{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agenta","slug":"agenta","name":"Agenta","type":"repo","url":"https://github.com/Agenta-AI/agenta","page_url":"https://unfragile.ai/agenta","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agenta__cap_0","uri":"capability://text.generation.language.multi.model.playground.with.version.controlled.prompt.variants","name":"multi-model playground with version-controlled prompt variants","description":"Interactive web-based environment for testing and iterating on prompts across multiple LLM providers (OpenAI, Anthropic, Ollama, LiteLLM) with automatic version tracking and configuration snapshots. Uses a FastAPI backend that manages prompt state, model selection, and parameter variations, while the Next.js frontend provides real-time prompt editing with side-by-side output comparison. Each variant is persisted as an immutable snapshot linked to an Application, enabling rollback and A/B testing workflows.","intents":["I want to test the same prompt across different models and compare outputs without manual context switching","I need to iterate on prompt parameters and track which version performed best","I want to save and reuse successful prompt configurations across team members"],"best_for":["prompt engineers optimizing LLM outputs for production","product teams running quick A/B tests on prompt variations","teams needing audit trails of prompt changes over time"],"limitations":["Playground latency depends on selected model provider response time (typically 1-5s per request)","No built-in prompt optimization suggestions — requires manual iteration","Version history stored in backend database; no local-first offline mode for playground","Limited to configured LLM providers; adding new providers requires backend configuration changes"],"requires":["Docker Compose or Kubernetes cluster for self-hosted deployment","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Modern web browser with WebSocket support for real-time updates","Python 3.9+ and Node.js 18+ for development"],"input_types":["text (prompt template with variable placeholders)","JSON (model parameters: temperature, max_tokens, top_p, etc.)","structured test inputs (from testsets)"],"output_types":["text (model completion output)","JSON (structured metadata: tokens used, latency, cost)","comparison matrices (variant outputs side-by-side)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_1","uri":"capability://data.processing.analysis.automated.evaluation.pipeline.with.20.built.in.evaluators","name":"automated evaluation pipeline with 20+ built-in evaluators","description":"Executes parameterized evaluation workflows against testsets using a modular evaluator registry that supports both built-in evaluators (regex matching, LLM-as-judge, similarity scoring) and custom Python evaluators. The evaluation system uses a task queue pattern (via Celery or direct execution) to parallelize evaluator runs across test cases, with results aggregated into a comparison matrix. Evaluators are configured via JSON schema, allowing non-technical users to customize thresholds and prompts without code changes.","intents":["I want to automatically score LLM outputs against expected results using multiple metrics","I need to run evaluations on large testsets (1000+ cases) without manual review","I want to compare evaluation results across prompt variants to identify the best performer"],"best_for":["ML engineers building evaluation frameworks for LLM applications","product teams measuring quality improvements across prompt iterations","teams needing reproducible, auditable evaluation results for compliance"],"limitations":["Built-in evaluators are limited to predefined metrics; complex domain-specific scoring requires custom Python evaluators","LLM-as-judge evaluators inherit model hallucination risks and cost scales linearly with testset size","Evaluation latency for large testsets (10k+ cases) can exceed 10 minutes depending on evaluator complexity","No built-in statistical significance testing; requires external tools for confidence intervals"],"requires":["Testset with expected outputs (ground truth labels)","For LLM-as-judge evaluators: API keys for evaluation model provider","Python 3.9+ for custom evaluator development","Backend service running (FastAPI + Celery for async execution)"],"input_types":["testset (structured test cases with inputs and expected outputs)","evaluator configuration (JSON schema with parameters)","LLM outputs (text or structured data from variant execution)"],"output_types":["evaluation scores (numeric: 0-1 or 0-100 range)","comparison matrix (variant × metric grid)","detailed results (per-case scores with explanations)","aggregated metrics (mean, std dev, pass rate)"],"categories":["data-processing-analysis","evaluation-testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_10","uri":"capability://tool.use.integration.litellm.proxy.service.for.multi.provider.llm.abstraction","name":"litellm proxy service for multi-provider llm abstraction","description":"Provides a unified API gateway that abstracts differences between LLM providers (OpenAI, Anthropic, Ollama, Cohere, etc.) using the LiteLLM library. The proxy normalizes request/response formats, handles authentication with provider-specific keys, and computes token counts and costs automatically. This enables applications to switch between providers or use multiple providers without code changes. The proxy is deployed as a separate service and handles rate limiting, retries, and fallback logic.","intents":["I want to test my application with different LLM providers without rewriting code","I need to automatically track token usage and cost across multiple providers","I want to implement fallback logic (e.g., use Anthropic if OpenAI is rate-limited)"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","applications requiring provider redundancy or failover","teams needing unified cost tracking across heterogeneous LLM infrastructure"],"limitations":["Proxy adds ~100-200ms latency per request due to request translation and token counting","Not all LLM features are supported; advanced features (vision, function calling) may not work across all providers","Token counting accuracy varies by provider; some providers have inconsistent token counting APIs","Fallback logic is basic (round-robin, sequential); no intelligent routing based on cost or latency"],"requires":["LiteLLM service running (included in Docker Compose)","API keys for at least one LLM provider","Network connectivity to LLM provider APIs"],"input_types":["LLM requests (messages, model name, parameters)","provider configuration (API keys, model mappings)"],"output_types":["normalized LLM responses (text, tokens, cost)","token counts (input and output)","cost estimates (based on provider pricing)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_11","uri":"capability://data.processing.analysis.evaluation.results.comparison.and.analytics.dashboard","name":"evaluation results comparison and analytics dashboard","description":"Provides a web-based dashboard that visualizes evaluation results across variants, testsets, and time periods. The dashboard displays comparison matrices (variant × metric), aggregate statistics (mean, std dev, pass rate), and trend charts showing performance over time. Users can filter results by metadata (model, testset, date range) and export data for external analysis. The dashboard supports custom metric visualization and drill-down into individual test cases to understand failure modes.","intents":["I want to see at a glance which prompt variant performs best across all metrics","I need to track how evaluation metrics change over time as I iterate on prompts","I want to drill down into failing test cases to understand why a variant underperformed"],"best_for":["product managers tracking LLM quality improvements over time","ML engineers analyzing evaluation results to identify optimization opportunities","teams presenting evaluation results to stakeholders"],"limitations":["Dashboard limited to metrics computed by evaluators; no custom metric calculation","Trend analysis limited to time-series visualization; no forecasting or anomaly detection","No built-in drill-down into model outputs; requires manual inspection of test cases","Performance degrades with large result sets (100k+ evaluations); requires external analytics database for scale"],"requires":["Completed evaluations with results stored in backend database","Web browser with JavaScript support","Optional: external analytics tool for advanced analysis"],"input_types":["evaluation results (scores, metrics)","variant metadata (model, prompt version, parameters)","testset metadata (for filtering)"],"output_types":["comparison matrices (variant × metric grid)","trend charts (metric over time)","aggregate statistics (mean, std dev, percentiles)","drill-down views (per-case results with explanations)","CSV/JSON export (for external analysis)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_12","uri":"capability://automation.workflow.variant.execution.against.testsets.with.batch.processing","name":"variant execution against testsets with batch processing","description":"Executes a prompt variant (application) against all test cases in a testset, collecting outputs and metrics. The system uses a task queue pattern to parallelize execution across test cases, with configurable concurrency limits to avoid rate limiting. Results are streamed to the frontend as they complete, providing real-time feedback. The system handles failures gracefully, retrying failed cases and collecting error logs for debugging. Execution results are persisted in the database and linked to the variant and testset for later analysis.","intents":["I want to run my prompt variant against 1000+ test cases without manual iteration","I need to see results in real-time as they complete, not wait for batch completion","I want to retry failed cases and understand why they failed"],"best_for":["teams evaluating prompt variants on large testsets (100+ cases)","applications requiring fast iteration cycles with quick feedback","teams needing detailed error logs for debugging failed cases"],"limitations":["Execution latency scales linearly with testset size; 10k cases may take 10+ minutes depending on model latency","Concurrency limited by LLM provider rate limits; no intelligent rate limiting based on provider quotas","No support for streaming outputs; all outputs collected before results are persisted","Execution results not deduplicated; identical inputs may be executed multiple times if testset contains duplicates"],"requires":["Variant (application) deployed and accessible","Testset with test cases","LLM provider API keys and sufficient quota","Backend task queue (Celery or direct execution)"],"input_types":["variant configuration (model, prompt, parameters)","testset (test cases with inputs)","execution parameters (concurrency, timeout, retry count)"],"output_types":["execution results (outputs per test case)","execution metadata (latency, tokens, cost per case)","error logs (for failed cases)","aggregated metrics (total cost, average latency)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_13","uri":"capability://automation.workflow.docker.compose.deployment.with.environment.configuration","name":"docker compose deployment with environment configuration","description":"Provides a production-ready Docker Compose configuration for self-hosted deployment of the entire Agenta stack (frontend, backend, database, services). The deployment includes environment variable templates for configuring LLM providers, database connections, and authentication. Supports both OSS (open-source) and EE (enterprise edition) deployments with feature flags. Includes migration scripts for upgrading between versions without data loss.","intents":["Deploy Agenta on-premises or in a private cloud without vendor lock-in","Configure LLM providers and database connections via environment variables","Upgrade Agenta to a new version while preserving data and configurations","Run Agenta in an air-gapped environment without internet access"],"best_for":["organizations with data residency or compliance requirements","teams preferring self-hosted solutions over SaaS","enterprises with existing Docker/Kubernetes infrastructure"],"limitations":["Docker Compose is suitable for development/small deployments; production deployments should use Kubernetes","No built-in high availability or auto-scaling; requires manual configuration","Database migrations must be run manually; no automatic schema updates"],"requires":["Docker and Docker Compose installed","PostgreSQL or MongoDB for data storage","API keys for LLM providers (OpenAI, Anthropic, etc.)","Sufficient disk space for database and logs (~10GB minimum)"],"input_types":["Docker Compose YAML configuration","environment variables (.env file)","database connection string"],"output_types":["running Agenta services (frontend, backend, database)","logs and monitoring data","persistent data (applications, evaluations, results)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_14","uri":"capability://tool.use.integration.litellm.proxy.service.for.multi.provider.llm.access","name":"litellm proxy service for multi-provider llm access","description":"Provides a unified LLM API proxy (via LiteLLM) that abstracts differences between LLM providers (OpenAI, Anthropic, Cohere, etc.) into a single interface. The proxy handles authentication, rate limiting, retry logic, and cost tracking across providers. Applications can switch between providers by changing a configuration parameter without code changes. Supports streaming responses and function calling across different provider APIs.","intents":["Use multiple LLM providers interchangeably without provider-specific code","Switch between providers for cost optimization or availability","Handle provider-specific features (streaming, function calling) uniformly","Track costs and usage across multiple providers in a single dashboard"],"best_for":["teams using multiple LLM providers and wanting a unified interface","organizations optimizing for cost by comparing provider pricing","teams requiring provider redundancy for high availability"],"limitations":["LiteLLM proxy adds ~50-100ms latency per request due to additional network hop","Not all provider features are supported; some advanced features (vision, tools) may not be available","Provider-specific error handling is limited; errors are normalized to a common format"],"requires":["API keys for at least one LLM provider","LiteLLM service running (included in Docker Compose)","Network connectivity to LLM provider APIs"],"input_types":["prompt text","model name (e.g., 'gpt-4', 'claude-3-opus')","provider configuration (API key, endpoint)"],"output_types":["LLM completion (text or streaming)","usage metadata (input/output tokens, cost)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_2","uri":"capability://data.processing.analysis.human.evaluation.workflow.with.annotation.interface","name":"human evaluation workflow with annotation interface","description":"Provides a web-based annotation interface for human raters to score LLM outputs against testsets, with support for multiple annotation types (binary choice, multi-class, Likert scale, free-form feedback). The system tracks annotator identity, timestamps, and inter-rater agreement metrics (Cohen's kappa, Fleiss' kappa) to measure evaluation consistency. Annotations are stored in the backend database and can be compared against automated evaluation results to identify cases where human judgment diverges from metrics.","intents":["I want human raters to evaluate LLM outputs on subjective criteria like tone or helpfulness","I need to measure agreement between raters to validate evaluation criteria","I want to identify edge cases where automated metrics fail and human judgment is needed"],"best_for":["product teams validating LLM quality with human feedback","research teams collecting labeled datasets for model training","teams needing compliance-auditable evaluation trails with human sign-off"],"limitations":["Annotation latency depends on rater availability; no SLA for completion time","Inter-rater agreement metrics require multiple annotators per case, increasing cost","No built-in annotator recruitment or payment integration; requires external tools","Annotation interface customization limited to predefined question types; complex workflows require custom development"],"requires":["Testset with LLM outputs to be evaluated","Human annotators with access to Agenta web interface","Authentication system (OIDC, SAML, or local accounts) for annotator identity"],"input_types":["testset (test cases with LLM outputs)","annotation schema (question types, response options)","variant outputs (from prompt execution)"],"output_types":["annotation scores (per-rater, per-case)","inter-rater agreement metrics (Cohen's kappa, Fleiss' kappa)","annotator feedback (free-form text or structured responses)","comparison reports (human vs automated evaluation)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_3","uri":"capability://data.processing.analysis.testset.management.with.structured.test.case.versioning","name":"testset management with structured test case versioning","description":"Manages collections of test cases (inputs, expected outputs, metadata) with version control and import/export capabilities. Testsets are stored as structured records in the backend database, supporting CSV/JSON import and export. The system tracks testset versions, allowing users to compare evaluation results across different testsets and identify performance regressions when testset coverage changes. Test cases can include dynamic variables that are substituted at evaluation time.","intents":["I want to organize test cases by domain or use case and reuse them across multiple evaluations","I need to version my testsets to track how evaluation coverage evolves over time","I want to import test cases from external sources (CSV, JSON) without manual data entry"],"best_for":["teams managing large test suites (100+ cases) for LLM applications","data teams preparing evaluation datasets for model validation","teams needing audit trails of testset changes for compliance"],"limitations":["No built-in test case generation; testsets must be created manually or imported from external sources","Testset size limited by database storage; no sharding for multi-billion case scenarios","No built-in test case deduplication; duplicate cases must be identified manually","Variable substitution limited to simple string replacement; no complex templating"],"requires":["CSV or JSON file with test cases (columns: input, expected_output, metadata)","Backend database with sufficient storage for testset size","Web browser for UI-based testset management"],"input_types":["CSV (columns: input, expected_output, optional metadata)","JSON (array of objects with test case fields)","manual entry (via web form)"],"output_types":["testset records (stored in database)","CSV/JSON export (for external analysis)","testset versions (immutable snapshots)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_4","uri":"capability://data.processing.analysis.a.b.testing.framework.with.statistical.comparison","name":"a/b testing framework with statistical comparison","description":"Enables side-by-side comparison of prompt variants or model configurations using evaluation results from the same testset. The system computes aggregate metrics (mean, median, std dev) for each variant and displays results in a comparison matrix. While the core comparison is deterministic, the framework supports filtering and slicing results by testset metadata to identify performance differences across subgroups. Results are persisted and can be exported for external statistical analysis.","intents":["I want to compare two prompt variants on the same testset to determine which performs better","I need to identify which testset subgroups show the largest performance differences between variants","I want to export comparison results for statistical significance testing in external tools"],"best_for":["product teams making go/no-go decisions on prompt changes","ML engineers validating model improvements before production deployment","teams needing documented comparison results for stakeholder approval"],"limitations":["No built-in statistical significance testing (p-values, confidence intervals); requires external tools","Comparison limited to variants evaluated on the same testset; cross-testset comparison requires manual alignment","No support for sequential testing or early stopping; all variants must complete full evaluation","Subgroup analysis limited to metadata filtering; no automated subgroup discovery"],"requires":["Two or more variants evaluated on the same testset","Evaluation results with comparable metrics across variants","Optional: external statistical analysis tool for significance testing"],"input_types":["evaluation results (from automated or human evaluation)","variant metadata (model, prompt version, parameters)","testset metadata (for subgroup filtering)"],"output_types":["comparison matrix (variant × metric grid)","aggregate statistics (mean, std dev, min, max per variant)","subgroup comparisons (filtered by metadata)","CSV/JSON export (for external analysis)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_5","uri":"capability://automation.workflow.opentelemetry.native.tracing.and.observability","name":"opentelemetry-native tracing and observability","description":"Instruments LLM application execution with OpenTelemetry traces that capture request/response spans, token counts, latency, and cost. The system uses Python SDK decorators (@app, @step) to automatically wrap function calls and emit traces to a backend collector. Traces are stored in a time-series database and can be queried via the web UI to identify performance bottlenecks, cost drivers, and error patterns. Integration with LiteLLM proxy enables automatic token counting and cost calculation for LLM calls.","intents":["I want to track latency and cost for each LLM call in my application without manual instrumentation","I need to identify which steps in my workflow are slowest and most expensive","I want to correlate trace data with evaluation results to understand quality-cost tradeoffs"],"best_for":["ML engineers optimizing LLM application performance and cost","DevOps teams monitoring production LLM deployments","teams needing cost attribution across different LLM providers and models"],"limitations":["Trace collection adds ~50-100ms overhead per instrumented function call","Token counting accuracy depends on LLM provider's token counting API; some providers have inconsistencies","Cost calculation requires accurate pricing data; manual updates needed when provider pricing changes","Trace storage scales linearly with request volume; high-traffic applications may require external trace backend"],"requires":["Python SDK integration via @app and @step decorators","OpenTelemetry collector running (included in Docker Compose setup)","LiteLLM proxy for automatic token counting and cost calculation","Backend database for trace storage"],"input_types":["Python function calls (decorated with @app or @step)","LLM API responses (from LiteLLM proxy)","custom span attributes (user-defined metadata)"],"output_types":["trace spans (request/response with latency, tokens, cost)","aggregated metrics (total cost, average latency per step)","trace queries (filtered by time range, model, cost range)","cost breakdown (by model, provider, step)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_6","uri":"capability://code.generation.editing.python.sdk.with.decorator.based.workflow.definition","name":"python sdk with decorator-based workflow definition","description":"Provides a Python library (published as 'agenta' on PyPI) that enables developers to define LLM applications using decorators (@app, @step) that automatically register functions as variants and instrument them for tracing. The SDK handles parameter serialization, testset execution, and result collection without requiring explicit API calls. Applications are defined as Python functions with type-annotated parameters, which are automatically exposed in the web UI as configurable inputs. The SDK supports both synchronous and asynchronous execution.","intents":["I want to define my LLM application in Python without learning a new DSL or API","I need to automatically expose my function parameters in the web UI for non-technical users to configure","I want to run my application against testsets and collect results without writing evaluation code"],"best_for":["Python developers building LLM applications who want minimal framework overhead","teams wanting to integrate Agenta into existing Python codebases with minimal refactoring","developers preferring decorator-based instrumentation over explicit API calls"],"limitations":["SDK limited to Python; no native support for JavaScript, Go, or other languages","Type annotations required for parameter serialization; dynamic typing not supported","Async execution requires Python 3.7+; older versions limited to synchronous functions","SDK version must match backend API version; version mismatches can cause silent failures"],"requires":["Python 3.9+","agenta package installed via pip","Agenta backend running (for registration and execution)","API key or authentication token for backend access"],"input_types":["Python function definitions (with @app or @step decorators)","type-annotated parameters (str, int, float, bool, List, Dict)","testset data (passed to function at execution time)"],"output_types":["variant registration (in backend)","execution results (function return values)","trace spans (automatically collected)","evaluation results (when run against testsets)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_7","uri":"capability://safety.moderation.secrets.management.with.environment.variable.injection","name":"secrets management with environment variable injection","description":"Provides secure storage for API keys and sensitive configuration values (e.g., LLM provider keys, database credentials) with automatic injection into application execution contexts. Secrets are encrypted at rest in the backend database and decrypted only when needed for execution. The system supports both global secrets (shared across workspace) and application-specific secrets. Secrets are never exposed in the web UI or logs; only secret names are visible to users.","intents":["I want to store API keys securely without hardcoding them in my application code","I need to rotate secrets without redeploying my application","I want to restrict which applications can access which secrets"],"best_for":["teams deploying LLM applications with multiple API keys and credentials","organizations with security requirements for credential management","teams needing audit trails of secret access"],"limitations":["Secrets stored in backend database; no integration with external secret managers (HashiCorp Vault, AWS Secrets Manager)","No built-in secret rotation; manual updates required when secrets expire","No audit logging of secret access; cannot track which applications accessed which secrets","Encryption key management is manual; no automatic key rotation"],"requires":["Backend database with encryption support","Environment variable names matching application expectations","Workspace admin access to create/manage secrets"],"input_types":["secret name (string identifier)","secret value (API key, password, token)","scope (global or application-specific)"],"output_types":["environment variables (injected at execution time)","secret metadata (name, scope, creation date, last updated)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_8","uri":"capability://safety.moderation.multi.tenant.workspace.isolation.with.rbac","name":"multi-tenant workspace isolation with rbac","description":"Implements organization and workspace hierarchies with role-based access control (RBAC) to isolate data and functionality across teams. Each workspace has its own applications, testsets, evaluations, and secrets. Users are assigned roles (admin, editor, viewer) that determine which operations they can perform. The system enforces access control at the API level, preventing unauthorized access to workspace data. Authentication is handled via OIDC, SAML, or local accounts.","intents":["I want to organize my team's LLM applications into separate workspaces by project or product","I need to grant different team members different levels of access (read-only vs edit)","I want to ensure that data from one workspace cannot be accessed by users in another workspace"],"best_for":["enterprises deploying Agenta across multiple teams or business units","organizations with strict data isolation requirements","teams needing fine-grained access control for compliance"],"limitations":["RBAC limited to predefined roles (admin, editor, viewer); no custom role creation","No resource-level access control; users with editor role can edit all applications in workspace","No audit logging of access control changes; cannot track who modified permissions","Cross-workspace collaboration requires manual data export/import"],"requires":["Authentication system (OIDC, SAML, or local accounts)","Backend database for workspace and permission storage","API enforcement of access control at endpoint level"],"input_types":["user identity (from authentication system)","workspace ID","resource type (application, testset, evaluation)"],"output_types":["access decision (allow/deny)","filtered resource list (only accessible resources)","permission metadata (role, scope, expiration)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__cap_9","uri":"capability://automation.workflow.docker.compose.deployment.with.environment.configuration","name":"docker compose deployment with environment configuration","description":"Provides production-ready Docker Compose configuration that orchestrates all Agenta services (web frontend, FastAPI backend, PostgreSQL database, OpenTelemetry collector, LiteLLM proxy) with a single command. Configuration is managed via environment variables (.env file), enabling users to customize deployment without modifying Docker Compose files. The setup includes health checks, volume mounts for persistence, and networking configuration. SSL/TLS support is available via reverse proxy configuration.","intents":["I want to deploy Agenta on my own infrastructure without vendor lock-in","I need to customize deployment configuration (database, ports, LLM providers) without modifying code","I want to ensure all services start in the correct order with health checks"],"best_for":["teams deploying Agenta on-premises or in private cloud","organizations with strict data residency requirements","DevOps teams managing infrastructure as code"],"limitations":["Docker Compose suitable for single-node deployments; Kubernetes required for multi-node scaling","No built-in backup/restore functionality; requires external database backup tools","SSL/TLS requires manual reverse proxy configuration (nginx, Traefik); not included in base setup","Database migrations must be run manually; no automatic schema updates on version upgrades"],"requires":["Docker and Docker Compose installed (Docker 20.10+, Compose 1.29+)","Minimum 4GB RAM and 10GB disk space","API keys for LLM providers (OpenAI, Anthropic, etc.)","PostgreSQL database (included in Docker Compose or external)"],"input_types":["environment variables (.env file)","Docker Compose configuration (docker-compose.yml)","SSL certificates (for TLS setup)"],"output_types":["running services (web, API, database, collector)","service logs (stdout/stderr)","health check status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agenta__headline","uri":"capability://data.processing.analysis.open.source.llmops.platform.for.prompt.engineering.and.evaluation","name":"open-source llmops platform for prompt engineering and evaluation","description":"Agenta is an open-source LLMOps platform designed for prompt engineering, evaluation, and deployment of large language model applications, providing tools for testing, human annotation, and automated evaluations.","intents":["best LLMOps platform","LLMOps for prompt engineering","open-source platform for evaluating LLMs","how to deploy LLM applications","tools for prompt testing and evaluation"],"best_for":["developers building LLM applications","teams needing evaluation tools for LLMs"],"limitations":[],"requires":["Docker for deployment"],"input_types":["prompts","evaluation criteria"],"output_types":["evaluation results","performance metrics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Docker Compose or Kubernetes cluster for self-hosted deployment","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Modern web browser with WebSocket support for real-time updates","Python 3.9+ and Node.js 18+ for development","Testset with expected outputs (ground truth labels)","For LLM-as-judge evaluators: API keys for evaluation model provider","Python 3.9+ for custom evaluator development","Backend service running (FastAPI + Celery for async execution)","LiteLLM service running (included in Docker Compose)","API keys for at least one LLM provider"],"failure_modes":["Playground latency depends on selected model provider response time (typically 1-5s per request)","No built-in prompt optimization suggestions — requires manual iteration","Version history stored in backend database; no local-first offline mode for playground","Limited to configured LLM providers; adding new providers requires backend configuration changes","Built-in evaluators are limited to predefined metrics; complex domain-specific scoring requires custom Python evaluators","LLM-as-judge evaluators inherit model hallucination risks and cost scales linearly with testset size","Evaluation latency for large testsets (10k+ cases) can exceed 10 minutes depending on evaluator complexity","No built-in statistical significance testing; requires external tools for confidence intervals","Proxy adds ~100-200ms latency per request due to request translation and token counting","Not all LLM features are supported; advanced features (vision, function calling) may not work across all providers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"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=agenta","compare_url":"https://unfragile.ai/compare?artifact=agenta"}},"signature":"yfcxZzMAcv2tS2sYyM5y1ZBgKKAJYGb7I/xtQyiLjmkqRI1W6HhtzJ2w8I9kjEVeOoLpbLptXbXf/15OxXXyDA==","signedAt":"2026-06-22T01:01:45.273Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agenta","artifact":"https://unfragile.ai/agenta","verify":"https://unfragile.ai/api/v1/verify?slug=agenta","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"}}