garak vs @tanstack/ai
Side-by-side comparison to help you choose.
| Feature | garak | @tanstack/ai |
|---|---|---|
| Type | Repository | API |
| UnfragileRank | 25/100 | 34/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Garak scans LLMs for vulnerabilities by routing prompts through a modular harness system that abstracts different model providers (OpenAI, Anthropic, Ollama, vLLM, etc.) behind a unified interface. Each harness handles authentication, rate limiting, and response parsing for its target model, allowing the same vulnerability test suite to run against any LLM without code changes. The architecture uses a plugin-based loader pattern to dynamically instantiate harnesses at runtime based on configuration.
Unique: Uses a harness abstraction layer that decouples vulnerability tests from model provider implementations, enabling the same test suite to run against OpenAI, Anthropic, open-source models, and custom endpoints without modification. Most competitors either target specific providers or require test rewrites per model.
vs alternatives: Garak's harness-based design allows security teams to test heterogeneous LLM deployments with a single tool, whereas alternatives like Promptfoo focus on prompt evaluation and Rebuff targets specific attack patterns.
Garak organizes vulnerability tests as 'probes' — modular test units that generate adversarial prompts, send them to a target LLM via a harness, and evaluate responses against detection criteria. Probes are organized into taxonomies (e.g., 'jailbreak', 'prompt-injection', 'hallucination') and can be composed into test suites. Each probe implements a generate() method that produces test prompts (often using templates or programmatic construction) and a detect() method that classifies model responses as vulnerable or safe based on heuristics, keyword matching, or semantic similarity.
Unique: Implements a two-stage probe architecture (generate + detect) that separates test prompt creation from response evaluation, allowing probes to be reused across different detection strategies and enabling custom detection logic without modifying prompt generation. This is more flexible than monolithic test frameworks that couple prompt and evaluation logic.
vs alternatives: Garak's probe taxonomy provides broader coverage of LLM vulnerabilities (jailbreaks, prompt injection, hallucination, bias) compared to narrower tools like Rebuff (jailbreak-focused) or Promptfoo (prompt optimization-focused).
Garak exposes both a command-line interface (CLI) and a Python API for executing vulnerability scans. The CLI uses argparse to parse configuration and invoke the orchestrator, making garak accessible to non-programmers. The Python API provides classes and functions for programmatic test execution, enabling integration into Python-based workflows, notebooks, and CI/CD pipelines. Both interfaces share the same underlying orchestrator, ensuring consistent behavior. The architecture uses a facade pattern to abstract CLI and API differences, allowing users to choose the interface that best fits their workflow.
Unique: Provides both CLI and Python API interfaces backed by the same orchestrator, allowing users to choose the interface that best fits their workflow (command-line for one-off scans, Python API for automation). The facade pattern ensures consistent behavior across interfaces.
vs alternatives: Garak's dual interface (CLI + API) is more flexible than CLI-only tools (like some security scanners) or API-only tools (like some Python libraries), enabling broader adoption across different user types and workflows.
Garak provides a configuration-driven orchestration layer that chains together harnesses, probes, and detectors into executable test suites. Users define test runs in YAML/JSON config files specifying which models to test, which probes to run, and how to aggregate results. The orchestrator handles sequential or parallel probe execution (depending on harness concurrency support), collects results, and generates structured reports (JSON, CSV, HTML) with vulnerability metrics, model comparisons, and risk summaries. The architecture uses a run manager pattern to track test state and enable resumable/incremental scanning.
Unique: Uses a declarative YAML/JSON configuration model to define test suites, allowing non-programmers to compose complex multi-model security tests without writing code. The run manager pattern enables resumable scans and incremental result collection, reducing cost and time for large-scale audits.
vs alternatives: Garak's configuration-driven orchestration is more flexible than CLI-only tools and provides better auditability than programmatic test frameworks, making it suitable for compliance-heavy environments.
Garak's probes generate adversarial prompts using multiple strategies: template-based (filling placeholders in predefined jailbreak/injection patterns), programmatic (constructing prompts via Python logic to vary parameters), and potentially LLM-based (using auxiliary models to generate novel attack prompts). Probes can combine strategies — e.g., a jailbreak probe might use templates for known attacks and programmatic generation for variations. The generation layer abstracts prompt construction, allowing probes to focus on detection logic and enabling reuse of generation strategies across multiple probes.
Unique: Separates prompt generation from detection, allowing probes to use multiple generation strategies (templates, programmatic, LLM-based) and enabling reuse of generation logic across different detection criteria. This modularity makes it easier to add new attack patterns without duplicating generation code.
vs alternatives: Garak's multi-strategy generation approach is more comprehensive than single-strategy tools; it supports both curated jailbreak templates and programmatic variation, whereas competitors often use only one approach.
Garak's detection layer evaluates LLM responses against multiple criteria to classify them as vulnerable or safe. Detection strategies include keyword/regex matching (e.g., detecting refusal phrases or harmful content keywords), semantic similarity (comparing responses to known vulnerable outputs using embeddings), classifier-based detection (using auxiliary ML models to score response safety), and custom heuristics. Probes compose these strategies — e.g., a jailbreak probe might use keyword matching for obvious bypasses and semantic similarity for subtle ones. The detection layer is decoupled from prompt generation, allowing the same response to be evaluated by multiple detectors.
Unique: Implements a composable detection architecture where multiple detection strategies (keyword, semantic, classifier) can be combined per probe, allowing fine-grained control over false positive/negative tradeoffs. Most competitors use single detection strategies, making them less flexible for diverse vulnerability types.
vs alternatives: Garak's multi-strategy detection is more robust than keyword-only tools (like simple regex scanners) and more flexible than single-model approaches (like classifier-only tools), enabling better accuracy across diverse attack types.
Garak organizes vulnerabilities into a hierarchical taxonomy (e.g., 'jailbreak', 'prompt-injection', 'hallucination', 'bias', 'privacy') with subtypes and specific probes for each category. The taxonomy is exposed as a discoverable API — users can list available probes, filter by vulnerability type, and understand the coverage of each category. The taxonomy structure enables organized reporting (grouping results by vulnerability class) and helps users understand which attack vectors are tested. The architecture uses a registry pattern to dynamically load probes and organize them by taxonomy.
Unique: Provides a discoverable, hierarchical taxonomy of LLM vulnerabilities with explicit probe mappings, allowing users to understand test coverage and plan audits systematically. Most competitors lack explicit taxonomy organization, making it harder to assess what vulnerabilities are tested.
vs alternatives: Garak's taxonomy-based organization makes it easier for non-security experts to understand vulnerability scope and plan comprehensive audits, whereas competitors often require deep knowledge of attack types.
Garak supports scanning multiple LLMs in a single test run, aggregating results across models to enable comparative analysis. The orchestrator manages harness instances for each model, routes probes to all harnesses, and collects results in a unified format. Aggregation includes per-model vulnerability counts, cross-model comparisons (e.g., 'Model A is vulnerable to X, Model B is not'), and overall risk rankings. The architecture uses a result collector pattern to normalize outputs from different harnesses and enable flexible aggregation strategies.
Unique: Normalizes results across heterogeneous LLM providers (OpenAI, Anthropic, open-source, custom) into a unified format, enabling direct comparative analysis without manual result reconciliation. The result collector pattern abstracts provider-specific output formats, making it easy to add new models.
vs alternatives: Garak's multi-model aggregation is more comprehensive than single-model tools and more flexible than provider-specific benchmarks, enabling fair comparisons across diverse LLM ecosystems.
+3 more capabilities
Provides a standardized API layer that abstracts over multiple LLM providers (OpenAI, Anthropic, Google, Azure, local models via Ollama) through a single `generateText()` and `streamText()` interface. Internally maps provider-specific request/response formats, handles authentication tokens, and normalizes output schemas across different model APIs, eliminating the need for developers to write provider-specific integration code.
Unique: Unified streaming and non-streaming interface across 6+ providers with automatic request/response normalization, eliminating provider-specific branching logic in application code
vs alternatives: Simpler than LangChain's provider abstraction because it focuses on core text generation without the overhead of agent frameworks, and more provider-agnostic than Vercel's AI SDK by supporting local models and Azure endpoints natively
Implements streaming text generation with built-in backpressure handling, allowing applications to consume LLM output token-by-token in real-time without buffering entire responses. Uses async iterators and event emitters to expose streaming tokens, with automatic handling of connection drops, rate limits, and provider-specific stream termination signals.
Unique: Exposes streaming via both async iterators and callback-based event handlers, with automatic backpressure propagation to prevent memory bloat when client consumption is slower than token generation
vs alternatives: More flexible than raw provider SDKs because it abstracts streaming patterns across providers; lighter than LangChain's streaming because it doesn't require callback chains or complex state machines
Provides React hooks (useChat, useCompletion, useObject) and Next.js server action helpers for seamless integration with frontend frameworks. Handles client-server communication, streaming responses to the UI, and state management for chat history and generation status without requiring manual fetch/WebSocket setup.
@tanstack/ai scores higher at 34/100 vs garak at 25/100.
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Unique: Provides framework-integrated hooks and server actions that handle streaming, state management, and error handling automatically, eliminating boilerplate for React/Next.js chat UIs
vs alternatives: More integrated than raw fetch calls because it handles streaming and state; simpler than Vercel's AI SDK because it doesn't require separate client/server packages
Provides utilities for building agentic loops where an LLM iteratively reasons, calls tools, receives results, and decides next steps. Handles loop control (max iterations, termination conditions), tool result injection, and state management across loop iterations without requiring manual orchestration code.
Unique: Provides built-in agentic loop patterns with automatic tool result injection and iteration management, reducing boilerplate compared to manual loop implementation
vs alternatives: Simpler than LangChain's agent framework because it doesn't require agent classes or complex state machines; more focused than full agent frameworks because it handles core looping without planning
Enables LLMs to request execution of external tools or functions by defining a schema registry where each tool has a name, description, and input/output schema. The SDK automatically converts tool definitions to provider-specific function-calling formats (OpenAI functions, Anthropic tools, Google function declarations), handles the LLM's tool requests, executes the corresponding functions, and feeds results back to the model for multi-turn reasoning.
Unique: Abstracts tool calling across 5+ providers with automatic schema translation, eliminating the need to rewrite tool definitions for OpenAI vs Anthropic vs Google function-calling APIs
vs alternatives: Simpler than LangChain's tool abstraction because it doesn't require Tool classes or complex inheritance; more provider-agnostic than Vercel's AI SDK by supporting Anthropic and Google natively
Allows developers to request LLM outputs in a specific JSON schema format, with automatic validation and parsing. The SDK sends the schema to the provider (if supported natively like OpenAI's JSON mode or Anthropic's structured output), or implements client-side validation and retry logic to ensure the LLM produces valid JSON matching the schema.
Unique: Provides unified structured output API across providers with automatic fallback from native JSON mode to client-side validation, ensuring consistent behavior even with providers lacking native support
vs alternatives: More reliable than raw provider JSON modes because it includes client-side validation and retry logic; simpler than Pydantic-based approaches because it works with plain JSON schemas
Provides a unified interface for generating embeddings from text using multiple providers (OpenAI, Cohere, Hugging Face, local models), with built-in integration points for vector databases (Pinecone, Weaviate, Supabase, etc.). Handles batching, caching, and normalization of embedding vectors across different models and dimensions.
Unique: Abstracts embedding generation across 5+ providers with built-in vector database connectors, allowing seamless switching between OpenAI, Cohere, and local models without changing application code
vs alternatives: More provider-agnostic than LangChain's embedding abstraction; includes direct vector database integrations that LangChain requires separate packages for
Manages conversation history with automatic context window optimization, including token counting, message pruning, and sliding window strategies to keep conversations within provider token limits. Handles role-based message formatting (user, assistant, system) and automatically serializes/deserializes message arrays for different providers.
Unique: Provides automatic context windowing with provider-aware token counting and message pruning strategies, eliminating manual context management in multi-turn conversations
vs alternatives: More automatic than raw provider APIs because it handles token counting and pruning; simpler than LangChain's memory abstractions because it focuses on core windowing without complex state machines
+4 more capabilities