ChainForge vs OpenPrompt
ChainForge
chainforge.aiOpenPrompt
github.comSide by side
| Figure | ChainForge | OpenPrompt |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Prompt Generators | 8.6 (#3) | 8.1 (#8) |
| Purpose | ChainForge is an open-source visual programming environment for prompt engineering, LLM evaluation and experimentation. | OpenPrompt is an open-source framework for building prompt-learning pipelines for downstream NLP tasks. |
Plans
In their own words
ChainForge
ChainForge is an open-source visual programming environment for prompt engineering, LLM evaluation, and experimentation. It can query multiple language models at once and compare results across prompts, models, and model settings. Responses can be scored with code-based or LLM-based evaluators, then plotted for review. Chat Turn nodes support continuing multiple conversations, templating chat messages, and handing a conversation from one model to another. The tool imports JSONL, XLSX, and CSV files and exports inspected response data as XLSX. Listed providers include OpenAI, Anthropic, Google Gemini and PaLM, Hugging Face, Azure OpenAI, Amazon Bedrock, and Ollama. A local installation can add custom providers written as Python completion functions. The web version has a limited feature set; local use supports API keys from environment variables, Python evaluation code, and locally hosted Alpaca or Llama models via Dalai. Web users must set API keys each time they load the app, and the documentation says keys are not stored. ChainForge is available on the web or self-hosted, is free and open source, and is described as an open beta in active development.
OpenPrompt
OpenPrompt is a free, open-source framework for building prompt-learning pipelines for downstream natural language processing tasks. It brings methods for prompt templating, verbalizing, and optimization into one framework, while allowing users to devise and test their own methods. It can load pretrained language models from Hugging Face Transformers. A PromptModel combines such a model with one or more templates and verbalizers for training and inference. Documented processors cover text classification, entity typing, relation classification, language inference, and conditional generation. Template options include manual, prefix, P-tuning, PTR, and mixed approaches; verbalizer choices include one-to-one, manual, automatic, knowledgeable, PTR, generation, and soft forms. The project provides scripts for downloading benchmark datasets and installation instructions for pip or its GitHub source. Its listed tested requirements are Python 3.8+ and PyTorch 1.8.1+. OpenPrompt is licensed under Apache-2.0 and supports Linux, macOS, Windows, and self-hosted use. The README cautions that some documentation may no longer reflect major project changes.