Install Orcheo
bash <(curl -fsSL https://ai-colleagues.com/install.sh)From architecture to deployment, we help teams ship AI systems that are reliable, observable, and measurable.
Build and evolve multi-step AI workflows with clear state transitions and reusable node patterns.
Design tool-using agents with deterministic controls for retries, memory, and execution limits.
Connect to operational systems and databases so workflows can act on business data safely.
Integrate OpenAI, Slack, Telegram, MongoDB, and custom tools through extensible adapters.
Track execution status, latency, and runtime events with production-oriented monitoring hooks.
Measure quality, compare prompt or tool changes, and enforce guardrails before full rollout.
Learn more about the company behind Orcheo and why we built it.
Our Vision
At AI Colleagues, we believe that the future of work involves seamless collaboration between human teams and autonomous AI systems. Our mission is to democratize access to enterprise-grade AI orchestration, enabling organizations of all sizes to build, deploy, and manage complex AI workflows with ease.
We developed Orcheo as the foundation for this vision—a robust, flexible platform designed to handle the complexities of real-world AI applications. By simplifying the orchestration layer, we empower developers and businesses to focus on innovation rather than infrastructure, bridging the gap between experimental AI and production-ready solutions.
Recent technical articles and company updates.
An inside look at the architecture decisions, implementation choices, and lessons learned while building Orcheo's managed plugin system — covering the loader, lifecycle CLI, stack-aware runtime targeting, the PluginAPI contract, and the WeCom and Lark listener plugins that validated the design.
A practical introduction to Orcheo for first-time users: install the platform, start from Canvas templates, vibe-code a new workflow, deploy it to an Orcheo local server on your laptop, and then extend the same ideas into private AI bot workflows across Telegram, Discord, and QQ.
We are shifting Canvas workflow authoring from drag-and-drop editing to vibe-coding in Python: why we are making this change now, what we learned from building the visual editor, and how visualisation still matters.
Our Orcheo research paper has been accepted to the SIGIR 2026 Resource Track, highlighting modular workflow graphs, reproducible evaluation, and deployment-ready orchestration for conversational search systems.
We launched quick installation commands for macOS, Linux, and Windows, plus SDK install and upgrade paths so teams can start using Orcheo in minutes.
The Orcheo conversational search paper is now publicly available on arXiv, covering modular workflow design, reproducible evaluation, and deployment-ready execution.
Answers about engagement model, deployment approach, and integration scope.