ChainForge vs PromptEval

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.

PromptEval is a web and API SaaS platform for evaluating and optimizing large-language-model prompts. It assigns a reproducible score from 0 to 100, with diagnostics covering clarity, specificity, structure and robustness, then identifies issues, warnings, strengths and targeted fixes. A token optimizer compresses prompts while preserving intent and reports the reduction percentage. Its versioned library keeps prompt history, score changes and diffs. The A/B Playground compares two prompts across as many as seven criteria using a user-provided API key. An official GitHub Action can fail pull requests after score drops, contradictions or production regressions. Pro and Team users can serve prompts by slug through an API endpoint, with changes taking effect in about 60 seconds. Plans include a free tier, Basic at $9 a month, Pro at $19 a month, and custom-priced Team.