An open-source AI agent studio for building and running visual AI workflows with multi-model composition.
Giselle is an open-source AI agent studio designed to power product delivery through seamless human-AI collaboration. It enables users to create, modify, and execute agentic workflows using an intuitive visual drag-and-drop interface, making AI automation accessible for various use cases like GitHub operations and document generation.
Product teams, developers, and non-engineers who need to automate workflows involving AI models without deep technical expertise, particularly those integrating AI with GitHub for operations like issues, PRs, and deployments.
Developers choose Giselle for its visual, no-code agent builder that simplifies creating complex AI workflows, its multi-model composition supporting GPT, Claude, and Gemini, and its open-source nature allowing self-hosting or cloud use with collaborative features.
Giselle: AI App Builder. Open Source.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The drag-and-drop interface allows users to create and modify AI agents in minutes without coding, as highlighted in the Visual Agent Builder feature, making it accessible for non-engineers.
Supports GPT, Claude, Gemini, and other models, with agents selecting the best model for each task, enabling versatile AI composition for diverse use cases.
Automates issues, pull requests, and deployments with AI, integrating seamlessly into GitHub workflows for product delivery, as specified in the GitHub AI Operations feature.
Can be run locally or in the cloud, offering flexibility and control over deployment, with detailed self-hosting guides provided in the README.
Key features like Team Collaboration and Template Hub are marked as 'In Development' in the README, limiting immediate usability for collaborative or template-driven projects.
Requires API keys from providers like OpenAI, which adds ongoing costs, potential latency, and may not suit environments with restricted internet access or budget constraints.
Local installation involves cloning, dependency management with pnpm, and environment configuration, which can be a barrier for quick adoption compared to plug-and-play services.