Promptfoo vs Worktrunk: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Promptfoo and Worktrunk — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Promptfoo
Promptfoo
CLI and web tool for testing, evaluating, red‑teaming, and monitoring LLM prompts and outputs to catch regressions and vulnerabilities.
Key features
- Red-Teaming & Vulnerability Scanning: Declarative red‑team tests and automated scans to surface prompt injections, unsafe completions, and other model security risks across providers.
- Evaluations & Regression Detection: Run reproducible eval suites and compare outputs before/after changes to detect regressions, with CI/CD and GitHub Action integration for automated checks on PRs.
- Multi-Provider Model Comparison: Execute the same tests across multiple model providers and families (e.g., OpenAI, Claude, Gemini, Llama) to compare quality and safety consistently.
- CLI and Web UI: Command‑line tools for running tests and a web 'view' UI to inspect prompts, final rendered prompts, outputs, and structured results in tabular form.
- Declarative Configs & Templating: Use promptfooconfig.yaml with Nunjucks templating and custom filter plugins to generate complex prompts and test permutations programmatically.
- Extensible Provider & Plugin System: Add or customize providers, local execution, or custom filters (JS/Python) to adapt tests to specific stacks or private model endpoints.
- Docker Distribution & Local Execution: Official container images and local execution modes enable isolated, reproducible runs and CI friendliness.
- GitHub & CI Integrations: Official GitHub Action and CI-friendly tooling to automatically post evaluations on PRs and enforce prompt quality gates.
- Command‑line interface and library for running declarative evals and tests
- Red‑teaming and vulnerability scanning for LLM outputs
- Declarative configuration via promptfooconfig.yaml (prompts, providers, filters, tests)
- Support for templated prompts using Nunjucks and custom filter modules
- Providers for multiple model backends (OpenAI and others; compare GPT, Claude, Gemini, Llama, etc.)
- Docker images published to GHCR (multi‑arch support: linux/amd64, linux/arm64, etc.)
- Web UI (src/app) that integrates with `promptfoo view` for inspecting outputs and final prompts
- CI/CD integrations including an official GitHub Action for evals on PRs
- Developer productivity features: live reload, caching, npm scripts for local dev
- Configurable Python executable (PROMPTFOO_PYTHON) and language‑agnostic test data (supports Python, JavaScript, others)
Best for
- Red‑teaming LLM integrations to find prompt injections, unsafe outputs, and info‑leakage before release.
- Regression testing in CI to automatically detect when a prompt or model update degrades output quality or safety on pull requests.
- Comparing model performance across providers and model families to choose the best model for a given task or guardrail requirements.
- Building test-driven prompt development workflows where prompts are versioned, evaluated, and iterated using reproducible eval suites.
- Adding automated before/after eval diffs on GitHub PRs to give reviewers quantitative and qualitative signal about prompt edits.
- Validating agents, RAG pipelines, and LLM apps end‑to‑end by running scenario-based tests and inspecting final rendered prompts and outputs.
- Test‑driven prompt engineering and automated evaluation of model outputs
- Red‑teaming and security testing of language model behavior
- Regression testing of prompts and model changes via CI/CD and GitHub Actions
- Comparing performance across multiple model providers
- RAG (retrieval augmented generation) and agent testing in local/dev environments
- Integrating automated evals into PR workflows to produce before/after views of prompt edits
Worktrunk
max-sixty
A Rust CLI that makes git worktrees as easy as branches, built for running several AI coding agents in parallel without collisions.
Key features
- Branch-Addressed Worktrees: wt switch, wt remove, and wt list refer to worktrees by branch name with paths computed from a configurable template, replacing multi-step git worktree incantations.
- Agent Launch in One Command: wt switch -c -x claude <branch> creates the worktree, enters it, and starts the agent in a single invocation.
- Lifecycle Hooks: Run commands automatically on create, pre-merge, and post-merge to automate setup and teardown for every new worktree.
- LLM Commit Messages: Generates commit messages from the diff so parallel agent branches stay legible without hand-writing every message.
- One-Command Merge Workflow: Squash, rebase, merge, and clean up the worktree and branch in a single step rather than a sequence of git commands.
- Interactive Picker: Browse worktrees with streaming CI status alongside diff, log, PR, and comment previews before switching.
- Shared Build Caches: wt step copy-ignored gives ten worktrees their own target/ and node_modules/ without rebuilding or copying, using reflinks on APFS, btrfs, and XFS.
- Per-Worktree Dev Servers: A hash-port template filter assigns each worktree a unique port so parallel dev servers do not conflict.
Best for
- Parallel Agent Runs: Give each of five to ten concurrently running AI coding agents its own worktree so their edits never collide.
- Fast Branch Context Switching: Jump between in-flight changes by branch name instead of navigating sibling directories by path.
- Pull Request Review: wt switch pr:123 checks out a pull request's branch directly for local inspection or testing.
- Monorepo Iteration: Share heavy build artifacts across many worktrees so each new branch is usable immediately instead of after a full rebuild.
- Automated Branch Setup: Use create hooks to install dependencies, copy env files, or start services whenever a worktree is made.
- Multi-Branch Status Review: wt list --full shows CI status and AI-generated summaries for every active branch in one view.