Promptfoo¶

What it is¶

Promptfoo is an open-source (MIT) CLI tool and library for evaluating, testing, and securing LLM prompts and models. It allows you to run systematic test cases across multiple providers and models, with a heavy focus on AI Security and Red Teaming. While the core CLI is free and self-hostable, a paid enterprise tier exists for governance and team features.

What problem it solves¶

It solves the problem of "prompt regression" and security vulnerabilities by providing a framework for regression testing and automated red teaming. It allows you to quantify how changes to a prompt affect output quality and safety across many different test cases, preventing silent failures when updating to models like Claude 4.8 or GPT-5.5.

Where it fits in the stack¶

Benchmarking / Eval / Security. It is a critical tool for the Dev-Workflow AI Assisted cycle, acting as the bridge between development and production-ready prompts.

Typical use cases¶

Prompt Comparison: Testing the same input against 10 different versions of a prompt.
Model Comparison: Testing the same prompt against GPT-5.5, Claude 4.8 Opus, and Llama 4 Maverick.
Red Teaming: Identifying prompt injection, data exfiltration, and permission misuse vulnerabilities.
CI/CD Integration: Automatically running a test suite before deploying a prompt change.
MCP Tool Testing: Verifying that agents correctly call MCP tools from servers like Grafana or New Relic.

Strengths¶

Fast and Local: Runs entirely on your machine; no external platform required for the core CLI.
Flexible Assertions: Support for JS, Python, and LLM-graded assertions (e.g., using llm-rubric).
Extensive Provider Support: Works with OpenAI, Anthropic, Ollama, Azure, and more.
AI Security Focus: Built-in scanners for 50+ vulnerability types, including specialized red teaming for agentic workflows.

Limitations¶

CLI-First: While it has a web viewer, the core experience is command-line based.
Configuration Overhead: Complex test suites require significant YAML/JSON definition effort.
Acquisition Uncertainty: OpenAI's acquisition of Promptfoo in March 2026 has raised questions about the long-term open-source roadmap, though the MIT core remains available.

When to use it¶

To systematically improve the reliability and safety of your LLM prompts.
To prevent regressions when updating models or prompts in an automation workflow.
To perform automated security audits of AI agents before production deployment.

When not to use it¶

For one-off, casual chats with an LLM.
If you require a purely visual, no-code evaluation environment.

Getting started¶

Installation¶

npm install -g promptfoo

Initialization¶

# Initialize a new project
promptfoo init

Basic Evaluation¶

Define your prompts and tests in promptfooconfig.yaml.
Run the eval:
```
promptfoo eval
```

CLI examples¶

Running Red Teaming Scans¶

# Run a red team evaluation against a specific target
promptfoo redteam run --config redteam.yaml

Comparing Models Side-by-Side¶

# Compare GPT-5.5 and Claude 4.8
promptfoo eval -p "Summarize: {{text}}" -r openai:gpt-5.5 -r anthropic:messages:claude-4-8-opus-20260528 -v text="MCP 3.0 protocol details"

Testing MCP Tools¶

Promptfoo supports MCP Proxy for evaluating tools:

# Evaluate an agent using a local MCP server
promptfoo eval --mcp-server http://localhost:8000/mcp

API examples¶

Programmatic Evaluation (TypeScript)¶

import promptfoo from 'promptfoo';

const results = await promptfoo.evaluate({
  prompts: ['Summarize this: {{text}}'],
  providers: ['openai:gpt-5.5'],
  tests: [
    {
      vars: { text: 'The Model Context Protocol (MCP) is an open standard...' },
      assert: [{ type: 'icontains', value: 'MCP' }],
    },
  ],
});

console.log(results);

Custom Python Assertion¶

def check_length(output, vars):
    # Ensure the output is concise for daily digests
    return len(output) < 500

# Used in promptfooconfig.yaml as:
# assert:
#   - type: python
#     value: file://assertions.py:check_length

LangSmith — Enterprise observability and evaluation.
AgentOps — Session replays and execution graphs for agents.
Ragas — Evaluation framework for RAG pipelines.
Model Context Protocol (MCP) — Protocol for extending agent capabilities.
Ollama — Local inference engine for evaluation.
Claude — Frontier model frequently used as a judge.
Grok-3 — Real-time data search for evaluation context.
Dev-Workflow AI Assisted — Playbook for using evals in development.

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-06-28
Confidence: high