{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_ape","slug":"ape","name":"Ape","type":"product","url":"https://weavel.ai","page_url":"https://unfragile.ai/ape","categories":["prompt-engineering"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_ape__cap_0","uri":"capability://debugging.llm.request.tracing.and.inspection","name":"llm request tracing and inspection","description":"Captures and visualizes the complete execution path of LLM requests, including intermediate steps, token consumption, and latency breakdowns. Provides granular visibility into what the model is doing at each stage of processing.","intents":["I need to understand why my LLM is producing unexpected outputs","I want to see exactly how many tokens each request is consuming","I need to identify performance bottlenecks in my LLM pipeline","I want to debug complex multi-step LLM workflows"],"best_for":["ML engineers","AI product managers","LLM application developers"],"limitations":["Requires integration with Ape platform","Only works with LLM requests routed through Ape","Learning curve for interpreting trace data"],"requires":["Active LLM application","Integration setup with Ape SDK/API","Understanding of LLM execution concepts"],"input_types":["LLM API calls","prompt text","model parameters"],"output_types":["trace visualization","execution timeline","token metrics","latency breakdown"],"categories":["debugging","monitoring","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_1","uri":"capability://testing.automated.prompt.evaluation.framework","name":"automated prompt evaluation framework","description":"Establishes objective performance benchmarks for prompts by running automated tests against defined evaluation criteria. Eliminates subjective assessment of prompt quality through systematic, measurable evaluation.","intents":["I want to objectively measure if my new prompt is better than the old one","I need to establish baseline metrics for prompt performance","I want to prevent prompt regressions when making changes","I need to compare multiple prompt variations systematically"],"best_for":["prompt engineers","AI product teams","teams running high-volume LLM applications"],"limitations":["Requires defining evaluation criteria upfront","Evaluation quality depends on test case design","May not capture all nuanced quality dimensions"],"requires":["Test dataset or evaluation cases","Defined success metrics","LLM requests integrated with Ape"],"input_types":["prompts","test cases","evaluation criteria","expected outputs"],"output_types":["evaluation scores","performance reports","comparison metrics","pass/fail results"],"categories":["testing","productivity","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_10","uri":"capability://collaboration.team.collaboration.and.prompt.sharing","name":"team collaboration and prompt sharing","description":"Enables teams to share prompts, evaluation results, and optimization insights across members. Facilitates collaborative prompt engineering through centralized access to prompt artifacts and performance data.","intents":["I want to share my optimized prompt with my team","I need to see what prompts other team members are working on","I want to collaborate on prompt improvements with colleagues","I need to access the history of prompt changes made by my team"],"best_for":["collaborative teams","distributed teams","organizations with multiple prompt engineers"],"limitations":["Requires team setup and permissions management","Collaboration effectiveness depends on team practices"],"requires":["Team account setup","Multiple users","Shared workspace"],"input_types":["prompts","evaluation results","comments","annotations"],"output_types":["shared prompt library","collaboration feeds","access logs","shared reports"],"categories":["collaboration","productivity","team-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_11","uri":"capability://integration.integration.with.llm.apis.and.frameworks","name":"integration with llm apis and frameworks","description":"Provides SDKs and API integrations to connect Ape with popular LLM providers and development frameworks. Enables seamless tracing and evaluation without major code restructuring.","intents":["I want to add Ape tracing to my existing LLM application","I need to integrate Ape with my preferred LLM provider","I want to use Ape with my development framework without rewriting code","I need to set up Ape monitoring with minimal engineering effort"],"best_for":["developers","engineering teams","teams with existing LLM applications"],"limitations":["Limited integration ecosystem compared to broader platforms","May require some code changes","Not all frameworks may be supported"],"requires":["Supported LLM provider or framework","API credentials","Development environment"],"input_types":["API keys","configuration parameters","code snippets"],"output_types":["integrated SDK","configuration files","example code"],"categories":["integration","developer-tools","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_2","uri":"capability://productivity.token.usage.analytics.and.optimization","name":"token usage analytics and optimization","description":"Tracks and analyzes token consumption across LLM requests to identify optimization opportunities. Provides detailed breakdowns of token usage by request, model, and prompt to reduce costs and improve efficiency.","intents":["I need to understand where my LLM token budget is being spent","I want to identify which prompts are most expensive to run","I need to optimize my prompts to reduce token consumption","I want to forecast and control LLM API costs"],"best_for":["cost-conscious teams","high-volume LLM application operators","AI product managers"],"limitations":["Requires sufficient request volume to identify patterns","Token optimization may impact output quality"],"requires":["Multiple LLM requests","Integration with Ape platform","Access to token count data"],"input_types":["LLM requests","prompts","model specifications"],"output_types":["token usage reports","cost breakdowns","optimization recommendations","trend analysis"],"categories":["productivity","cost-management","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_3","uri":"capability://monitoring.latency.monitoring.and.performance.profiling","name":"latency monitoring and performance profiling","description":"Measures and profiles the latency of LLM requests across different stages of execution. Identifies performance bottlenecks and provides insights into response time optimization opportunities.","intents":["I need to understand why my LLM responses are slow","I want to identify which part of my pipeline is causing delays","I need to ensure my LLM application meets SLA requirements","I want to optimize response times for better user experience"],"best_for":["performance-focused teams","production LLM application operators","infrastructure engineers"],"limitations":["Latency optimization may require architectural changes","Network latency is outside Ape's control"],"requires":["Active LLM requests","Ape integration","Baseline performance data"],"input_types":["LLM API calls","request metadata"],"output_types":["latency metrics","performance profiles","bottleneck identification","trend reports"],"categories":["monitoring","productivity","performance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_4","uri":"capability://productivity.prompt.version.control.and.comparison","name":"prompt version control and comparison","description":"Maintains version history of prompts and enables side-by-side comparison of different prompt variations. Tracks changes and allows teams to understand the impact of prompt modifications over time.","intents":["I want to track how my prompts have evolved over time","I need to compare the performance of different prompt versions","I want to revert to a previous prompt if a new version performs worse","I need to understand what changed between two prompt versions"],"best_for":["prompt engineering teams","collaborative AI development teams"],"limitations":["Requires consistent prompt management practices","Comparison is only meaningful with evaluation data"],"requires":["Multiple prompt iterations","Ape platform integration"],"input_types":["prompt text","metadata","evaluation results"],"output_types":["version history","diff views","comparison reports","performance deltas"],"categories":["productivity","collaboration","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_5","uri":"capability://testing.multi.prompt.a.b.testing.and.experimentation","name":"multi-prompt a/b testing and experimentation","description":"Enables systematic comparison of multiple prompt variations against the same test dataset. Provides statistical insights into which prompt performs best under different conditions.","intents":["I want to test multiple prompt variations simultaneously","I need to determine which prompt variation is statistically better","I want to run controlled experiments on my prompts","I need to understand prompt performance across different input types"],"best_for":["data-driven teams","prompt optimization specialists","AI product teams"],"limitations":["Requires sufficient test cases for statistical significance","Results may not generalize beyond test dataset"],"requires":["Multiple prompt candidates","Test dataset","Evaluation framework setup"],"input_types":["prompts","test cases","evaluation criteria"],"output_types":["comparison results","statistical analysis","winner determination","performance insights"],"categories":["testing","productivity","experimentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_6","uri":"capability://debugging.llm.behavior.visualization.and.analysis","name":"llm behavior visualization and analysis","description":"Creates visual representations of LLM execution patterns, decision points, and output generation processes. Helps teams understand and debug complex LLM behaviors through interactive visualizations.","intents":["I want to visualize how my LLM is processing requests","I need to understand the decision-making process of my model","I want to identify patterns in LLM outputs","I need to communicate LLM behavior to non-technical stakeholders"],"best_for":["visual learners","teams explaining LLM behavior","debugging-focused engineers"],"limitations":["Visualization complexity increases with request complexity","May not capture all nuanced behaviors"],"requires":["Traced LLM requests","Ape integration"],"input_types":["execution traces","request metadata"],"output_types":["visual diagrams","interactive charts","timeline visualizations","behavior reports"],"categories":["debugging","visualization","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_7","uri":"capability://productivity.evaluation.metric.definition.and.customization","name":"evaluation metric definition and customization","description":"Allows teams to define custom evaluation metrics and criteria tailored to their specific use cases. Supports creation of domain-specific quality measures beyond generic benchmarks.","intents":["I need to define what 'good' means for my specific LLM application","I want to create custom evaluation metrics for my domain","I need to measure LLM output quality in ways that matter to my business","I want to establish objective criteria for prompt acceptance"],"best_for":["domain experts","product teams","teams with specific quality requirements"],"limitations":["Requires domain expertise to define meaningful metrics","Custom metrics may be harder to compare across teams"],"requires":["Understanding of success criteria","Domain knowledge","Test cases"],"input_types":["evaluation criteria definitions","test cases","expected outputs"],"output_types":["custom metrics","evaluation rules","scoring frameworks"],"categories":["productivity","quality-assurance","customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_8","uri":"capability://testing.batch.prompt.evaluation.and.reporting","name":"batch prompt evaluation and reporting","description":"Processes large batches of LLM requests through evaluation framework and generates comprehensive performance reports. Enables bulk assessment of prompt quality across many test cases.","intents":["I want to evaluate my prompt against hundreds of test cases at once","I need to generate a comprehensive performance report for my prompt","I want to identify edge cases where my prompt fails","I need to validate prompt quality before production deployment"],"best_for":["teams with large test datasets","production-focused teams","quality assurance roles"],"limitations":["Batch processing may take time for very large datasets","Requires well-designed test cases"],"requires":["Large test dataset","Defined evaluation criteria","Ape integration"],"input_types":["prompts","test cases","evaluation criteria"],"output_types":["batch evaluation results","performance reports","failure analysis","quality metrics"],"categories":["testing","productivity","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ape__cap_9","uri":"capability://quality.assurance.prompt.performance.regression.detection","name":"prompt performance regression detection","description":"Automatically detects when prompt changes result in performance degradation. Alerts teams to regressions and prevents deployment of lower-quality prompt versions.","intents":["I want to know immediately if my new prompt is worse than the previous version","I need to prevent bad prompts from reaching production","I want to establish quality gates for prompt changes","I need to catch performance regressions before they impact users"],"best_for":["production-focused teams","quality-conscious organizations","continuous deployment teams"],"limitations":["Requires baseline performance data","May produce false positives if thresholds not tuned"],"requires":["Historical performance data","Defined regression thresholds","Automated testing setup"],"input_types":["new prompts","historical performance data","regression thresholds"],"output_types":["regression alerts","performance comparisons","pass/fail decisions"],"categories":["quality-assurance","monitoring","productivity"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"low","permissions":["Active LLM application","Integration setup with Ape SDK/API","Understanding of LLM execution concepts","Test dataset or evaluation cases","Defined success metrics","LLM requests integrated with Ape","Team account setup","Multiple users","Shared workspace","Supported LLM provider or framework"],"failure_modes":["Requires integration with Ape platform","Only works with LLM requests routed through Ape","Learning curve for interpreting trace data","Requires defining evaluation criteria upfront","Evaluation quality depends on test case design","May not capture all nuanced quality dimensions","Requires team setup and permissions management","Collaboration effectiveness depends on team practices","Limited integration ecosystem compared to broader platforms","May require some code changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.550Z","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=ape","compare_url":"https://unfragile.ai/compare?artifact=ape"}},"signature":"4hkVgbV51yyHISbgkuPWt+hb6knHb5DB8Nufd+m255GToX1aS2kZ95IvMbQvKoELXGtIzEcaGzLKvx+yNKR3Dw==","signedAt":"2026-06-20T08:05:56.348Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ape","artifact":"https://unfragile.ai/ape","verify":"https://unfragile.ai/api/v1/verify?slug=ape","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"}}