Introduction

Compan AI is an open-source large language model (LLM) application development platform. It combines the concepts of Backend as Service (BaaS) and LLMOps, enabling developers to quickly build production-ready generative AI applications. Even if you are a non-technical individual, you can participate in the definition of AI applications and data operations.

Compan AI includes a built-in technology stack that provides the key components needed to build LLM applications, including support for hundreds of models, an intuitive prompt orchestration interface, a high-quality RAG engine, a robust Agent framework, flexible workflow orchestration, and an easy-to-use UI and API. This saves developers a significant amount of time from reinventing the wheel, allowing them to focus on innovation and business requirements.

Compan AI offers a complete solution that includes:

• Tracking & Analysis

• Evaluation

• Human-in-the-loop

• API, CLI, SDK, and Embeddable Chatbot

• Team and Workspace

There are mainly 3 visual builders:

• Assistant

• ChatFlow

• AgentFlow

What can Compan AI do?

• Entrepreneurship: Quickly turn your AI application ideas into reality, where both success and failure require acceleration. In the real world, dozens of teams have already used Compan AI to build MVPs (Minimum Viable Products) to secure investment or win customer orders through POCs (Proof of Concept).

• Integrate LLM into existing business: Enhance the capabilities of existing applications by introducing LLMs. Connect via Compans RESTful API to decouple prompts from business code, and use Compan's management interface to track data, costs, and usage to continuously improve application performance.

• As an enterprise-level LLM infrastructure, some internet companies and individuals are deploying Compan AI as an LLM gateway within their enterprises.

Compan AI workflows are divided into two types:

• Chatflow: Designed for conversational scenarios, including customer service, semantic search, and other conversational applications that require multi-step logic in response construction.

• Workflow: Geared towards automation and batch processing scenarios, suitable for high-quality translation, data analysis, content generation, email automation, and more.

To address the complexity of user intent recognition in natural language input, Chatflow provides question understanding nodes. Compared to Workflow, it adds support for Chatbot features such as conversation history (Memory), annotated replies, and Answer nodes.

To handle complex business logic in automation and batch processing scenarios, Workflow offers a variety of logic nodes, such as code nodes, IF/ELSE nodes, template transformation, iteration nodes, and more. Additionally, it provides capabilities for timed and event-triggered actions, facilitating the

---

Edit this page | Report an issue