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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes the same prompt across multiple LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, local models) in parallel, collecting structured outputs with metadata (latency, token counts, cost). Uses a provider registry pattern with pluggable provider implementations that normalize API differences into a unified interface, enabling side-by-side comparison of model behavior on identical inputs.
Unique: Uses a pluggable provider registry pattern where each provider (OpenAI, Anthropic, Bedrock, Ollama, HTTP, Python scripts) implements a normalized interface, allowing new providers to be added without modifying core evaluation logic. Tracks cost per provider using model-specific pricing tables, enabling ROI analysis across providers.
vs alternatives: Broader provider support (10+ integrations including local models) and native cost tracking than competitors like LangSmith or Weights & Biases, with zero-config local execution via Ollama
Defines test assertions (exact match, similarity, regex, LLM-based grading) that automatically evaluate whether model outputs meet criteria. Supports custom evaluator functions (JavaScript, Python, HTTP webhooks) that receive the prompt, output, and test case metadata, returning a pass/fail score and optional details. Assertions are composable and can be chained to create complex evaluation logic without writing test harnesses.
Unique: Supports four distinct assertion types (exact, similarity, regex, LLM-rubric) plus arbitrary custom evaluators (JS functions, Python scripts, HTTP webhooks), allowing teams to mix deterministic checks with LLM-based subjective evaluation in a single test suite. Custom evaluators receive full test context (prompt, output, variables, metadata) enabling sophisticated domain-specific grading.
vs alternatives: More flexible assertion model than basic string matching in competitors; native support for LLM-as-judge grading without requiring separate evaluation pipeline setup
Stores evaluation results in local SQLite database or cloud storage (AWS S3, Google Cloud Storage, etc.), enabling historical tracking of prompt quality over time. Results include full metadata (prompt, model, variables, outputs, scores, latency, cost). Enables trend analysis (e.g., 'pass rate improved 5% over last month') and regression detection by comparing against previous baselines.
Unique: Stores evaluation results in local SQLite or cloud storage with full metadata (prompt, model, variables, outputs, scores, latency, cost). Enables historical tracking and trend analysis. Results can be queried to detect regressions by comparing against previous baselines.
vs alternatives: Integrated persistence (not a separate tool); supports both local and cloud storage; enables historical tracking and regression detection without external databases
Provides native integration with AWS Bedrock (Claude, Llama, Mistral models), Google Vertex AI, Azure OpenAI, and other cloud providers. Handles authentication (IAM roles, API keys), model selection, and parameter mapping. Enables teams to test against cloud-hosted models without writing custom provider code. Supports streaming responses for real-time output evaluation.
Unique: Native integration with AWS Bedrock, Google Vertex AI, and Azure OpenAI with support for cloud provider authentication (IAM roles). Handles model selection, parameter mapping, and streaming responses. Enables teams to test cloud-hosted models without custom integration code.
vs alternatives: Broader cloud provider support than competitors; native IAM role support for better security; integrated streaming response handling
Executes Python scripts (3.7+) and Node.js scripts (18+) as providers, passing prompt and variables as command-line arguments or stdin. Scripts can implement arbitrary logic (e.g., calling local models, preprocessing inputs, routing to multiple models). Output is captured from stdout and parsed as JSON or plain text. Enables teams to test custom inference logic without modifying promptfoo.
Unique: Supports Python and Node.js scripts as first-class providers, receiving prompt and variables as command-line arguments or stdin. Scripts can implement arbitrary logic (preprocessing, routing, local model calls). Output is captured from stdout and parsed as JSON or plain text.
vs alternatives: More flexible than HTTP provider for local execution; enables testing of custom inference logic without external servers; supports both Python and Node.js
Provides native integration with Ollama (local LLM inference engine) and compatible local model servers (LLaMA.cpp, LocalAI). Connects to local HTTP endpoints, enabling teams to test open-source models (Llama, Mistral, etc.) without cloud API costs or latency. Supports model selection, parameter tuning, and streaming responses.
Unique: Native Ollama integration with support for local model servers (LLaMA.cpp, LocalAI). Connects to local HTTP endpoints, enabling zero-cost local inference. Supports model selection, parameter tuning, and streaming responses.
vs alternatives: Purpose-built for local model testing; enables cost-free evaluation of open-source models; supports multiple local model servers (Ollama, LLaMA.cpp, LocalAI)
Provides CLI and web UI search/filtering capabilities to navigate large evaluation result sets. Supports filtering by test case name, provider, model, pass/fail status, and custom metadata. Search uses full-text indexing for fast queries. Enables teams to quickly find specific test cases or failure patterns without manually reviewing all results.
Unique: Provides both CLI and web UI search/filtering with full-text indexing. Supports filtering by test case name, provider, model, status, and custom metadata. Enables fast navigation of large result sets without manual review.
vs alternatives: Integrated search (not a separate tool); supports both CLI and web UI; enables efficient navigation of large result sets
Generates adversarial test cases using attack strategies (jailbreaks, prompt injection, prompt leaking, toxicity, bias) to probe LLM vulnerabilities. Uses a plugin-based attack provider system where each strategy (e.g., 'crescendo jailbreak', 'SQL injection') generates variations of inputs designed to trigger unsafe behavior. Results are graded using guardrails (safety checks) to identify which attacks succeeded, producing a vulnerability report.
Unique: Implements a modular attack strategy system where each vulnerability type (jailbreak, injection, prompt leaking, toxicity, bias) is a pluggable provider that generates test cases. Strategies can be composed and parameterized (e.g., 'crescendo jailbreak with 5 iterations'), and results are graded against guardrails (safety checks) to produce a structured vulnerability report.
vs alternatives: Purpose-built red-teaming system integrated into evaluation pipeline (not a separate tool); supports custom attack strategies via plugins; generates reproducible adversarial test cases that can be version-controlled and shared
+7 more capabilities
Enables an LLM agent to read, analyze, and modify files in a local codebase through a sandboxed execution environment. The agent receives file contents as context, generates code modifications or new files, and applies changes back to disk with isolation guarantees. Uses OpenAI's API for reasoning about code structure and intent before executing file operations.
Unique: Implements sandboxed file operations at the CLI level with direct OpenAI integration, allowing agents to reason about and modify code without requiring a full IDE or language server — trades IDE-level precision for lightweight, portable execution in terminal environments
vs alternatives: Lighter and faster to deploy than GitHub Copilot for Workspace or Cursor, with explicit sandboxing and agent-driven multi-file edits rather than completion-based suggestions
Allows the LLM agent to execute shell commands (bash, zsh, PowerShell) within the sandboxed environment and receive stdout/stderr output back into the agent's reasoning loop. The agent can chain commands, parse output, and make decisions based on execution results. Execution is scoped to prevent destructive operations on system files outside the project directory.
Unique: Integrates shell execution directly into the agent's reasoning loop with output feedback, enabling agents to validate changes in real-time rather than blindly generating code — uses command results as context for next reasoning step
vs alternatives: More reactive than static code generation tools like Copilot; agents can run tests and fix failures iteratively, similar to Devin or Claude but in a lightweight CLI form
Automatically reads and aggregates relevant files from the codebase into a single context window for the LLM agent, using heuristics like import statements, file proximity, and user-specified patterns to determine relevance. The agent receives a coherent view of related code without manually specifying every file, enabling cross-file reasoning and refactoring.
Codex CLI scores higher at 75/100 vs promptfoo at 58/100.
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Unique: Uses import statement parsing and file proximity heuristics to automatically assemble relevant context without requiring manual file lists, enabling agents to reason about cross-file changes without explicit user guidance on scope
vs alternatives: More automated than manual context specification in ChatGPT or Claude, but less precise than full AST-based dependency analysis in IDEs like VS Code with language servers
Interprets high-level natural language instructions from the user (e.g., 'refactor this function to use async/await' or 'add error handling to all API calls') and translates them into concrete code modification tasks for the agent. Uses OpenAI's language understanding to disambiguate intent, infer scope, and generate specific modification plans before executing changes.
Unique: Leverages OpenAI's language understanding to infer scope and intent from vague instructions, enabling agents to ask clarifying questions or propose execution plans before modifying code — treats natural language as a first-class interface rather than a fallback
vs alternatives: More flexible than template-based code generation; similar to Copilot's chat interface but with explicit task decomposition and agent-driven execution rather than suggestion-based interaction
Implements a multi-turn loop where the agent executes changes, observes results (test failures, linter errors, runtime issues), and refines modifications based on feedback. The agent can retry failed operations, adjust code based on error messages, and converge on a working solution without human intervention between iterations.
Unique: Closes the loop between code generation and validation by feeding test/linter output back into the agent's reasoning, enabling autonomous error recovery and iterative improvement — treats failures as learning signals rather than terminal states
vs alternatives: More autonomous than Copilot's suggestion-based workflow; similar to Devin's iterative approach but lighter-weight and CLI-based rather than IDE-integrated
Enables the agent to create new files that conform to the existing codebase structure, naming conventions, and architectural patterns. The agent analyzes existing files to infer directory organization, module structure, and style conventions, then generates new files that fit seamlessly into the project without manual specification of paths or formatting.
Unique: Analyzes existing codebase to infer structure and conventions, then applies them to new file generation without explicit configuration — enables agents to create files that fit the project's architecture automatically
vs alternatives: More context-aware than generic code generators or scaffolding tools; similar to IDE project templates but learned from actual codebase rather than predefined templates
Provides seamless integration with OpenAI's API, allowing users to select between available models (GPT-4, GPT-3.5-turbo, etc.) and automatically handles authentication, request formatting, and response parsing. The CLI abstracts away API details while exposing model selection as a configuration option, enabling users to trade off cost vs. reasoning capability.
Unique: Abstracts OpenAI API complexity into CLI configuration, allowing users to switch models via command-line flags or environment variables without code changes — treats model selection as a first-class configuration concern
vs alternatives: Simpler than building custom OpenAI integrations; less flexible than frameworks like LangChain that support multiple providers, but more lightweight and focused
Maintains conversation history and agent state across multiple turns, allowing the agent to reference previous instructions, modifications, and results. The CLI stores interaction logs and can resume interrupted sessions or provide context for follow-up instructions without requiring users to repeat information.
Unique: Persists agent state and conversation history locally, enabling multi-turn interactions and session resumption without requiring cloud infrastructure or external state stores — trades cloud convenience for local control and privacy
vs alternatives: More persistent than stateless API calls; similar to ChatGPT's conversation history but local and focused on code modification tasks
+1 more capabilities