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dataelement/bisheng

BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more. observed · 2026-08-28

github.com/dataelement/bisheng · homepage · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

94/100

  • Activity 99
  • Release rhythm 97
  • Longevity 78
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 11.0
  • age_days: 1101
  • days_rel: 22
  • days_push: 7
  • n_releases_24m: 31

Full methodology

Adoption not part of the score

11911 stars · 1950 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

BISHENG is an open-source LLM DevOps (LLMOps) platform for building enterprise AI applications, offering GenAI workflow orchestration, RAG, agents, unified model management, evaluation, SFT/fine-tuning, dataset management, and observability. It is a self-hosted Python/React platform used by large enterprises, including Fortune 500 companies.

Use cases

  • build enterprise LLM applications with a visual workflow
  • create RAG pipelines over company documents
  • orchestrate AI agents with human-in-the-loop review
  • manage and evaluate multiple LLM models centrally
  • fine-tune models with SFT and manage datasets
  • deploy a self-hosted chatbot platform for my company
  • add observability to LLM app deployments
  • extract data from documents with OCR in AI workflows

When to choose

  • you need an enterprise-grade, self-hosted LLMOps platform combining workflows, RAG, agents, and evaluation
  • you want human-in-the-loop intervention inside AI workflows
  • you need unified model management plus fine-tuning (SFT) and dataset tooling in one platform
  • your organization requires enterprise-level system management and observability for GenAI apps

When to avoid

  • you only need a lightweight library or SDK to call LLMs from code
  • you want a fully managed cloud service with no self-hosting overhead
  • you need a minimal single-purpose RAG or chatbot tool rather than a full platform
  • your team cannot operate a multi-component Python/Docker deployment

Facets

application · maturity active

agent-framework rag llm-inference workflow-automation chatbot ocr machine-learning data-science monitoring etl large-language-models chatbots developer-tools self-hosted erp python self-hosted cross-platform llmops llm-devops genai workflow-orchestration fine-tuning sft evaluation enterprise-ai model-management observability human-in-the-loop ai-agents retrieval-augmented-generation natural-language-processing automation docker web-server

1 source

Member repositories

RepositoryRoleHealth v2
dataelement/bishengmain94

For agents

markdown · JSON · MCP: product_card(name="dataelement/bisheng")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem