| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 10 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
Implements configurable access control for file reading through a path allowlist/blocklist security model that validates all requested paths against configured boundaries before exposing file contents. Uses TypeScript SDK's tool registration pattern to expose read_file and list_directory tools with automatic path normalization and symbolic link resolution, preventing directory traversal attacks while maintaining transparent access semantics for LLM clients.
Unique: Uses MCP's native tool registration with declarative path allowlisting rather than OS-level permissions, enabling fine-grained LLM-specific access control that survives across different execution contexts and doesn't require filesystem-level changes
vs alternatives: More granular than OS-level file permissions and easier to configure per-client than containerization, while remaining simpler than full capability-based security models
Provides write_file tool that creates or overwrites files with optional safety checks for preventing accidental data loss. Implements atomic write semantics by writing to a temporary file first, then renaming to target path, ensuring partial writes don't corrupt existing files. Respects the same path validation layer as read operations, preventing writes outside configured boundaries.
Unique: Combines MCP tool semantics with filesystem-level atomic writes (temp-then-rename pattern) to guarantee consistency even if the MCP client crashes mid-operation, unlike simple write implementations that may leave partial files
vs alternatives: More reliable than direct file writes because atomic semantics prevent corruption, while remaining simpler than full transactional filesystems or version control integration
Filesystem MCP Server scores higher at 60/100 vs promptfoo at 58/100.
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Implements list_directory tool that recursively enumerates directory trees with configurable depth limits and file type filtering. Returns structured metadata (size, modification time, permissions) for each entry without loading file contents, enabling efficient directory analysis. Uses TypeScript's fs.promises API with concurrent operations to traverse large directory structures while respecting path validation boundaries.
Unique: Exposes directory metadata through MCP tools with configurable recursion depth and filtering, allowing LLM clients to make informed decisions about which files to read next without requiring multiple round-trips or loading entire directory contents upfront
vs alternatives: More efficient than having LLMs read entire files to understand structure, and more flexible than simple ls-style listings because it includes metadata and supports filtering
Provides move_file tool that relocates files or directories within the sandboxed filesystem with configurable behavior for handling destination conflicts (fail, overwrite, or rename). Validates both source and destination paths against the same security boundaries, ensuring moves cannot escape the allowed directory tree. Implements atomic move semantics using OS-level rename operations when possible.
Unique: Integrates move operations into the MCP tool model with path validation on both source and destination, preventing LLM agents from accidentally moving files outside sandboxed boundaries while maintaining atomic semantics through OS-level rename when possible
vs alternatives: Safer than exposing raw filesystem operations because it validates both paths, and more flexible than read-only filesystem access because it enables file organization workflows
Implements search_files tool that finds files matching patterns (regex, glob, or literal strings) across the allowed filesystem tree. Returns matching file paths with optional context snippets showing where matches occur. Uses efficient pattern matching libraries (e.g., minimatch for globs) to avoid loading entire files into memory, supporting large codebases with thousands of files.
Unique: Exposes pattern-based file search through MCP tools with support for multiple pattern syntaxes (regex, glob, literal), allowing LLM clients to locate files efficiently without requiring full directory enumeration or file content loading upfront
vs alternatives: More efficient than having LLMs read entire directories to find files, and more flexible than simple filename matching because it supports content-based and pattern-based search
Implements the MCP server-side tool registration pattern using TypeScript SDK, exposing filesystem operations as callable tools through JSON-RPC 2.0 protocol. Handles tool schema definition (input parameters, return types), request routing, and error serialization automatically. Supports multiple transport mechanisms (stdio, HTTP, WebSocket) through MCP's transport abstraction layer, allowing the same server to work with different client configurations without code changes.
Unique: Leverages MCP's native tool registration abstraction to decouple tool implementation from transport mechanism, enabling the same filesystem server to work with stdio, HTTP, or WebSocket clients without modification through MCP's transport-agnostic design
vs alternatives: More standardized than custom REST APIs because it uses MCP's protocol, and more flexible than direct function calls because it supports multiple transport mechanisms and automatic schema validation
Implements a declarative security model where filesystem access is controlled through configuration files specifying allowed_directories (allowlist) or denied_paths (blocklist). Configuration is loaded at server startup and applied to all subsequent requests without requiring code changes. Supports glob patterns and environment variable expansion in configuration paths, enabling flexible deployment across different environments (dev, staging, production).
Unique: Provides declarative, configuration-driven access control that is loaded at server startup and applied uniformly to all requests, enabling environment-specific security policies without code changes or recompilation
vs alternatives: More flexible than hardcoded access rules because it supports configuration files, and simpler than role-based access control because it uses straightforward allowlist/blocklist semantics
Implements comprehensive error handling for filesystem operations with detailed diagnostic messages that help LLM clients understand why operations failed (e.g., 'Path /etc/passwd is outside allowed directories' vs generic 'Access denied'). Validates all inputs (paths, patterns, parameters) before execution and returns structured error responses through JSON-RPC error protocol, enabling clients to implement retry logic or fallback strategies.
Unique: Provides structured error responses with detailed diagnostic messages that distinguish between different failure modes (path validation, permissions, filesystem errors), enabling LLM clients to implement intelligent error handling without exposing sensitive system information
vs alternatives: More informative than generic error messages because it explains the specific reason for failure, while remaining secure by avoiding stack traces and sensitive path information
+2 more capabilities