Waldiez
Open-source suite pairing a visual multi-agent workflow builder with a Rust/Python actor-model runtime for edge and IoT deployments.
Waldiez is the rare open-source stack that covers both design and runtime: Waldiez's drag-and-drop builder (AG2-powered, one-click export to Python/Jupyter) feeds the Wactorz actor-model engine with MQTT messaging, runtime agent spawning, and on-every-tick persistence. If you're shipping edge or IoT agent systems in Python and Rust and are fine self-hosting, it's a strong fit. If you need a managed cloud, multi-tenancy, or SLAs, look at CrewAI or LangGraph hosted offerings instead — Waldiez has no managed tier.
Verified 21h 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 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, multi-tenant SaaS with SLAs and no self-hosting — it's Apache-2.0 open source with no cloud tier, so it assumes you can run Python, MQTT, and Docker or systemd yourself.
There's no license fee, but you pay your own LLM provider bills — the dashboard tracks per-agent and aggregate cost so a runaway loop doesn't become a surprise, which means someone on the team has to watch it.
Waldiez and Wactorz are free under Apache 2.0 — no tier list, no seats, no usage fees. Where money actually moves is LLM spend (tracked per-agent and in aggregate across OpenAI, Anthropic, Google, Mistral, AWS Bedrock, and Azure OpenAI) plus the MQTT broker and hosts you run yourself. That makes it cheaper than managed multi-agent platforms like CrewAI or LangGraph hosted offerings at the license level, but more expensive in engineering time if you don't already run Python, MQTT, and Docker or
In short
Waldiez — Open-source suite pairing a visual 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 people actually say about Waldiez — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
17 mentions across 2 sources (Bluesky, GitHub) · researched Jul 5, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Visual drag-and-drop builder lowers barrier to multi-agent design.
- +One-click export to Python or Jupyter notebooks speeds up prototyping.
- +Multi-interface support: REST, WebSocket, Discord, WhatsApp, etc.
- +Runtime actor orchestration with auto-persistence and crash recovery.
- +Edge and IoT ready with Docker, systemd, and Python runner.
- −Installation via pip often fails; Docker is the only reliable method.
- −Captain agent flows are broken in some versions.
- −No model validation before running a flow, causing frustrating errors.
- −Predefined model names are sometimes incorrect.
- −Documentation unclear for non-Docker, non-Jupyter deployment.
- • No hidden costs for base tool; may incur LLM API fees.
- • Potential infrastructure costs for MQTT broker or cloud deployment.
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: September 2026
How we score →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
About Waldiez
Waldiez is an open-source (Apache 2.0) stack of two complementary tools for building and running multi-agent AI systems. The Waldiez visual workflow builder lets you compose multi-agent flows with drag-and-drop, powered by the AG2 framework, and export to Python scripts or Jupyter notebooks in one click, with a Community Hub for publishing and forking workflows. The Wactorz runtime engine — written in Python and Rust — handles production: each agent runs in its own async loop with an isolated mailbox, agents spawn at runtime via LLM intent classification, all inter-agent messaging goes over MQTT pub/sub, and agent state writes to disk on every tick so agents survive crashes and restore full context on restart. A live dashboard surfaces agent health, message flows, and per-agent plus aggregate LLM cost meters. Agents run via Docker, systemd, or the native Python runner from laptop to Raspberry Pi, and InstallerAgent deploys remote nodes over SSH to bridge them into the central MQTT graph. Interfaces include REST, WebSocket, Discord, WhatsApp, Telegram, and CLI, with streaming responses and rolling history summarization. Built-in agents include MainActor, MonitorAgent, DynamicAgent, PlannerAgent, HomeAssistant agents, InstallerAgent, and CatalogAgent. Multi-LLM support covers OpenAI, Anthropic, Google, Mistral, AWS Bedrock, and Azure OpenAI. It suits AI engineers, robotics developers, and teams building self-hosted edge-native agent systems who want full control rather than a managed cloud.
Behind the Verdict
The interesting thing about Waldiez isn't the visual builder — plenty of tools have one — it's that the design surface and the runtime are one stack. You compose flows in Waldiez, export to Python or a Jupyter notebook, and the same agents run under Wactorz with an actor model: isolated async mailboxes per agent, so crashing one doesn't take down the rest. MQTT as the inter-agent nervous system is a deliberate edge-first choice — it means sensors, thresholds, and Home Assistant entities can trigger agents in real time without polling, and InstallerAgent can SSH out to remote nodes and bridge them into the same MQTT graph without the wactorz package installed on the edge. Runtime spawning via LLM intent classification is the other standout: one LLM call decides which agent to spin up, so you're not predefining every agent type. The live dashboard tracks agent health, message flows, and per-agent and aggregate LLM costs across OpenAI, Anthropic, Google, Mistral, AWS Bedrock, and Azure OpenAI — cost visibility before it becomes a surprise is genuinely useful in multi-agent systems where one runaway loop can burn budget. The honest weaknesses are structural, not cosmetic. There is no managed cloud — everything requires self-hosting, so your performance and uptime ride on your own LLM and MQTT infrastructure. There's no multi-tenancy, no SLA, no enterprise support contract, and the platform assumes you're comfortable with Python, MQTT, and things like Docker or systemd. DynamicAgent writes and runs Python at runtime from LLM output — powerful and sandboxed and introspectable, but it means you own the security posture of that execution path. Non-technical users who want a fully managed SaaS will find nothing here, and teams wanting a pure no-code platform beyond visual workflow design are out of scope. Where it fits: AI engineers and robotics teams deploying agent fleets to laptops, local servers, or Raspberry Pi, and anyone who values zero vendor lock-in. Where it doesn't: organizations that need turnkey SaaS or a hosted control plane out of the box.
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Real-world workflow fit
Concrete scenarios for the personas Waldiez actually fits — and what changes day-one when you adopt it.
Design the agent flow in the Waldiez visual builder with drag-and-drop, connect agents and tools, then export to a Python script or Jupyter notebook in one click and run it under Wactorz.
Outcome: You get from a visual sketch to runnable Python without rewriting orchestration code, and the exported flow runs unchanged on your own infrastructure.
Use InstallerAgent to SSH into remote edge nodes and deploy the self-contained runner, bridging each node into the central MQTT graph without installing the wactorz package on the edge.
Outcome: Edge nodes appear as agents in the live dashboard, with per-agent cost meters and crash recovery via on-every-tick persistence.
Give agents control of Home Assistant entities through natural language, with MQTT events from sensors firing agent actions in real time rather than polling.
Outcome: Commands and reactions run in-thread with streaming responses, and MonitorAgent flags crashes or resource spikes automatically.
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.
Models Under the Hood
as of 2026-09-09
Limitations
- Both Waldiez and Wactorz are Apache 2.0 open source, and there is no managed cloud offering — every deployment is self-hosted, so uptime, scaling, and LLM connectivity depend on your own infrastructure.
- There is no multi-tenancy, no enterprise support contract, and no SLA.
- Effective use assumes comfort with Python, MQTT, and either Docker, systemd, or the native Python runner; non-technical users will hit a wall quickly.
- DynamicAgent executes Python generated from LLM output at runtime — sandboxed and introspectable, but you own the security and review posture of that path.
- The platform leans on MQTT as its messaging backbone, which is excellent for edge and IoT but is an extra dependency if your stack doesn't already speak it.
as of 2026-09-15
Verification history
We have re-verified Waldiez 9 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.
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Free to cite with attribution — this page re-verifies continuously.
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 and Wactorz are free under Apache 2.0 — no tier list, no seats, no usage fees. Where money actually moves is LLM spend (tracked per-agent and in aggregate across OpenAI, Anthropic, Google, Mistral, AWS Bedrock, and Azure OpenAI) plus the MQTT broker and hosts you run yourself. That makes it cheaper than managed multi-agent platforms like CrewAI or LangGraph hosted offerings at the license level, but more expensive in engineering time if you don't already run Python, MQTT, and Docker or
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 fluent in Python and Docker: roughly an afternoon to install Waldiez and Wactorz, point them at an LLM provider key, and run a first multi-agent flow. Developers new to MQTT: add a day to stand up and understand the broker and pub/sub topology. Edge deployments with InstallerAgent over SSH: allow a day per node topology to validate bridging into the central MQTT graph.
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 hand-rolled Python agent scripts: replicate the orchestration in the Waldiez visual builder, then export back to Python or a Jupyter notebook and run under Wactorz.
- →From an AG2-based prototype: import or rebuild the flow in Waldiez, which is powered by AG2, and add Wactorz for runtime spawning, MQTT messaging, and persistence.
- →From a polling-based IoT pipeline: replace the poll loop with MQTT pub/sub so agents are triggered by sensor events rather than repeated checks.
- →From a single-host deployment: use InstallerAgent over SSH to pull remote nodes into the central MQTT graph without installing wactorz on the edge.
- ↗To a managed platform like CrewAI or LangGraph hosted offerings: export your Waldiez flows to Python and port the orchestration, since Waldiez has no managed cloud to migrate from.
- ↗To a plain Python service: export the same flows as Python scripts or Jupyter notebooks and run them without the Wactorz runtime, though you lose runtime spawning and auto-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
Tools that pair well with Waldiez
Common stack mates teams adopt alongside Waldiez, with the specific reason each pairing earns its keep.
Dify
Dify is an open-source platform for building agentic workflows and RAG pipelines with a visual builder.
ChatDev
ChatDev 2.0 (DevAll) is a free, open-source zero-code multi-agent platform for orchestrating AI agent workflows from YAML or JSON config.
Coze Studio
Self-hosted, open-source visual AI agent builder for full control.
Featured 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.
Waldiez 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.
Waldiez 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.
Alternatives to Waldiez
View allDify
Dify is an open-source platform for building agentic workflows and RAG pipelines with a visual builder.
ChatDev
ChatDev 2.0 (DevAll) is a free, open-source zero-code multi-agent platform for orchestrating AI agent workflows from YAML or JSON config.
Coze Studio
Self-hosted, open-source visual AI agent builder for full control.
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