 · cite: rightaichoice.com/tools/waldiez
- AI engineers building production multi-agent systems for edge and IoT
- Robotics developers shipping agents on Raspberry Pi or local servers
- Teams that need self-hosted deployment with no vendor lock-in
- Developers already working in the AG2 framework
- Non-technical users who need a fully managed SaaS or zero deployment
- Teams requiring enterprise SLAs, support contracts, or multi-tenancy
- Projects wanting cloud-only, API-driven orchestration without self-hosting
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Skip Waldiez if you want a managed cloud service and aren't prepared to run MQTT, Docker or systemd, and your own Python and Rust runtime on your own infrastructure.
You supply your own LLM API keys across OpenAI, Anthropic, Google, Mistral, AWS Bedrock, and Azure OpenAI — the per-agent and aggregate cost meters in the Wactorz dashboard bill spend your provider invoices you for, not
Waldiez is Apache 2.0 open source and free to use, so the relevant comparison isn't tier pricing but total cost of ownership. Teams comparing Waldiez to hosted agent orchestration from CrewAI or LangGraph trade licence fees for self-hosted infrastructure and engineering time. If you already run MQTT, Docker, and Python on edge hardware, Waldiez is effectively free.
In short
Waldiez — Open-source stack pairing a drag-and-drop multi-agent workflow builder with a Rust/Python actor-model runtime for edge and IoT deployments. Best for AI engineers building production multi-agent systems for edge and IoT, Robotics developers shipping agents on Raspberry Pi or local servers, Teams that need self-hosted deployment with no vendor lock-in. Free to use.
What is Waldiez?
Waldiez — Open-source stack pairing a drag-and-drop multi-agent workflow builder with a Rust/Python actor-model runtime for edge and IoT deployments. Best for AI engineers building production multi-agent systems for edge and IoT, Robotics developers shipping agents on Raspberry Pi or local servers, Teams that need self-hosted deployment with no vendor lock-in. Free to use.
Waldiez is an open-source (Apache 2.0) suite of two products for building and running multi-agent AI systems. Waldiez itself is a drag-and-drop visual workflow builder powered by the AG2 framework: you compose multi-agent flows, then export to Python scripts or Jupyter notebooks in one click. A Community Hub lets you publish, discover, and fork public workflows, and the builder ships as a JupyterLab extension, a VS Code extension, and Waldiez Studio. Wactorz is the runtime half — a Python and Rust actor-model engine where each agent runs in its own async loop with an isolated mailbox, agents spawn at runtime via an LLM intent-classification call rather than at compile time, all inter-agent messaging flows over MQTT pub/sub, and agent state writes to disk on every tick so agents survive crashes and restore full context on restart. A browser dashboard shows agent health, message flows, and per-agent plus aggregate LLM cost meters. Built-in agents include MainActor, MonitorAgent, DynamicAgent, PlannerAgent, HomeAssistant agents, InstallerAgent, and CatalogAgent. Agents run via Docker, systemd, or the native Python runner from laptop to Raspberry Pi, and InstallerAgent deploys remote edge nodes over SSH and bridges them into the central MQTT graph without needing the wactorz package on the edge. Interfaces include REST, WebSocket, Discord, WhatsApp, Telegram, and CLI, with streaming responses and rolling history summarization. It suits AI engineers, robotics developers, and teams building self-hosted edge-native agent systems who want full control rather than a managed cloud.
Key features
- Drag-and-drop visual multi-agent workflow builder
- Wactorz runtime actor-model orchestration engine (Python + Rust)
- Actor-model concurrency with isolated async mailboxes per agent
- MQTT pub/sub as the inter-agent messaging backbone
- Runtime agent spawning via LLM intent classification
- Auto-persistence — agent state writes to disk on every tick and restores on restart
- Live dashboard for agent health, message flows, and error alerts
- Per-agent and aggregate LLM cost tracking across providers
- One-click export to Python scripts or Jupyter notebooks
- Multi-LLM support: OpenAI, Anthropic, Google, Mistral, AWS Bedrock, Azure OpenAI
- Multi-interface: REST, WebSocket, Discord, WhatsApp, Telegram, CLI
- Built-in agents: MainActor, MonitorAgent, DynamicAgent, PlannerAgent, HomeAssistant agents, InstallerAgent, CatalogAgent
- Edge and IoT deployment via Docker, systemd, or native Python runner
- InstallerAgent deploys remote edge nodes over SSH into the central MQTT graph
- Streaming responses with rolling history summarization
Our long-form take written Jul 23, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-25
Limitations
- Both Waldiez and Wactorz are Apache 2.0 open source and hosted on GitHub, so every deployment is self-hosted — uptime, scaling, and LLM connectivity depend on your own infrastructure.
- The stack assumes comfort with Python, Rust, MQTT, and either Docker, systemd, or the native Python runtime; non-technical users will hit a wall quickly.
- MQTT is the platform's inter-agent messaging backbone, which is excellent for edge and IoT but is an extra dependency if your stack doesn't already speak it.
- Wactorz runtime spawning relies on LLM intent classification, so agent behavior is coupled to LLM routing quality — a bad classification spawns the wrong agent.
as of 2026-10-10
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Is Waldiez still active?
Waldiez is live · free · last verified 30 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Waldiez? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Waldiez?
What people actually say about Waldiez — is it worth it?
Our community sentiment research written Jul 5, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Waldiez— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 30 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Waldiez 13 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed switching in, hidden costs, who should skip it, switching out, our verdict, setup time, use cases, description, limitations, summary, pricing fit, integrations
- — changed platforms, limitations
- — changed link health
- — changed our verdict, description, who it suits, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed platforms
- — changed pricing page
Showing the 6 most recent of 13 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Waldiez cost in 2026?
Waldiez: Free to use. Pricing last verified 30 Oct 2026.
Where the pricing makes sense
The company stage and team size where Waldiez's pricing actually pencils out — and where peers do it cheaper.
Waldiez is Apache 2.0 open source and free to use, so the relevant comparison isn't tier pricing but total cost of ownership. Teams comparing Waldiez to hosted agent orchestration from CrewAI or LangGraph trade licence fees for self-hosted infrastructure and engineering time. If you already run MQTT, Docker, and Python on edge hardware, Waldiez is effectively free.
Has Waldiez's pricing changed?
No plan price for Waldiez has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Waldiez?
Pick Waldiez if you are AI engineers building production multi-agent systems for edge and IoT, Robotics developers shipping agents on Raspberry Pi or local servers, Teams that need self-hosted deployment with no vendor lock-in, Developers already working in the AG2 framework.
Look elsewhere if you are Non-technical users who need a fully managed SaaS or zero deployment, Teams requiring enterprise SLAs, support contracts, or multi-tenancy, Projects wanting cloud-only, API-driven orchestration without self-hosting, Users seeking a no-code platform beyond visual workflow design.
Skip it if Skip Waldiez if you want a managed cloud service and aren't prepared to run MQTT, Docker or systemd, and your own Python and Rust runtime on your own infrastructure.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Waldiez actually fits — and what changes day-one when you adopt it.
Describe HomeAssistant agents in natural language, let Wactorz spawn them at runtime, and have them react to MQTT sensor events — issuing entity commands over the same broker the sensors publish to.
Outcome: You get natural-language control of Home Assistant entities with real-time sensor reactions, and MonitorAgent watches for actor crashes.
Design the flow in the Waldiez drag-and-drop builder, export it to a Jupyter notebook with one click, and iterate on the AG2 logic in your usual notebook environment.
Outcome: You move from visual design to scriptable code without rewriting the orchestration, and can publish the flow to the Waldiez Hub for others to fork.
Run InstallerAgent to deploy agents on remote nodes over SSH, bridging each node into the central MQTT graph without installing the wactorz package on the edge.
Outcome: Agents on Raspberry Pi or local servers join the fleet, and the live dashboard shows agent health and per-agent LLM spend across the whole graph.
Use cases
- Orchestrate a fleet of edge agents over MQTT for real-time IoT data processing.
- Control Home Assistant entities through natural language, with agents reacting to sensor events in real time.
- Build reactive 'if X then Y' pipelines over MQTT that trigger on sensor fires, threshold breaches, or schedules without polling.
- Run DynamicAgent to write and execute Python at runtime from LLM output, sandboxed and streamed back to chat.
- Use PlannerAgent to decompose complex goals into subtask pipelines that spawn specialist agents per step.
- Deploy agents on remote nodes via SSH with InstallerAgent and bridge them into the central MQTT graph.
- Author multi-agent flows in the visual builder, share them on the Hub, and export to Python or Jupyter.
- Interact with agents over Discord, WhatsApp, or Telegram with streaming output and on-demand sub-agent spawning.
Setup time & first value
How long it actually takes to get something useful out of Waldiez — broken out by persona, not the marketing-page minute.
AI engineers already running Python, MQTT, and Docker can stand up Waldiez and Wactorz in an afternoon: install the runtime, point it at an MQTT broker, and start describing agents. Teams without MQTT infrastructure should budget extra time for standing up and securing the broker. Non-technical users will not get to first value without engineering help.
Switching to or from Waldiez
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hand-rolled Python multi-agent script: reauthor the flow in the Waldiez visual builder on AG2 and export it back to Python, keeping the code path.
- →From a LangGraph or CrewAI graph: map your nodes to Waldiez agents and your edges to MQTT topics, then let Wactorz spawn agents at runtime instead of at graph definition time.
- →From a Home Assistant automation-only setup: add HomeAssistant agents and let them issue commands and react to the same MQTT sensor events you already publish.
- ↗To plain AG2 code: use the one-click export to a Python script or Jupyter notebook and drop the visual layer entirely.
- ↗To a managed orchestration service: re-implement agents and routing in the hosted platform, replacing MQTT pub/sub with the vendor's messaging layer and giving up per-agent local persistence.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Waldiez”, and we withheld 6: 6 could not be judged, because “Waldiez” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Waldiez.
Official links
What are the best Waldiez alternatives?
The closest alternatives to Waldiez we list are LangGraph, AutoGen Studio and Coze Studio. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| LangGraph | Freemium | Pricing not published | — |
| AutoGen Studio | Free | Free | — |
| Coze Studio | Free | Free | — |
Alternatives to Waldiez
View all
LangGraph
LangGraph is an MIT-licensed, low-level agent orchestration framework for building stateful, controllable AI agents as graphs of states and transitions.
AutoGen Studio
Open-source web UI for prototyping multi-agent AI teams on Microsoft's AutoGen AgentChat without writing orchestration code.
Coze Studio
Coze Studio is an Apache-2.0, self-hosted AI agent builder with a visual workflow engine, RAG, plugins, and databases — you run the stack.
Tools that pair well with Waldiez
Common stack mates teams adopt alongside Waldiez, with the specific reason each pairing earns its keep.
LangGraph
LangGraph is an MIT-licensed, low-level agent orchestration framework for building stateful, controllable AI agents as graphs of states and transitions.
AutoGen Studio
Open-source web UI for prototyping multi-agent AI teams on Microsoft's AutoGen AgentChat without writing orchestration code.
Coze Studio
Coze Studio is an Apache-2.0, self-hosted AI agent builder with a visual workflow engine, RAG, plugins, and databases — you run the stack.
Waldiez vs its closest rival
Waldiez's closest head-to-head is with Locus Robotics — Choose Locus Robotics if you need to automate physical warehouse operations with proven AMRs and RaaS model for high-volume fulfillment. Choose Waldiez if you are building software-based multi-agent systems and need an open-source visual builder with runtime orchestration for edge or IoT deployments. They solve fundamentally different problems—one in physical logistics, the other in software agent workflows. Verdict generated
Head-to-head comparisons
Waldiez vs Locus Robotics
Choose Locus Robotics if you need to automate physical warehouse operations with proven AMRs and RaaS model for high-volume fulfillment. Choose Waldiez if you are building software-based multi-agent systems and need an open-source visual builder with runtime orchestration for edge or IoT deployments. They solve fundamentally different problems—one in physical logistics, the other in software agent workflows.
GeneratedWaldiez vs Presto Voice
If you're a QSR chain aiming to boost drive-thru revenue and efficiency with proven voice AI, Presto Voice is the way to go—recent partnerships like Dairy Queen underscore its enterprise traction. If you're an AI engineer building custom multi-agent workflows for edge or IoT, Waldiez's free, open-source visual builder and runtime orchestration offer unmatched flexibility. Choose based on your industry and need for turnkey vs. DIY.
GeneratedWaldiez vs Truleo
Truleo and Waldiez serve fundamentally different needs: Truleo is a specialized law enforcement intelligence platform that automates case research and report writing, while Waldiez is an open-source tool for AI engineers building multi-agent systems. Choose Truleo if you're a police department drowning in siloed data and manual casework. Choose Waldiez if you're a developer prototyping or deploying agent workflows at the edge with full control and zero licensing cost.
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