AgentToolDrop

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Promptfoo

Runs evals and red-team scans against prompts, models and agents from a CLI, with a web results viewer

Install with npm

npm install -g promptfoo
promptfoo init --example getting-started

Copied from the project's docs at github.com/promptfoo/promptfoo

Why you'd want it

Promptfoo is a CLI and library that runs automated evaluations of prompts and models, compares providers such as OpenAI, Anthropic, Azure, Bedrock and Ollama side by side, and shows results in a web viewer. It also does red teaming and vulnerability scanning, and can review pull requests for LLM-related security and compliance issues. Evals run locally, it works with any LLM API or programming language, and it plugs into CI/CD.

Best for

  • Regression tests for prompts in CI
  • Comparing models side by side
  • Red teaming LLM apps before launch

Use it when

You want repeatable, local eval runs and security scans that can gate changes in CI.

Skip it if

You need tracing and monitoring of live production traffic; promptfoo is built around evals, red teaming and code scanning rather than observability.

Editor's note

Promptfoo is now part of OpenAI and states it remains open source and MIT licensed.

Related tools

Same category or shared tags, by drop score.

  1. Rank 55

    Inspect

    Evals

    Defines and runs model and agent evaluations in Python from datasets, solvers and scorers

    • score 44
    • 3k stars
    • pushed
    • 0.3.280 on
    • MIT

    Install with pip

    pip install inspect-ai
    Use it when, skip it if, stats for Inspect

    Why you'd want it

    Inspect, from the UK AI Security Institute and Meridian Labs, builds evaluations from composable datasets, solvers (from a single generate call to multi-turn agents with tools) and scorers, and runs them with inspect eval. It supports custom and MCP tools plus built-in bash, python, web browsing and computer tools, can evaluate external agents like Claude Code, Codex CLI and Gemini CLI, and sandboxes untrusted model code in Docker, Kubernetes, Modal and other systems. It ships over 200 pre-built benchmark evals, support for over 20 model providers, the Inspect View log viewer and a VS Code extension.

    Best for

    • Benchmarking models and agents
    • Agentic evals that run code in sandboxes
    • Research and safety evaluation teams

    Use it when

    You want to measure how a model or agent performs on a task suite, including agentic tasks with tools and sandboxed code, and compare results across providers.

    Skip it if

    You need tracing of a live production app; Inspect is an evaluation framework, and evals are written in Python.

    Stats

    Stars
    3,005
    Last push
    Release
    0.3.280,
    License
    MIT
    Pricing
    Open source
    Works with
    VS Code
    Drop score
    44 of 100

    Full stat block for Inspect

  2. Rank 5

    Langfuse

    Evals

    Traces LLM calls and agent steps, then runs evals, datasets and prompt versioning on top of them

    • score 85
    • 35.8k stars
    • pushed
    • v4.59.0 on
    • MIT

    Install the Python SDK

    pip install langfuse
    Use it when, skip it if, stats for Langfuse

    Why you'd want it

    Langfuse instruments an app through Python and JS/TS SDKs or integrations such as the OpenAI SDK, LangChain, LlamaIndex, Vercel AI SDK and Mastra, and records LLM calls, retrieval and agent actions as traces you can inspect. The same platform covers prompt management with server and client caching, datasets for pre-deployment tests, and evaluations via LLM-as-a-judge, code evaluators, user feedback and manual labeling. You can use Langfuse Cloud or self-host it with Docker Compose, on a VM or on Kubernetes via Helm.

    Best for

    • Teams that want to self-host LLM observability
    • Tracing, prompts and evals in one place
    • Python and JS/TS stacks

    Use it when

    You need to see what your LLM app or agent did on each request and want to evaluate and iterate on prompts against the same traces.

    Skip it if

    You do not want to run a server stack yourself and cannot use a hosted account; self-hosting means running the Docker Compose or Kubernetes deployment. Code under the ee/ directories is not MIT licensed.

    Stats

    Stars
    35,832
    Last push
    Release
    v4.59.0,
    License
    MIT
    Pricing
    Open source
    Works with
    Docker
    Drop score
    85 of 100

    Full stat block for Langfuse

  3. Rank 24

    Arize Phoenix

    Evals

    Traces LLM and agent apps over OpenTelemetry and runs evals, datasets and experiments in a local UI

    • score 63
    • 11.8k stars
    • pushed
    • arize-phoenix-v20.21.4 on
    • Elastic License 2.0

    Install with pip

    pip install arize-phoenix
phoenix serve
    Use it when, skip it if, stats for Arize Phoenix

    Why you'd want it

    Phoenix collects traces through OpenTelemetry-based OpenInference instrumentation, with integrations for frameworks such as the OpenAI Agents SDK, Claude Agent SDK, LangGraph and Vercel AI SDK across Python, JavaScript, Java and Go. On top of traces it offers LLM-based evals, versioned datasets, experiments, a prompt playground and prompt management. It runs locally with phoenix serve, in a container or on Kubernetes, and exposes a remote MCP endpoint so Claude Code, Cursor and other MCP clients can query traces, datasets and experiments.

    Best for

    • Local trace debugging during development
    • OpenTelemetry-based stacks
    • Self-hosted eval and experiment tracking

    Use it when

    You want an observability and eval UI on your own machine or infrastructure, instrumented with OpenTelemetry rather than a vendor-specific SDK.

    Skip it if

    You need an OSI open source license or want to offer it as a hosted service; the Elastic License 2.0 forbids that. For a managed platform Arize points to its separate Arize AX product.

    Stats

    Stars
    11,839
    Last push
    Release
    arize-phoenix-v20.21.4,
    License
    Elastic License 2.0
    Pricing
    Free
    Works with
    Docker
    Drop score
    63 of 100

    Full stat block for Arize Phoenix

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/promptfoo.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'