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| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 11 decomposed | 13 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
LangChain provides a Chain abstraction that sequences LLM calls, prompt templates, and tool invocations into directed acyclic graphs (DAGs). Chains support sequential execution (SequentialChain), conditional branching (RouterChain), and parallel execution patterns. The framework uses a Runnable interface that standardizes input/output contracts across all chain components, enabling composition via pipe operators and method chaining. This allows developers to build complex multi-step workflows without managing state manually.
Unique: Uses a unified Runnable interface across all components (LLMs, tools, retrievers, parsers) enabling composability via pipe operators, unlike frameworks that require separate orchestration layers for different component types. Supports both sync and async execution with identical code paths.
vs alternatives: More flexible than simple prompt chaining (like OpenAI's function calling alone) because it abstracts orchestration logic, making chains reusable and testable; simpler than full workflow engines (Airflow, Prefect) because it's optimized for LLM-specific patterns rather than general data pipelines.
LangChain's PromptTemplate class provides structured prompt engineering with variable placeholders, automatic validation, and support for few-shot learning patterns. Templates use Jinja2-style syntax for variable substitution and support dynamic example selection via ExampleSelector. The framework includes specialized templates (ChatPromptTemplate for multi-turn conversations, FewShotPromptTemplate for in-context learning) that handle formatting differences across LLM types. This enables prompt reusability, version control, and systematic experimentation without string concatenation.
Unique: Provides first-class abstractions for few-shot learning (FewShotPromptTemplate) with pluggable ExampleSelector strategies, enabling dynamic example selection based on input similarity without requiring developers to implement selection logic. Separates system prompts, conversation history, and user input in ChatPromptTemplate, making multi-turn conversations composable.
LangChain scores higher at 41/100 vs garak at 22/100. garak leads on ecosystem, while LangChain is stronger on quality. However, garak offers a free tier which may be better for getting started.
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vs alternatives: More structured than manual string formatting because it validates variable names and supports semantic example selection; more specialized than generic templating engines (Jinja2) because it understands LLM-specific patterns like chat message roles and few-shot formatting.
LangChain abstracts function calling across LLM providers by converting Python functions or Pydantic models into provider-specific schemas (OpenAI function_call, Anthropic tool_use, etc.). The framework automatically generates schemas, handles argument parsing, and routes calls to the correct provider. Developers define functions once and LangChain handles provider-specific formatting. This enables tool use without learning each provider's function calling API.
Unique: Automatically converts Python functions and Pydantic models into provider-specific function calling schemas (OpenAI, Anthropic, Cohere, etc.) and handles parsing and routing transparently. Developers define tools once and LangChain handles provider-specific formatting and execution.
vs alternatives: More portable than using provider SDKs directly because function definitions are provider-agnostic; more automated than manual schema management because schemas are generated from function signatures.
LangChain supports streaming LLM output at token granularity, enabling real-time user feedback as tokens are generated. The framework provides streaming iterators and async generators that yield tokens as they arrive from the LLM. Streaming is integrated into chains and agents, so developers can stream output from complex workflows without special handling. This enables responsive user experiences where output appears in real-time rather than waiting for full completion.
Unique: Integrates streaming at the framework level so chains and agents can stream output transparently without special handling. Provides both sync and async streaming iterators and handles provider-specific streaming formats uniformly.
vs alternatives: More integrated than provider-specific streaming APIs because streaming works across chains and agents; more responsive than buffering full output because tokens appear in real-time.
LangChain provides async/await support throughout the framework, enabling concurrent execution of LLM calls, chains, and agents. All major components (LLMs, chains, retrievers, agents) have async variants (e.g., arun() alongside run()). The framework uses asyncio for Python and native async/await for Node.js. This enables high-concurrency applications that can handle multiple requests simultaneously without blocking. Async execution is transparent; developers write the same code as sync but use async/await syntax.
Unique: Provides async/await support throughout the framework with parallel async implementations of all major components. Enables transparent concurrent execution without requiring developers to manage thread pools or explicit parallelization.
vs alternatives: More integrated than manual async management because async is built into the framework; more scalable than sync-only implementations because it enables handling multiple concurrent requests.
LangChain abstracts LLM APIs behind a common BaseLanguageModel interface, supporting OpenAI, Anthropic, Cohere, Hugging Face, Ollama, and 20+ other providers. The abstraction handles provider-specific details: token counting, streaming, function calling schemas, and cost tracking. Developers write LLM-agnostic code and swap providers via configuration. The framework includes built-in retry logic, rate limiting, and fallback chains for reliability. This enables portability and cost optimization without rewriting application logic.
Unique: Implements a unified BaseLanguageModel interface that abstracts away provider differences in token counting, streaming protocols, and function calling schemas. Includes built-in retry policies, rate limiting, and cost tracking at the framework level rather than requiring developers to implement these separately for each provider.
vs alternatives: More portable than using provider SDKs directly because swapping providers requires only configuration changes; more comprehensive than simple wrapper libraries because it handles streaming, retries, and cost tracking uniformly across 20+ providers.
LangChain provides a Retriever abstraction that enables RAG by connecting LLMs to external knowledge sources. The framework supports multiple retrieval strategies: vector similarity search (via VectorStore), BM25 keyword search, hybrid search, and custom retrievers. Documents are chunked, embedded, and stored in vector databases (Pinecone, Weaviate, Chroma, FAISS, etc.). The RetrievalQA chain automatically retrieves relevant documents and passes them as context to the LLM. This enables LLMs to answer questions grounded in custom data without fine-tuning.
Unique: Provides a unified Retriever interface that abstracts different retrieval strategies (vector, keyword, hybrid, custom) and integrates seamlessly with LLM chains via RetrievalQA. Includes built-in document loaders for 50+ formats (PDF, HTML, Markdown, code files) and automatic chunking strategies, reducing boilerplate for document ingestion.
vs alternatives: More integrated than building RAG from scratch because document loading, chunking, embedding, and retrieval are unified in one framework; more flexible than specialized RAG platforms (Pinecone, Weaviate) because it supports multiple vector stores and custom retrieval logic.
LangChain's Agent abstraction enables autonomous task execution by combining LLMs with tools (functions, APIs, retrievers). The agent uses an action-observation loop: the LLM decides which tool to call based on the task, executes the tool, observes the result, and repeats until the task is complete. Agents support multiple reasoning strategies: ReAct (reasoning + acting), chain-of-thought, and tool-use patterns. The framework handles tool schema generation, argument parsing, and error recovery. This enables building autonomous systems that can decompose complex tasks without explicit step-by-step instructions.
Unique: Implements a generalized Agent interface that supports multiple reasoning strategies (ReAct, chain-of-thought, tool-use) and automatically handles tool schema generation, argument parsing, and error recovery. The action-observation loop is abstracted, allowing developers to focus on defining tools rather than implementing agent logic.
vs alternatives: More flexible than simple function calling (OpenAI's tool_choice) because it implements multi-step reasoning and tool sequencing; more accessible than building agents from scratch because it handles schema generation, parsing, and error recovery automatically.
+5 more capabilities