Magick 
Magick is an open-source visual AI development platform for building agent graphs, connecting models and tools, and orchestrating application workflows.
Starting price Free and open source + paid services
Tool Information
Magick is an open-source visual AI development platform for building agent graphs, connecting models and tools, and orchestrating application workflows.
Developers use the official repository, isolate a test environment, protect secrets, grant least privilege, validate every node and external action, add budgets and logs, and deploy with monitoring and rollback.
The core project is free and open source, with optional hosted or commercial services; stable public hosted pricing was not verified.
Agent graphs can expose keys, execute unintended actions, propagate prompt injection, or create uncontrolled costs. Sandboxing, permissions, evaluations, audit logs, budgets, and human approval are essential.
F.A.Q (3)
Pros and Cons
Pros
- Magick provides a visual node-based environment for building AI agents
- Agents can run as long-lived processes that respond to events
- Graph-based spells make logic and data flow visible
- Prebuilt templates shorten the path to a first working agent
- Text-generation nodes can call models from multiple providers
- Users can choose proprietary or open-source language models
- Built-in nodes cover logic; mathematics; actions; data; time; and queues
- The development environment supports testing and debugging before release
- Secrets and environment variables can be managed within the AIDE
- Agents can be published and deployed from the same platform
- The runtime is designed to scale agents as demand changes
- Document processing supports assistants grounded in specialized material
- A plugin architecture allows custom nodes and third-party services
- Discord bots; game characters; and web assistants are documented use cases
- The project offers open-source flexibility and a public GitHub path
- Metered billing can let small experiments begin without a large fixed commitment
Cons
- Complex agent graphs can become difficult to read and maintain
- A visual builder does not remove the need to understand prompts; state; APIs; and failure modes
- Long-running agents require monitoring to prevent repeated or unintended actions
- Model-provider charges can accumulate in addition to platform usage
- Metered costs are harder to forecast for variable or recursive workflows
- Plugin code can introduce security; reliability; and supply-chain risks
- Secrets connected to external services create a high-value target
- An automatically scaled error can multiply cost and impact quickly
- Document retrieval can return irrelevant passages or unsupported answers
- Changing provider models may alter behavior even when the graph is unchanged
- Event-driven flows need idempotency to avoid duplicate side effects
- Custom nodes reduce portability and increase maintenance burden
- High-availability claims should be validated against service-level commitments
- Nontechnical users may still need engineering help for production deployments
- Agent actions need permissions; audit logs; and human approval at consequential boundaries
- Open-source self-hosting transfers infrastructure; upgrades; and incident response to the operator
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Overview
Before you subscribe
- Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
- App-store pricing can differ from website pricing by country, device and tax.
- Share only authorised data, and read the provider's encryption, retention and model-training terms first.
- AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
- Commercial usage rights vary by plan, provider terms and jurisdiction.
- None of these tools replaces a qualified professional for medical, legal or financial decisions.
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