Giselle is an open-source agentic workflow builder that enables teams to create and deploy intelligent AI agents using a visual, node-based interface. It supports seamless integration of multiple large language models and data sources, orchestrating them collaboratively to tackle complex tasks—such as automated market research, pull request reviews, and documentation upkeep. Giselle streamlines development with drag-and-drop visual nodes, reducing the engineering burden of coordinating multi-model workflows. It supports both cloud-hosted and self-hosted deployments with GitHub integration, versioning, and traceability. Ideal for engineering and product teams, Giselle enables rapid iteration and transparent workflows without deep AI infrastructure expertise.
Key Features
Node-based visual interface for drag-and-drop workflow design.
Multi-LLM orchestration enabling collaborative agent actions.
GitHub integration for documentation and code review agents.
Open-source with cloud and self-hosted deployment options.
Built-in documentation automation and product development acceleration.
Supports Windows, Mac, Linux, mobile, and cloud platforms.
How Does It Work?
Giselle empowers users to design workflow graphs visually—each node representing an AI agent or tool. Developers chain nodes to define task sequences (e.g., research → analysis → documentation). Once configured, agents work collaboratively, pulling data from sources, analyzing code, updating docs, or summarizing insights. Cloud-hosted or self-hosted, the system logs each run and supports GitHub versions, making workflows traceable and maintainable. The output is a flexible multi-agent pipeline that automates core operations and can be iteratively refined.
Step-by-step Overview:
- Launch Giselle: (cloud or self-hosted).
- Design workflow: by placing nodes for LLM calls, data sources, and logic.
- Connect nodes: to model collaborative agent behavior.
- Link GitHub or data sources: for real-time updates.
- Run agents: to execute workflows automatically.
- Review logs and outputs: refine nodes as needed.
Ideal Users for Giselle AI Agent
Developers
AI Engineers
Data Scientists
Product Managers
Software Architects
Business Analysts
QA Engineers
Technical Writers
Documentation Teams
DevOps Engineers
R&D Teams
Engineering Managers
Project Managers
UX Designers
Research Analysts
IT Teams
Innovation Leaders
Startups
Case Studies & Examples
Case Study 1: Engineering Efficiency
- Challenge: A SaaS company used Giselle to automate pull request reviews.
- Solution: Agents scanned codebases, flagged outdated docs, and generated summaries.
- Result: This reduced review cycles by 25% and improved documentation consistency.
Case Study 2: Market Research Automation
- Challenge: A marketing team built a multi-agent pipeline to gather market signals, aggregate competitor data, and draft summaries.
- Solution: Tasks that once took days were executed automatically within hours, accelerating strategic decision cycles.
- Result: Tasks that once took days were executed automatically within hours, accelerating strategic decision cycles.
How to Deploy and Integrate
- Choose deployment: start with cloud or deploy self-hosted.
- Design workflows: build desired agent pipelines via node-editor.
- Add integrations: connect LLM platforms and GitHub or data APIs.
- Test agents: run trials to validate outputs.
- Deploy pipelines: schedule automatic runs for ongoing tasks.
- Monitor & refine: review logs; iterate for accuracy and performance.
Pricing & Plans
| Plan |
Price |
Notes |
| Community |
Free |
Open-source, self-hosted, limited to local deployment |
| Cloud Free |
Free (with monthly usage) |
Includes ~30 minutes of agent time per month |
| Cloud Hosted |
$20/user/month |
Full features, multi-user support, pay-per-user |
| Enterprise |
Contact |
Custom SLAs, dedicated support, on-premise options |
💡 For exact pricing or to explore bundle options, contact the Giselle AI Agent team directly.
Pros and Cons
Visual workflow builder simplifies multi-agent setup
Multi-LLM orchestration enables smarter automation
GitHub integration boosts developer collaboration
Open-source with both cloud and self-hosted flexibility
Transparent workflows, logs, and change tracking
Node editor may require learning time
Self-hosting needs infrastructure setup
Community support may lag advanced use cases
