ChainForge is a visual programming environment for battle-testing prompts to large language models.
The tool addresses the challenge of systematically evaluating and comparing LLM prompts at scale. Rather than ad-hoc testing in individual chatbots, ChainForge provides a data flow interface where you can query multiple LLMs simultaneously with different prompt variations and model settings, then compare response quality across all permutations. You can define custom evaluation metrics as scoring functions and immediately visualize results. The environment includes built-in AI features to generate synthetic test data and starter code for evaluation logic, streamlining the entire workflow.
ChainForge suits teams doing serious prompt engineering and LLM evaluation work. It works well for projects where you need to systematically test prompt variations, compare multiple models, or establish quantitative metrics for response quality. The tool supports both cloud-based LLM APIs and locally-run models via Ollama. Retrieval-augmented generation capabilities run client-side in the browser with no extra setup, though server-side RAG features like persistent vector stores and hosted embeddings are available as an optional installation. The web version at chainforge.ai/play offers limited functionality; local installation unlocks Python code evaluation, environment variable-based API key loading, and full RAG features.
The project maintains active community engagement through a Discord server. Development activity shows consistent attention to expanding evaluation capabilities and supporting diverse LLM deployment scenarios. The codebase is built on ReactFlow for the visual interface and Flask for the backend, with TypeScript as the primary language. The project provides numerous example evaluation flows derived from established LLM benchmarks to help users understand what's possible.
