Langfuse vs promptfoo
Two of the top observability, side by side: score, setup, license, activity and what each review found.
Langfuse
Tracing, prompt management and evals for LLM apps on ClickHouse
promptfoo
CLI for evaluating and red-teaming prompts, agents and RAG
| What we compare | Langfuse | promptfoo |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 85, widely used | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 87, healthy | 82, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 35.6k | 25.9k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
Langfuse
Ingests traces of LLM calls, retrieval and agent steps via Python and JS/TS SDKs or drop-in OpenAI, LangChain, LlamaIndex, LiteLLM and Vercel AI SDK integrations, then adds prompt versioning with caching, LLM-as-a-judge and code evaluators, datasets and a playground. Stores data in ClickHouse; deploys with docker compose, Helm on Kubernetes, or Terraform for AWS, Azure and GCP. For teams debugging and evaluating LLM apps.
Who it is for: Teams debugging and evaluating LLM apps
Strengths
- Public OpenAPI spec, Postman collection and typed Python and JS/TS SDKs
- Prompt management with server and client caching adds no request latency
- Deployment paths from docker compose to Helm and Terraform templates
- Integrations with Dify, Flowise, Langflow, OpenWebUI, LobeChat, CrewAI, smolagents
Weaknesses
- MIT except the ee folders; enterprise features need a commercial license
- Runs on ClickHouse plus other services; heavier than single-binary tools
- Default compose inherits Docker json-file logging with no rotation; disk can fill
- No Dockerfile at the repo root; images come from Docker Hub
- no GPU
- Compose
- Needs ClickHouse
promptfoo
Runs prompt and model evaluations from a YAML config via promptfoo eval, compares providers side by side, and generates red-team vulnerability reports; promptfoo view opens a local web viewer. Installs with npm, Homebrew or pip, runs in CI/CD, and can scan pull requests for LLM security issues, for developers testing prompts and agents before release.
Who it is for: Developers testing prompts and agents before release
Strengths
- Evals run locally; prompts stay on your machine
- Red-team scans produce vulnerability reports alongside quality evals
- Live reload and caching for fast iteration; npx usage needs no install
- MIT licensed and still open source after joining OpenAI
Weaknesses
- Primarily a CLI; the web viewer is a local results UI, not a multi-user server
- Most providers require an API key; local use needs Ollama or similar
- README is short; config syntax, assertions and providers are only in the docs
- Dockerfile exists at the root but the README gives no Docker instructions
- no GPU
- Docker
- Needs Node.js (npm) or Python (pip), LLM provider API key or Ollama
- Models: OpenAI, Anthropic, Azure, Bedrock, Ollama