While browsing GitHub, I discovered a domestic Chinese open-source AI project called BISHENG.
This is an open-source AgentOps platform primarily aimed at enterprises.
The name is derived from Bi Sheng, the inventor of movable type printing. Movable type printing greatly promoted the dissemination of human knowledge. The BISHENG team's vision is to provide strong support for the widespread deployment of intelligent applications.
01
Open-Source Project Overview
BISHENG has already garnered 10K Stars on GitHub.
The first line of the README shows the developers' dedication to the project: Originating from Chinese craftsmanship, we hope to bring more goodness to the world like the DeepSeek and Black Myth: Wukong teams.
It has been adopted by numerous industry-leading organizations and Fortune 500 companies.
This project is like an enterprise-grade LLM DevOps platform.
Open-source address: https://github.com/dataelement/bisheng
It integrates core capabilities such as GenAI workflows, RAG, Agent, unified model management, evaluation, SFT, dataset management, enterprise-grade system management, and observability.
Compared to many open-source AI agent platforms, BISHENG handles two of the most challenging issues better: orchestrating complex business logic and processing unstructured data.
① Unique Workflow
BISHENG provides a visual graph orchestration system, allowing developers to design applications like drawing flowcharts.
It supports loops (Loop), conditional branching (If-Else), and parallel processing. More importantly, it supports Human-in-the-loop (human intervention), such as pausing the workflow after the model generates a draft, waiting for human review and modification before proceeding to subsequent steps.
This design truly enables it to handle serious business scenarios.
Moreover, the philosophy of BISHENG's Workflow is to accomplish the most diverse applications with the fewest component types, which is the core value of low-code, not endlessly pursuing more components.
② High-Precision Document Parsing
BISHENG includes a commercial-grade document parsing model polished by the team for years, which is rare in the open-source community.
It can accurately recognize not only regular documents but also complex tables in financial reports, red seals on contracts, and even messy handwriting, greatly improving the quality of the knowledge base.
③ Expert-Level Universal Agent
BISHENG also has an Agent assistant called LingSi. For thoughts on LingSi, highly recommend reading this article from BISHENG.
This team doesn't just stop at a demo; they are truly aiming for practical usability. To significantly increase the real usage rate of large models in enterprises, that's the most important thing right now.
Its core philosophy is that Agents must have expert-level taste, meaning business experts' preferences and unique knowledge; otherwise, the output is mediocre.
Moreover, LingSi outperforms Manus Pro in scenarios like travel guides, prospectus reading, and product updates collection. Key advantages include: more detailed requirement inquiries, deeper content mining (e.g., executive details in prospectuses), and more precise information gathering (e.g., prioritizing vertical sites).
Besides development, BISHENG also provides model fine-tuning (SFT), performance evaluation, and system monitoring features.
Enterprises don't need to switch between seven or eight tools; they can complete the entire process from data preparation, model tuning, to application deployment on one platform.
Moreover, BISHENG will soon release enterprise edition ima and intelligence subscription features, further boosting large model usage among non-technical staff.
02
How to Use
BISHENG officially recommends deploying with Docker Compose, which has minimal environment dependencies. Before deployment, prepare a decent Linux server (recommended: 4 cores, 16G+).
Download BISHENG
git clone https://github.com/dataelement/bisheng.git
# Enter the installation directory
cd bisheng/docker
# If the system lacks the git command, download the BISHENG code as a zip file.
wget https://github.com/dataelement/bisheng/archive/refs/heads/main.zip
# Unzip and enter the installation directory
unzip main.zip && cd bisheng-main/docker
Start BISHENG
docker compose -f docker-compose.yml -p bisheng up -d
After startup, access http://IP:3001 in your browser.
The login page will appear; register a user. By default, the first registered user becomes the system administrator.
Alternatively, visit www.bisheng.ai to start using BISHENG and boost your work efficiency.