Openllm — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Openllm. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 27, 2026 • Sentiment updated: Apr 9, 2026

GitHub Statistics

• GitHub Stars: 12,311
• Forks: 809
• Open Issues: 12
• Primary Language: Python
• License: Apache-2.0
• Weekly Star Velocity: +8 stars/week

Community Sentiment

Community Buzz: OpenLLM in comparison focuses more on building LLM apps for production, as stated on HackerNews. The integration with LangChain + BentoML makes it easy to run multiple LLMs in parallel across multiple GPUs/Nodes, or chain LLMs with other type of AI/ML models, and deploy the entire pipeline on Kubernete (via Yatai or BentoCloud)

Pros & Cons

What People Love

Building LLM apps for production (HackerNews), Integration with LangChain + BentoML (HackerNews), Open-source experience (GitHub)

Common Complaints

Insufficient memory (GitHub), Error indicating insufficient memory (GitHub)

Biggest Positive: Production Ready

Biggest Negative: Memory Issues

Why Openllm Stands Out

OpenLLM is different from alternatives because it allows developers to run any open-source LLMs as OpenAI-compatible APIs with a single command, making it easy to self-host and customize LLMs. The project takes a technical approach by providing a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for cloud deployments. This solves the problem of having to manually set up and configure LLM servers, which can be time-consuming and require significant expertise.

Built With

Build a self-hosted LLM server for custom models — OpenLLM allows running any open-source LLMs as OpenAI-compatible APIs with a single command, Build a chat UI for interacting with LLMs — OpenLLM provides a built-in chat UI at the /chat endpoint for launched LLM servers, Build a cloud deployment for LLMs with Docker and Kubernetes — OpenLLM features a simplified workflow for creating enterprise-grade cloud deployments, Build a custom model repository for LLMs — OpenLLM supports adding custom model repositories to run custom models, Build an OpenAI-compatible API endpoint for LLMs — OpenLLM allows developers to run LLMs as OpenAI-compatible APIs with a single command

Getting Started

1. Install OpenLLM using pip: pip install openllm
2. Set up a Hugging Face token (HF_TOKEN) for gated models: export HF_TOKEN=<your token>
3. Request access to a gated model, such as meta-llama/Llama-3.2-1B-Instruct
4. Start an LLM server using the openllm serve command: openllm serve llama3.2:1b
5. Try interacting with the LLM server using the chat UI or OpenAI-compatible APIs to verify it works

About

Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

Official site: https://bentoml.com

Category & Tags

Category: infrastructure

Tags: bentoml, fine-tuning, llama, llama2, llama3-1, llama3-2, llama3-2-vision, llm, llm-inference, llm-ops, llm-serving, llmops, mistral, mlops, model-inference, open-source-llm, openllm, vicuna

Market Context

Competitive positioning in LLM market