promptfoo
RepositoryFreeLLM eval & testing toolkit
Capabilities14 decomposed
multi-model llm evaluation framework
Medium confidenceEvaluates prompts and LLM outputs across multiple providers (OpenAI, Anthropic, Ollama, local models) using a unified configuration-driven approach. Supports batch testing of prompt variants against test cases with structured result aggregation, enabling systematic comparison of model behavior without provider lock-in.
Provides a unified YAML-driven configuration layer that abstracts provider-specific API differences, allowing users to define prompts once and evaluate across OpenAI, Anthropic, Ollama, and custom endpoints without code changes. Uses a plugin-based provider system rather than hardcoding provider logic.
Unlike Weights & Biases or Langsmith which focus on production monitoring, promptfoo specializes in pre-deployment prompt iteration with lightweight local-first evaluation that doesn't require cloud infrastructure.
assertion-based output validation
Medium confidenceValidates LLM outputs against user-defined assertions (exact match, regex, similarity thresholds, custom functions) applied to each test case result. Supports both deterministic checks and probabilistic assertions, enabling automated quality gates that fail evaluations when outputs don't meet specified criteria.
Implements a composable assertion system supporting exact matching, regex patterns, semantic similarity (via embeddings), and custom functions in a single framework. Assertions are declarative in YAML, allowing non-programmers to define basic checks while enabling advanced users to inject custom logic.
More flexible than simple string matching but lighter-weight than full LLM-as-judge approaches; combines deterministic assertions with optional LLM-based grading for nuanced evaluation.
output caching and deduplication
Medium confidenceCaches LLM outputs for identical prompts and inputs, avoiding redundant API calls and reducing costs. Implements content-based caching that detects duplicate requests across evaluation runs.
Implements transparent content-based caching at the evaluation layer, automatically detecting and reusing identical prompt/input combinations without user configuration. Cache is persistent across evaluation runs.
More transparent than manual caching; reduces costs without requiring users to explicitly manage cache keys or invalidation logic.
integration with version control and ci/cd
Medium confidenceSupports integration with Git workflows and CI/CD systems (GitHub Actions, GitLab CI, Jenkins) via CLI and configuration files. Enables automated evaluation on code changes and enforcement of evaluation gates in pull requests.
Designed for CLI-first integration into CI/CD pipelines, with exit codes and structured output formats enabling seamless integration with existing DevOps tools. Configuration files are version-controlled alongside prompts.
More lightweight than enterprise CI/CD platforms; enables prompt evaluation as a native CI/CD step without requiring specialized integrations or plugins.
custom evaluation metrics and scoring
Medium confidenceAllows users to define custom metrics and scoring functions beyond built-in assertions, implementing domain-specific evaluation logic. Supports JavaScript and Python for custom metric implementation.
Implements custom metrics as first-class evaluation primitives alongside built-in assertions, allowing users to define arbitrary scoring logic without forking the framework. Metrics are configured declaratively in YAML.
More flexible than fixed assertion sets; enables domain-specific evaluation without requiring framework modifications, though with development overhead.
prompt history and versioning
Medium confidenceTracks changes to prompts over time, maintaining a history of prompt versions and enabling comparison between versions. Supports reverting to previous prompt versions and understanding how changes affect evaluation results.
Leverages Git for prompt versioning, avoiding the need for custom version control. Evaluation results can be correlated with Git commits to understand the impact of prompt changes.
Simpler than dedicated prompt management platforms; integrates with existing Git workflows without requiring additional infrastructure.
llm-as-judge grading system
Medium confidenceUses a separate LLM instance to evaluate and score outputs from the primary model under test, implementing chain-of-thought reasoning to assess quality against rubrics. Supports custom grading prompts and scoring scales, enabling semantic evaluation beyond pattern matching.
Implements LLM-as-judge as a first-class evaluation primitive with support for custom grading prompts, chain-of-thought reasoning, and configurable scoring scales. Separates grader model selection from primary model, allowing cost optimization (e.g., using cheaper models for primary task, expensive models for grading).
More sophisticated than regex assertions but more practical than full human evaluation; enables semantic evaluation at scale without manual review, though with inherent LLM grader limitations.
prompt template variable substitution
Medium confidenceSupports parameterized prompts with variable placeholders that are substituted with test case values at evaluation time. Uses a simple template syntax (e.g., {{variable}}) to enable prompt reuse across different inputs without code changes.
Implements lightweight template substitution directly in the evaluation configuration layer, avoiding the need for separate templating engines. Variables are resolved at evaluation time, allowing test case data to drive prompt customization without modifying prompt definitions.
Simpler than Jinja2 or Handlebars templating but sufficient for most prompt parameterization use cases; integrates directly into the evaluation workflow rather than requiring separate preprocessing.
batch evaluation with result aggregation
Medium confidenceExecutes evaluations across multiple test cases and prompt variants in batch mode, collecting results and computing aggregate metrics (pass rate, average scores, statistical comparisons). Results are stored in a structured format enabling post-evaluation analysis and reporting.
Implements batch evaluation as a core workflow primitive with built-in result aggregation and multiple output formats (JSON, CSV, HTML). Results are structured to enable downstream analysis without requiring custom parsing or transformation.
More integrated than running individual API calls; provides immediate aggregation and reporting without requiring external analytics tools, though lacks advanced statistical analysis features.
interactive web-based evaluation dashboard
Medium confidenceProvides a web UI for viewing evaluation results, comparing prompt variants, and drilling into individual test cases. The dashboard displays metrics, model outputs, and assertion results in a visual format, enabling non-technical stakeholders to understand evaluation outcomes.
Implements a lightweight web dashboard that runs locally without external dependencies, making evaluation results immediately accessible without cloud infrastructure. Dashboard is automatically generated from evaluation results without requiring manual configuration.
More accessible than command-line result inspection but simpler than full observability platforms; provides just enough visualization for prompt evaluation without the overhead of enterprise monitoring tools.
cli-based evaluation execution
Medium confidenceProvides a command-line interface for running evaluations, specifying configuration files, and controlling evaluation parameters. Supports both interactive and non-interactive modes, enabling integration with shell scripts and CI/CD pipelines.
Implements a full-featured CLI that mirrors the programmatic API, allowing users to run complex evaluations without writing code. CLI supports both simple one-off commands and complex workflows via configuration files.
More accessible than programmatic APIs for non-developers; integrates naturally into shell scripts and CI/CD pipelines without requiring language-specific SDKs.
provider abstraction layer with plugin system
Medium confidenceAbstracts LLM provider APIs (OpenAI, Anthropic, Ollama, Azure, local models) behind a unified interface, allowing users to switch providers without changing evaluation code. Implements a plugin architecture enabling custom provider implementations.
Implements a clean provider abstraction layer that normalizes API differences across OpenAI, Anthropic, Ollama, and others, allowing configuration-driven provider switching. Plugin system enables custom providers without modifying core code.
More flexible than single-provider tools like OpenAI Playground; enables true provider comparison without vendor lock-in, though with some abstraction overhead.
cost tracking and optimization
Medium confidenceTracks API costs for each evaluation run, breaking down costs by provider and model. Enables cost-aware evaluation decisions, such as using cheaper models for initial testing and expensive models for final validation.
Integrates cost tracking directly into the evaluation workflow, providing real-time cost visibility without requiring external billing tools. Enables cost-aware evaluation decisions at configuration time.
More integrated than external cost tracking tools; provides immediate cost feedback during evaluation planning, though less sophisticated than enterprise cost management platforms.
test case management and organization
Medium confidenceSupports organizing test cases in structured formats (CSV, JSON, JSONL) with metadata and tagging. Enables filtering and grouping of test cases for targeted evaluation runs.
Implements lightweight test case management directly in the evaluation configuration, avoiding the need for external test management tools. Supports multiple formats (CSV, JSON, JSONL) without requiring format conversion.
Simpler than dedicated test management platforms but sufficient for prompt evaluation workflows; integrates directly into the evaluation pipeline without external dependencies.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓ML engineers optimizing prompt performance across model families
- ✓Teams evaluating LLM providers before committing to a single vendor
- ✓Developers building multi-model LLM applications requiring comparative analysis
- ✓QA engineers implementing automated prompt quality gates
- ✓Teams integrating prompt evaluation into CI/CD pipelines
- ✓Developers building domain-specific LLM applications with strict output requirements
- ✓Teams running frequent evaluations with overlapping test cases
- ✓Cost-conscious organizations optimizing API spending
Known Limitations
- ⚠Evaluation speed limited by sequential API calls to external providers; no built-in parallelization across provider calls
- ⚠Cost scales with number of test cases and model evaluations; no caching of identical requests across runs
- ⚠Local model support requires manual setup and configuration; no automated model downloading or environment management
- ⚠Custom assertion functions require JavaScript/Python knowledge; no visual assertion builder
- ⚠Regex and similarity assertions may have false positives/negatives on semantically equivalent but syntactically different outputs
- ⚠No built-in semantic understanding; assertions are pattern-based rather than meaning-based
Requirements
Input / Output
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LLM eval & testing toolkit
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