{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-varies","slug":"varies","name":"varies","type":"benchmark","url":"https://www.swebench.com/","page_url":"https://unfragile.ai/varies","categories":["testing-quality"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-varies__cap_0","uri":"capability://automation.workflow.software.engineering.task.benchmark.evaluation","name":"software-engineering-task-benchmark-evaluation","description":"Evaluates AI agents' ability to solve real-world software engineering tasks by executing them against a curated benchmark of GitHub issues and pull requests. The system runs agent-generated solutions in isolated environments, validates outputs against ground-truth implementations, and measures success rates across multiple dimensions (task completion, code quality, test passage). Uses a standardized evaluation framework that normalizes metrics across different model architectures and agent implementations.","intents":["Measure how well an AI coding agent can autonomously resolve real GitHub issues end-to-end","Compare performance of different LLM models and agent architectures on identical software engineering tasks","Identify which categories of software tasks (bug fixes, feature implementation, refactoring) an agent struggles with most","Establish baseline metrics for evaluating new coding agent designs before production deployment"],"best_for":["AI research teams developing and benchmarking autonomous coding agents","LLM providers (OpenAI, Anthropic, Meta) validating model capabilities on code tasks","Enterprise teams evaluating whether to adopt AI-assisted development tools","Academic researchers studying AI-driven software engineering"],"limitations":["Benchmark is static and may not reflect emerging coding patterns or modern frameworks introduced after dataset creation","Evaluation environment isolation may not catch issues that only manifest in production-like distributed systems","Success metrics are binary or threshold-based and don't capture partial progress or near-correct solutions","Requires agents to have full repository context and tool access; doesn't measure performance under constrained token budgets","GitHub-specific task format may not generalize to other software engineering workflows (embedded systems, hardware, scientific computing)"],"requires":["Agent implementation with code execution and repository manipulation capabilities","Access to isolated sandboxed environment (Docker, VM, or cloud sandbox) for safe code execution","Git and version control integration to apply and validate patches","Test runner infrastructure compatible with Python, JavaScript, and other common languages","API access to benchmark dataset and evaluation harness"],"input_types":["GitHub issue descriptions (text)","Repository code (multiple languages: Python, JavaScript, TypeScript, Java, C++, etc.)","Test suites (pytest, Jest, unittest, etc.)","Agent execution traces and generated patches (diff format)"],"output_types":["Structured evaluation metrics (success rate, test passage rate, code quality scores)","Per-task result reports (pass/fail, error logs, execution time)","Comparative performance rankings across models and agent architectures","Detailed failure analysis and task categorization breakdowns"],"categories":["automation-workflow","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-varies__cap_1","uri":"capability://automation.workflow.multi.model.agent.performance.comparison","name":"multi-model-agent-performance-comparison","description":"Provides standardized evaluation infrastructure that allows direct performance comparison of different LLM models (GPT-4, Claude, Llama, etc.) and agent architectures (ReAct, Chain-of-Thought, tool-use patterns) on identical software engineering tasks. Normalizes evaluation across model-specific API differences, context window constraints, and function-calling conventions to produce comparable metrics. Tracks performance deltas as models are updated or new agents are introduced.","intents":["Determine which LLM model performs best for autonomous code generation and bug fixing tasks","Quantify performance improvements when upgrading from one model version to another","Identify which agent reasoning patterns (planning, tool-use, iterative refinement) yield highest success rates","Build internal dashboards showing model performance trends over time as new versions release"],"best_for":["LLM product teams making model selection decisions for coding features","AI research groups conducting comparative studies of agent architectures","Engineering leaders evaluating cost-benefit of upgrading to newer, more expensive models","Open-source project maintainers benchmarking community models against commercial alternatives"],"limitations":["Benchmark results are snapshot-in-time and don't account for model fine-tuning or prompt optimization specific to SWE-Bench","Evaluation doesn't measure latency, token efficiency, or cost-per-successful-task, only success rate","Model performance may vary significantly based on prompt engineering and agent implementation details not controlled by benchmark","Doesn't evaluate models' ability to handle novel or out-of-distribution tasks beyond the fixed benchmark set"],"requires":["API access to multiple LLM providers (OpenAI, Anthropic, Together, Replicate, etc.) or local model serving","Standardized agent implementation that can be instantiated with different model backends","Sufficient API quota and budget to run full benchmark suite across all models","Monitoring infrastructure to track and log results for each model-task combination"],"input_types":["Model identifiers and API credentials","Agent configuration templates","Benchmark task dataset (GitHub issues and repositories)"],"output_types":["Comparative performance matrices (model × task success rate)","Ranking tables sorted by overall success rate or task category","Performance delta reports (improvement from model A to model B)","Cost-effectiveness analysis (success rate per dollar spent on API calls)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-varies__cap_2","uri":"capability://automation.workflow.repository.context.aware.code.execution","name":"repository-context-aware-code-execution","description":"Executes agent-generated code patches within the full context of the target repository, including all dependencies, test suites, and version control history. The system applies patches to a clean repository state, runs the full test suite to validate correctness, and captures execution logs and error traces. Uses sandboxed execution environments (containerized or VM-based) to safely run untrusted code without affecting the host system or benchmark infrastructure.","intents":["Validate that an agent's generated code patch actually fixes the target issue without breaking existing tests","Capture detailed error messages and stack traces when agent-generated code fails to execute or passes tests incorrectly","Measure how many existing tests pass after applying an agent's patch (regression detection)","Reproduce and debug agent failures by inspecting the exact execution environment and code state"],"best_for":["Benchmark operators running evaluation infrastructure at scale","Researchers analyzing failure modes of coding agents in detail","Teams building CI/CD pipelines that integrate AI code generation with automated testing"],"limitations":["Sandboxed execution adds 30-120 seconds overhead per task due to container startup and dependency installation","Environment setup may fail for projects with complex, undocumented dependencies or system-level requirements","Test suite execution time varies wildly (seconds to minutes) depending on project size and test count, making latency unpredictable","Some projects require external services (databases, APIs) that can't be easily mocked in sandbox environment","Flaky tests in the original repository may cause false negatives (agent's code is correct but test fails intermittently)"],"requires":["Container runtime (Docker) or VM infrastructure for isolated execution","Git and version control tools to apply patches and reset repository state","Language-specific runtime environments (Python 3.x, Node.js, Java, etc.) pre-installed in sandbox images","Test runner frameworks compatible with target projects (pytest, Jest, unittest, etc.)","Sufficient disk space and compute resources to run multiple concurrent evaluations"],"input_types":["Repository code and metadata (cloned from GitHub)","Generated code patches in unified diff format","Test suite definitions and test runner configurations","Environment variables and configuration files needed for execution"],"output_types":["Test execution results (pass/fail per test case)","Execution logs and stderr/stdout capture","Error traces and stack dumps when code fails","Code coverage reports (if instrumented)","Performance metrics (execution time, memory usage)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-varies__cap_3","uri":"capability://data.processing.analysis.task.category.performance.breakdown","name":"task-category-performance-breakdown","description":"Segments benchmark results by software engineering task type (bug fixes, feature implementation, documentation, refactoring, etc.) and provides per-category success rates and performance analysis. Enables identification of which task categories agents excel at versus struggle with, revealing systematic weaknesses in agent reasoning or code generation capabilities. Uses task metadata and issue classification to automatically bucket results and generate category-specific reports.","intents":["Identify which types of software engineering tasks (e.g., bug fixes vs new features) an agent is most/least capable of handling","Understand whether an agent's failures are concentrated in specific domains (e.g., frontend vs backend, testing vs implementation)","Make targeted improvements to agent prompts or reasoning strategies based on category-specific failure patterns","Communicate to stakeholders which real-world engineering tasks are safe to automate with current agent capabilities"],"best_for":["Product teams deciding which coding tasks to expose to AI automation features","Researchers studying systematic biases in agent capabilities across task types","Engineering leaders assessing risk of deploying agents on specific codebases or task categories"],"limitations":["Task categorization is coarse-grained and may not capture nuanced task characteristics (e.g., a bug fix in a complex distributed system vs simple typo fix)","Category-level metrics may hide important variance within categories (some bug fixes are much harder than others)","Requires manual or heuristic-based task labeling which may introduce classification errors","Small sample sizes within some categories may produce unreliable statistics"],"requires":["Benchmark dataset with task type annotations or metadata","Classification logic to automatically assign tasks to categories","Aggregation and statistical analysis infrastructure"],"input_types":["Benchmark results (per-task success/failure)","Task metadata (issue type, labels, description text)","Agent execution traces"],"output_types":["Per-category success rate tables","Category-level performance rankings","Failure analysis grouped by task type","Heatmaps showing model performance across categories"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-varies__cap_4","uri":"capability://automation.workflow.agent.execution.trace.logging.and.replay","name":"agent-execution-trace-logging-and-replay","description":"Captures detailed execution traces of agent decision-making, tool calls, and reasoning steps during task execution. Logs all intermediate states, API calls, code generation attempts, and error recovery actions in a structured format. Enables post-hoc analysis and replay of agent behavior to understand failure modes, debug agent logic, and identify where agents made suboptimal decisions. Supports both real-time streaming logs and batch analysis of completed runs.","intents":["Debug why an agent failed on a specific task by replaying its exact decision sequence and tool calls","Analyze agent reasoning patterns to identify systematic errors (e.g., agent always makes same mistake in certain scenarios)","Extract examples of successful agent behavior for prompt engineering or few-shot learning","Build dashboards showing agent behavior metrics (number of tool calls, reasoning depth, error recovery attempts)"],"best_for":["AI researchers studying agent behavior and decision-making patterns","Agent developers debugging failures and optimizing prompts","Teams building observability and monitoring for production AI systems"],"limitations":["Trace logging adds overhead (storage, processing) that scales with agent complexity and task count","Sensitive information in traces (API keys, credentials, private code) requires careful redaction before sharing","Trace format is agent-implementation-specific and may not be portable across different agent frameworks","Large traces (thousands of tool calls) are difficult to visualize and analyze manually"],"requires":["Instrumentation in agent code to emit structured log events","Log storage infrastructure (database, object storage) with sufficient capacity","Log parsing and analysis tools to extract insights from traces","Visualization tools to display execution flows and decision trees"],"input_types":["Agent execution events (tool calls, reasoning steps, errors)","Model API responses and token usage","Code generation outputs and validation results"],"output_types":["Structured execution traces (JSON or similar format)","Execution timeline visualizations","Decision tree diagrams showing agent reasoning flow","Aggregated metrics (average tool calls per task, error recovery rate, etc.)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Agent implementation with code execution and repository manipulation capabilities","Access to isolated sandboxed environment (Docker, VM, or cloud sandbox) for safe code execution","Git and version control integration to apply and validate patches","Test runner infrastructure compatible with Python, JavaScript, and other common languages","API access to benchmark dataset and evaluation harness","API access to multiple LLM providers (OpenAI, Anthropic, Together, Replicate, etc.) or local model serving","Standardized agent implementation that can be instantiated with different model backends","Sufficient API quota and budget to run full benchmark suite across all models","Monitoring infrastructure to track and log results for each model-task combination","Container runtime (Docker) or VM infrastructure for isolated execution"],"failure_modes":["Benchmark is static and may not reflect emerging coding patterns or modern frameworks introduced after dataset creation","Evaluation environment isolation may not catch issues that only manifest in production-like distributed systems","Success metrics are binary or threshold-based and don't capture partial progress or near-correct solutions","Requires agents to have full repository context and tool access; doesn't measure performance under constrained token budgets","GitHub-specific task format may not generalize to other software engineering workflows (embedded systems, hardware, scientific computing)","Benchmark results are snapshot-in-time and don't account for model fine-tuning or prompt optimization specific to SWE-Bench","Evaluation doesn't measure latency, token efficiency, or cost-per-successful-task, only success rate","Model performance may vary significantly based on prompt engineering and agent implementation details not controlled by benchmark","Doesn't evaluate models' ability to handle novel or out-of-distribution tasks beyond the fixed benchmark set","Sandboxed execution adds 30-120 seconds overhead per task due to container startup and dependency installation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=varies","compare_url":"https://unfragile.ai/compare?artifact=varies"}},"signature":"cfO1YapiL1ZPrdYCE8HqcOeg9jmcvmUQqWCZ1ud+k5Dt5NQLmixuuAj3Zhw3MzYVonKqTGFKnMuQsuuPbdymDQ==","signedAt":"2026-06-22T10:32:35.698Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/varies","artifact":"https://unfragile.ai/varies","verify":"https://unfragile.ai/api/v1/verify?slug=varies","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}