What is Lacquer?
Lacquer is an open-source AI workflow engine designed for engineers who want to automate complex, repeatable tasks using a developer-native toolchain. It allows you to build reliable, AI-powered internal tools using the same GitOps principles you apply to your application code. Think of it as GitHub Actions, but purpose-built for orchestrating AI agents within your own infrastructure.
Key Features
Lacquer provides the essential features for building, testing, and deploying production-grade AI automations.
↔️ GitOps Native by Design: Your workflows are defined in simple, declarative YAML files. This means you can commit, version, and peer-review your automations just like any other piece of code, bringing predictability and auditability to your internal tooling.
💻 Local-First, Zero-Dependency Development: Lacquer is a single, lightweight Go binary with no external dependencies. You can build and test entire workflows on your laptop without needing a cloud account or wrestling with complex Python environments, ensuring a frictionless development cycle.
🤖 Advanced Multi-Agent Orchestration: Define and combine multiple AI agents within a single workflow. You can assign different models (e.g., Anthropic's Claude for analysis, OpenAI's models for generation), system prompts, and tools to each agent, allowing you to use the best tool for each step of the job.
⚙️ Powerful and Familiar Control Flow: With a DSL that will feel immediately familiar to anyone who's used GitHub Actions, you can build truly dynamic workflows. Implement conditional execution based on the outputs of previous steps or create loops that run until a specific condition is met.
Use Cases
Automated Kubernetes Pod Diagnosis: When a pod is failing, you can trigger a Lacquer workflow that automatically fetches the latest logs, filters for errors, and passes them to an AI agent pre-configured as a Kubernetes expert. The agent analyzes the logs to identify the root cause, check for error patterns, and returns a set of specific remediation steps, dramatically reducing your mean time to resolution (MTTR).
Intelligent Service Health Monitoring & Remediation: Create a workflow that runs on a schedule to check a service's key health metrics. If the workflow detects an error rate above a defined threshold (e.g., 5%), it can conditionally trigger a series of automated actions, such as scaling up the deployment, performing a rolling restart, and then re-verifying service health post-restart.
Why Choose Lacquer?
For engineering teams, building AI automation often means compromising with no-code platforms that don't fit a developer's workflow. Lacquer is built on a different philosophy.
You Work in Code, Not UIs: Forget drag-and-drop interfaces that can't be version-controlled. With Lacquer, your entire automation is transparent, auditable, and lives in your Git repository.
You Get Control and Extensibility: Unlike "black box" systems, Lacquer is open-source and built for extension. You can create your own custom tools in any language to give your AI agents new capabilities, ensuring the system adapts to your unique needs.
You Develop Faster and Deploy Anywhere: The ability to run and test everything locally removes deployment friction. Once ready, the self-contained binary can be deployed with ease to Kubernetes, serverless environments, or a standard virtual machine.
Get Started in Minutes
Lacquer is designed to deliver value immediately. You can go from installation to running your first AI-powered workflow in under a minute. Explore the documentation and view the project on GitHub to see how Lacquer can streamline your internal engineering operations.
More information on Lacquer
Launched
2025-06
Pricing Model
Free
Starting Price
Global Rank
Follow
Month Visit
<5k
Tech used
Lacquer was manually vetted by our editorial team and was first featured on 2025-08-25.
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