ChainForge vs PromptPex

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.

PromptPex is an open-source tool for developers who keep AI prompts in their codebase and want to examine how models follow prompt requirements. It treats prompts as functions, extracts output rules expressed in natural language, and generates unit tests to check whether a model follows those rules. It can also evaluate prompt performance across models, using an LLM to judge whether outputs meet stated requirements. Test generation can be shaped with scenarios, expansions, custom instructions, and effort levels. The CLI accepts Markdown or text templates, Prompty files, and GitHub Models prompt YAML files. Setup lists OpenAI, Azure OpenAI, GitHub Models, and Ollama as supported providers. PromptPex is packaged as an npm CLI and can run in Docker; its setup guide also describes a local web interface. Documented integrations include OpenAI Evals, Azure OpenAI Stored Completions, GitHub Models Evals, and GitHub Actions. Microsoft is identified as the maker. The project is intended for research, and its maker advises against downstream use without additional evaluation of robustness, safety, harm, and bias. Vague prompts can lead to suboptimal results, and rule extraction may be less accurate with complex or idiomatic language.