Waldiez
FreeOpen-source visual multi-agent workflow builder with runtime actor orchestration
By Tanmay Verma, Founder · Last verified 05 Jul 2026
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Added 7d ago
69/100MonitorIn short
Waldiez — Open-source visual multi-agent workflow builder with runtime actor orchestration. Best for AI engineers building production multi-agent systems for edge and IoT, Developers prototyping with AG2 framework seeking a visual builder, Teams deploying agent workflows on laptops, local servers, or Raspberry Pi. Free to use.
Is Waldiez actually worth it?
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Editorial Verdict
Best for
AI engineers building production multi-agent systems for edge and IoTDevelopers prototyping with AG2 framework seeking a visual builderTeams deploying agent workflows on laptops, local servers, or Raspberry PiResearchers exploring actor-model orchestration and runtime spawning
Not ideal for
Non-technical users who need fully managed SaaS or zero deploymentTeams requiring enterprise-grade support, SLAs, or multi-tenancyUsers seeking a no-code platform beyond visual workflow designProjects needing cloud-only, API-driven agent orchestration without self-hosting
Waldiez uniquely combines a visual workflow builder with a runtime actor engine, making it powerful for developers who want to prototype fast and deploy to edge devices. The lack of a managed SaaS tier means it's self-hosted only, so non-technical users or teams wanting turnkey cloud may look elsewhere. For open-source enthusiasts building production multi-agent systems, it's a compelling choice.
Compare with: Waldiez vs AutoGen Studio, Waldiez vs Agent.ai, Waldiez vs Draftbit
Last verified: July 2026
What independent users actually report about Waldiez
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).
60% positive40% critical
Recurring strengths
- +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.
Recurring frustrations
- −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.
Patterns worth knowing
Visual builder enables non-coders and rapid prototyping
Seen on Bluesky
Installation and setup friction (pip issues, Docker dependence)
Seen on GitHub
Bugs in core features (captain agent, model names) hinder reliability
Seen on GitHub
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • No hidden costs for base tool; may incur LLM API fees.
- • Potential infrastructure costs for MQTT broker or cloud deployment.
Viability Score
69/100
MonitorHow likely is Waldiez to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
40
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Drag-and-drop visual multi-agent workflow builder
- One-click export to Python scripts or Jupyter notebooks
- Runtime agent spawning via LLM intent routing over MQTT
- Actor-model concurrency with isolated async mailboxes
- Live dashboard for agent health, cost meters, and error alerts
- Auto-persistence — agent state saves on every tick, survives crashes
- Multi-LLM support: OpenAI, Anthropic, Google, Mistral, AWS Bedrock, etc.
- Community Hub to share and fork workflows
- Edge and IoT ready — supports Docker, systemd, native Python runner
- Multi-interface: REST, WebSocket, Discord, WhatsApp, Telegram, CLI
- Per-agent and aggregate LLM cost tracking
- Built-in agents: MainActor, MonitorAgent, DynamicAgent, PlannerAgent, HomeAssistant agents, InstallerAgent, CatalogAgent
- MQTT pub/sub as inter-agent messaging backbone
- Streaming responses with rolling history summarization
- Deploy remote nodes via SSH using InstallerAgent
About Waldiez
FreeAdvancedAPI availableWeb · Desktop · CLI
Waldiez is an open-source platform that pairs a visual multi-agent workflow builder (Waldiez) with a runtime actor-model orchestration engine (Wactorz). Built on the AG2 framework, it lets developers design complex agent workflows via drag-and-drop, then deploy them with production-grade runtime features like MQTT messaging, auto-persistence, and live dashboards. The visual builder supports one-click export to Python scripts or Jupyter notebooks, while Wactorz allows agents to spawn dynamically via LLM intent routing, survive crashes, and scale from laptop to edge without code changes. This dual-tool architecture covers both design and deployment, making Waldiez suitable for AI engineers, robotics developers, and teams building edge-native agent systems. It integrates with major LLM providers (OpenAI, Anthropic, Google, etc.), supports multi-interface interaction (REST, WebSocket, Discord, WhatsApp, Telegram, CLI), and offers a Community Hub for sharing workflows. Unlike single-purpose agent frameworks, Waldiez provides a full pipeline from visual prototyping to runtime deployment, all under Apache 2.0 licensing.
Behind the Verdict
Waldiez fills a gap for developers who need both a visual design tool and a runtime that can handle real-world edge conditions. The actor model with MQTT messaging and auto-persistence is mature for an open-source project. You'll love it if you are comfortable self-hosting Docker or systemd and want to spawn agents on the fly. Compared to LangGraph or AutoGen, Waldiez offers a more complete pipeline from visual drag-and-drop to edge deployment, but with a steeper initial setup. The Community Hub for sharing workflows is a nice bonus, though it depends on user adoption. Where it bites: no managed cloud tier, so scaling to many nodes means managing your own MQTT broker and persistence layer. Also, the AG2 framework is less documented than alternatives like LangChain, so expect to read source code. Best for AI engineers building edge-native systems on Raspberry Pis or local servers.
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Use Cases
- Design and simulate multi-agent workflows visually before deploying to production.
- Orchestrate a fleet of edge agents that communicate via MQTT for real-time IoT data processing.
- Rapidly prototype a customer support bot using multiple LLM agents with drag-and-drop flow.
- Deploy a home automation system where agents manage HomeAssistant devices through natural language commands.
- Build a research tool that spawns specialized agents on demand to answer queries across domains.
Limitations
- Waldiez is open-source with no managed cloud offering, so users handle their own infrastructure.
- The visual builder relies on the AG2 framework, which may have a learning curve for those unfamiliar with it.
- There is no explicit rate limiting or context window documentation, but performance depends on the underlying LLM and MQTT setup.
- The platform currently lacks a formal pricing tier, which may indicate limited enterprise features.
Integrations
MQTTDockersystemdJupyterLabVS CodeAG2 frameworkHomeAssistantOpenAIAnthropicGoogle GeminiMistralAWS BedrockAzure OpenAIDiscordWhatsApp
Resources & Guides
Frequently Asked Questions
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