8 Best Promptfoo Alternatives in 2026 (Open Source)

Promptfoo — Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and. Unlike LangSmith (production observability) or Langfuse (logging), promptfoo is the only open-source tool combining eval + red teaming + CI/CD code scanning — now backed by OpenAI while remaining fully MIT-licensed

These 8 open-source tools do the same job. They are ordered by how closely they match Promptfoo, with live GitHub data so you can see which projects are actively maintained.

1. 1. ChainForge

   An open-source visual programming environment for battle-testing prompts to LLMs.

   What sets it apart: vs PromptFoo/LangSmith: visual data-flow environment for prompt engineering with built-in cross-model comparison, permutation testing, and statistical visualization

   Best for: Systematic prompt evaluation across multiple LLMs; Research teams comparing model performance with visual analytics

   ⭐ 3.0kPromptfoo vs ChainForge →Full profile →

2. 2. Langfuse

   🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

   What sets it apart: Unlike LangSmith (LangChain-specific) or Helicone (proxy-based), Langfuse is fully open-source, framework-agnostic, and self-hostable, combining tracing, prompt management, evaluations, and datasets in a single platform built on ClickHouse for scalable production use.

   Best for: Teams operating production LLM applications who need tracing, prompt management, and evaluation in one platform; Organizations requiring self-hosted LLM observability for data privacy compliance

   ⭐ 35.2kPromptfoo vs Langfuse →Full profile →

3. 3. phoenix

   AI Observability & Evaluation

   What sets it apart: Full-stack AI observability (tracing + eval + datasets + prompt management) in one open-source platform — vs LangSmith which is closed-source and LangChain-specific

   Best for: Debugging and monitoring LLM applications in production; Systematic prompt engineering and experiment tracking

   ⭐ 11.7kPromptfoo vs phoenix →Full profile →

4. 4. Agenta

   The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.

   What sets it apart: Unified open-source LLMOps platform combining prompt playground, version control, 20+ evaluators, and OTel-native observability in one tool — vs separate tools for each

   Best for: Teams needing integrated prompt management + evaluation + observability; Product teams collaborating with SMEs on prompt engineering; Organizations wanting open-source LLMOps alternative

   ⭐ 4.8kPromptfoo vs Agenta →Full profile →

5. 5. DeepEval

   The LLM Evaluation Framework

   What sets it apart: Most comprehensive open-source LLM eval framework with 30+ research-backed metrics including agentic, RAG, multi-turn, MCP, and multimodal — vs Ragas (RAG-only) or custom eval scripts

   Best for: Teams needing comprehensive LLM/agent evaluation pipelines; CI/CD integration for LLM app quality gates; RAG pipeline evaluation and optimization

   ⭐ 18.5kPromptfoo vs DeepEval →Full profile →

6. 6. UpTrain

   UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform ro

   What sets it apart: vs generic eval tools: 20+ preconfigured evaluations with customizable prompts, few-shot examples, and scenario descriptions — all running locally for data privacy with root cause analysis on failures

   Best for: RAG system evaluation and quality assurance; LLM application testing before production deployment; Safety and security testing for prompt injection vulnerabilities

   ⭐ 2.4kPromptfoo vs UpTrain →Full profile →

7. 7. OpenAI Evals

   Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

   Best for: Teams systematically evaluating LLM performance across model versions; Prompt engineers needing no-code YAML-based evaluation workflows; Organizations building quality assurance pipelines for LLM applications

   ⭐ 19.5kPromptfoo vs OpenAI Evals →Full profile →

8. 8. Opik

   Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

   What sets it apart: Full-lifecycle LLM platform combining tracing, evaluation, and optimization — uniquely includes Agent Optimizer and Guardrails alongside observability, unlike trace-only tools like LangSmith

   Best for: Teams needing end-to-end LLM observability from development to production; Automated LLM evaluation and quality assurance in CI/CD pipelines

   ⭐ 22.3kPromptfoo vs Opik →Full profile →