{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"braintrust","slug":"braintrust","name":"Braintrust","type":"platform","url":"https://braintrust.dev","page_url":"https://unfragile.ai/braintrust","categories":["observability","model-training","testing-quality"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"braintrust__cap_0","uri":"capability://memory.knowledge.scalable.trace.ingestion.and.storage.with.proprietary.brainstore.database","name":"scalable trace ingestion and storage with proprietary brainstore database","description":"Ingests production execution traces (prompts, responses, tool calls, latency, cost metadata) from AI applications via native SDKs (Python, TypeScript, Go, Ruby, C#) and stores them in Braintrust's proprietary Brainstore database optimized for nested AI data structures. The system handles millions of traces with full-text search and supports querying large, deeply-nested trace hierarchies without flattening. Traces are retained for 14 days (Starter), 30 days (Pro), or custom periods (Enterprise), with per-GB pricing ($4/GB overage on Starter, $3/GB on Pro).","intents":["I need to capture every LLM call, tool invocation, and latency metric from my production AI application without modifying core logic","I want to search across millions of production traces to find patterns, errors, or performance regressions","I need to store and query deeply nested trace data (multi-turn conversations, chained tool calls, branching logic) without flattening or losing context"],"best_for":["AI teams running production applications with high trace volume (100k+ traces/month)","Companies needing compliance-grade trace retention and audit trails","Teams using multiple AI frameworks and providers simultaneously"],"limitations":["Data retention capped at 14 days on Starter tier; Pro/Enterprise required for 30+ days","Proprietary Brainstore database creates vendor lock-in; S3 export available only on Pro/Enterprise tiers","Trace ingestion latency and throughput limits unknown from documentation","No on-premises deployment available for Starter/Pro tiers"],"requires":["Python 3.7+ or TypeScript/Node.js 14+ (SDK version numbers not specified)","API key provisioned from Braintrust dashboard","Network connectivity to Braintrust cloud (or on-prem for Enterprise)","Instrumentation code added to application (framework-agnostic)"],"input_types":["structured trace objects (prompts, completions, tool calls, latencies, costs)","nested JSON/YAML trace hierarchies","metadata tags (environment, model, version, user_id)"],"output_types":["indexed trace records queryable via full-text search","trace export to S3 (Pro/Enterprise only)","trace visualization in web dashboard"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_1","uri":"capability://safety.moderation.llm.as.judge.and.code.based.evaluation.scoring.with.automated.quality.gates","name":"llm-as-judge and code-based evaluation scoring with automated quality gates","description":"Evaluates AI application outputs using three scoring approaches: (1) LLM-as-judge evaluators that use Claude or GPT-4 to score responses against custom rubrics, (2) code-based scorers written in Python/TypeScript that implement custom logic (regex, semantic similarity, domain-specific checks), and (3) human evaluators who manually score outputs via annotation UI. Scores are tracked per evaluation run with versioning, and automated quality gates can block deployments if scores fall below thresholds. Pricing is per-1k scores ($2.50/1k on Starter, $1.50/1k on Pro, with 10k/50k monthly included respectively).","intents":["I want to automatically score LLM outputs against custom criteria (correctness, tone, safety) without writing evaluation infrastructure","I need to catch quality regressions in CI/CD before deploying prompt or model changes","I want to combine automated scoring (LLM judges, code logic) with human review for high-stakes decisions"],"best_for":["Teams deploying LLM applications with strict quality requirements (customer-facing, compliance-sensitive)","Prompt engineers iterating rapidly and needing automated feedback loops","Organizations requiring human-in-the-loop evaluation for regulatory or safety reasons"],"limitations":["LLM-as-judge scoring depends on external model availability and cost (Claude/GPT-4 API calls not included in Braintrust pricing)","Starter tier limited to 1 human review score per project; Pro/Enterprise required for unlimited human scoring","Code-based scorers require manual implementation; no pre-built scorer library documented","Evaluation latency depends on external LLM API response times (typically 1-5 seconds per score)"],"requires":["Evaluation dataset with expected outputs or rubrics","API keys for external LLM providers (OpenAI, Anthropic) if using LLM-as-judge","Python or TypeScript environment for code-based scorers","Braintrust Pro or Enterprise tier for unlimited human scoring"],"input_types":["AI application outputs (text, structured data)","reference/expected outputs for comparison","custom evaluation rubrics (JSON schema)","code-based scorer functions (Python/TypeScript)"],"output_types":["numeric scores (0-1 or custom range)","score metadata (scorer type, latency, cost)","evaluation reports with pass/fail status","regression alerts if scores drop below threshold"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_10","uri":"capability://safety.moderation.role.based.access.control.rbac.and.saml.sso.for.enterprise.compliance","name":"role-based access control (rbac) and saml sso for enterprise compliance","description":"Enterprise-grade access control with role-based permissions (viewer, editor, admin) and SAML/OAuth SSO integration for identity management. Supports fine-grained permissions on projects, datasets, and evaluations. SAML SSO enables centralized authentication via corporate identity providers (Okta, Azure AD, etc.). Available on Pro/Enterprise tiers; Starter tier has basic roles only. Enterprise tier supports custom RBAC policies and BAA (HIPAA) agreements.","intents":["I need to restrict access to sensitive evaluation data based on team roles (data scientists can edit, managers can view)","I want to use our corporate identity provider (Okta) for authentication instead of managing Braintrust passwords","I need HIPAA compliance for handling sensitive customer data in evaluations"],"best_for":["Enterprise organizations with compliance requirements (HIPAA, SOC 2, GDPR)","Teams with multiple roles and need for fine-grained access control","Organizations using centralized identity management (Okta, Azure AD)"],"limitations":["RBAC available only on Pro/Enterprise tiers; Starter tier has basic roles only","SAML SSO available only on Enterprise tier; Pro tier limited to OAuth","Custom RBAC policies available only on Enterprise tier","BAA (HIPAA) agreement requires Enterprise tier and custom negotiation"],"requires":["Braintrust Pro or Enterprise subscription","SAML identity provider (Okta, Azure AD, etc.) for SSO","Enterprise tier for HIPAA BAA agreement"],"input_types":["user identity (email, SAML attributes)","role assignment (viewer, editor, admin)","resource permissions (project, dataset, evaluation)"],"output_types":["authenticated session with role-based permissions","audit logs of access and modifications"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_11","uri":"capability://data.processing.analysis.evaluation.result.comparison.and.regression.analysis.across.versions","name":"evaluation result comparison and regression analysis across versions","description":"Compares evaluation scores across prompt versions, model changes, or time periods to detect regressions and improvements. Generates comparison reports showing score deltas, statistical significance (if applicable), and affected test cases. Supports baseline selection (previous version, main branch, or custom baseline). Regression alerts can be configured to notify teams when scores drop below thresholds. Comparison results are visualized in dashboards and can be exported for reporting.","intents":["I want to see if my new prompt version improved accuracy compared to the current production version","I need to detect which test cases regressed when I changed my prompt","I want to compare evaluation scores across multiple time periods to track quality trends"],"best_for":["Prompt engineers iterating on versions and needing to measure improvements","Teams running continuous evaluation pipelines with automated regression detection","Organizations tracking quality trends over time"],"limitations":["Statistical significance testing not documented; unclear if comparisons use t-tests or other methods","Baseline selection logic not detailed; unclear how 'main branch' baseline is determined in non-git workflows","No built-in visualization of score distributions or confidence intervals","Comparison latency depends on evaluation dataset size and scorer complexity"],"requires":["Multiple evaluation runs with comparable datasets and scorers","Baseline version or time period for comparison"],"input_types":["evaluation results from multiple runs","baseline selection (version, branch, or time period)","comparison filters (test case category, scorer type)"],"output_types":["comparison report with score deltas","regression alerts if thresholds exceeded","visualization of score changes across test cases","statistical summary (mean, std dev, min/max)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_12","uri":"capability://safety.moderation.compliance.and.security.certifications.with.data.governance","name":"compliance and security certifications with data governance","description":"Provides SOC 2 Type II, GDPR, and HIPAA compliance certifications with Business Associate Agreement (BAA) available on Enterprise tier. Implements data governance controls including encryption, access logging, and data residency options. Supports on-premises or hosted deployment for Enterprise customers requiring data sovereignty.","intents":["I need to ensure my AI observability platform meets compliance requirements (SOC 2, GDPR, HIPAA)","I want to deploy Braintrust on-premises for data sovereignty or regulatory requirements","I need to audit data access and ensure encryption for sensitive AI system data"],"best_for":["healthcare organizations requiring HIPAA compliance","enterprises with GDPR data residency requirements","organizations with SOC 2 audit requirements","companies requiring on-premises deployment for data sovereignty"],"limitations":["HIPAA compliance requires Enterprise tier with BAA — not available on Pro or Starter","On-premises deployment details not specified — unclear what infrastructure is required","Data residency options not documented — unclear which regions support on-premises deployment","Encryption details not specified — unclear if encryption is at-rest, in-transit, or both","Audit logging capabilities not fully documented"],"requires":["Enterprise Braintrust tier for HIPAA/BAA and on-premises options","Signed Business Associate Agreement (for HIPAA)","Infrastructure for on-premises deployment (if applicable)"],"input_types":["compliance configuration","data residency preferences","encryption key management"],"output_types":["compliance attestations and certifications","audit logs","encryption enforcement"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_2","uri":"capability://text.generation.language.interactive.prompt.playground.with.a.b.comparison.and.environment.tagging","name":"interactive prompt playground with a/b comparison and environment tagging","description":"Web-based IDE for iterating on prompts with real-time execution against live LLM APIs (OpenAI, Anthropic, etc.). Supports side-by-side A/B comparison of prompt versions, variable templating, and environment-specific configuration (dev/staging/prod with different models or parameters). Prompt versions are automatically versioned and tagged with metadata (author, timestamp, environment). Playground annotations enable inline comments on prompt iterations. Available on Pro tier and above; Starter tier has no playground access.","intents":["I want to experiment with prompt variations and see results side-by-side without context-switching to code","I need to manage different prompt versions for different environments (dev uses GPT-3.5, prod uses GPT-4)","I want to collaborate with non-technical team members on prompt refinement with version history"],"best_for":["Prompt engineers and product managers iterating on LLM behavior","Teams with non-technical stakeholders who need to review and approve prompts","Organizations managing multiple prompt variants across environments"],"limitations":["Playground available only on Pro/Enterprise tiers; Starter tier excluded","Annotations feature not available on Starter tier","No built-in prompt optimization suggestions; requires manual iteration","Execution cost (LLM API calls) billed separately to external provider accounts"],"requires":["Braintrust Pro or Enterprise subscription","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Web browser with JavaScript enabled"],"input_types":["prompt templates with variable placeholders","model selection (GPT-4, Claude, etc.)","environment configuration (parameters, temperature, max_tokens)","test inputs for prompt execution"],"output_types":["LLM completions side-by-side for A/B comparison","prompt version history with metadata","environment-tagged prompt variants","annotation comments on prompt iterations"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_3","uri":"capability://data.processing.analysis.versioned.dataset.management.with.test.case.organization.and.export","name":"versioned dataset management with test case organization and export","description":"Centralized repository for organizing evaluation test cases (inputs, expected outputs, metadata) with automatic versioning and branching. Datasets can be created from production traces (sampling real user inputs), manually uploaded (CSV/JSON), or generated by the Loop agent. Datasets are tagged with metadata (version, author, creation date) and can be filtered by attributes. Supports exporting datasets for use in external evaluation frameworks. Dataset versions are immutable, enabling reproducible evaluations across time.","intents":["I want to build a curated test set from production traces to evaluate prompt changes against real user patterns","I need to version my evaluation datasets so I can reproduce evaluation results from 3 months ago","I want to organize test cases by category (edge cases, happy path, error handling) and reuse them across multiple evaluations"],"best_for":["Teams running continuous evaluation pipelines with versioned test sets","Organizations needing reproducible evaluation across time (regulatory, compliance)","Prompt engineers building curated test suites for specific use cases"],"limitations":["No built-in dataset versioning branching (linear versioning only, no merge/conflict resolution)","Dataset size limits unknown from documentation","No collaborative editing of datasets; single author per version","Export format limited to CSV/JSON; no integration with external test frameworks documented"],"requires":["Braintrust account with dataset creation permissions","Test case data in structured format (JSON, CSV, or production traces)"],"input_types":["production traces (auto-sampled for dataset creation)","CSV/JSON files with test cases","metadata tags (category, difficulty, domain)"],"output_types":["versioned dataset records with immutable snapshots","dataset exports (CSV, JSON)","dataset statistics (size, coverage, metadata distribution)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_4","uri":"capability://automation.workflow.ci.cd.integration.with.automated.regression.detection.and.deployment.gates","name":"ci/cd integration with automated regression detection and deployment gates","description":"Integrates with CI/CD pipelines (GitHub Actions, GitLab CI, etc.) to automatically run evaluations on prompt or model changes and block deployments if quality scores regress below configured thresholds. Compares current evaluation results against baseline (previous version or main branch) and generates pass/fail reports. Supports custom quality gates (e.g., 'accuracy must stay above 90%' or 'latency must not increase by >10%'). Integration is framework-agnostic and triggered via webhook or API calls from CI/CD runners.","intents":["I want to prevent prompt regressions from reaching production by automatically evaluating changes in CI","I need to compare evaluation scores between my current branch and main to see if my changes improved or hurt quality","I want to set quality thresholds (e.g., accuracy > 95%) that must be met before a deployment is allowed"],"best_for":["Teams with continuous deployment pipelines for LLM applications","Organizations requiring automated quality gates for regulatory compliance","Prompt engineers working in collaborative environments with multiple contributors"],"limitations":["Specific CI/CD platform integrations not documented; webhook/API approach requires custom scripting","Baseline comparison logic not detailed; unclear if it compares to main branch, previous version, or custom baseline","No built-in rollback mechanism; deployment blocking is the only gate (manual rollback required)","Evaluation latency (time to run all scorers) directly impacts CI/CD pipeline duration"],"requires":["CI/CD pipeline with webhook or API call capability","Evaluation dataset and scorers configured in Braintrust","Custom CI/CD script or GitHub Action to trigger evaluations and check results"],"input_types":["prompt or model changes (detected via git diff or explicit trigger)","evaluation dataset version","quality threshold configuration (JSON)"],"output_types":["pass/fail status for CI/CD pipeline","comparison report (current vs. baseline scores)","regression alerts with score deltas"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_5","uri":"capability://search.retrieval.real.time.trace.monitoring.with.full.text.search.and.pattern.discovery.via.topics","name":"real-time trace monitoring with full-text search and pattern discovery via topics","description":"Live dashboard for monitoring production traces in real-time with filtering, sorting, and full-text search across prompt/response content and metadata. 'Topics' feature uses LLM-powered pattern discovery to automatically classify traces into categories (e.g., 'user authentication errors', 'slow API calls') based on custom prompts. Supports custom trace views with annotation interfaces for human review. Alerts can be configured to notify teams when specific patterns emerge or metrics exceed thresholds (latency, cost, error rate). Topics feature available on Pro/Enterprise tiers only.","intents":["I want to search production traces to find all instances where my LLM returned a specific error or pattern","I need to automatically categorize traces into buckets (e.g., 'hallucinations', 'timeouts', 'cost overruns') without manual labeling","I want to get alerted immediately if latency spikes or error rates increase in production"],"best_for":["Production support teams monitoring LLM application health","Data scientists analyzing failure modes and edge cases in production","Teams needing to detect emerging issues before users report them"],"limitations":["Topics feature (pattern discovery) available only on Pro/Enterprise tiers; Starter tier excluded","Custom trace views and annotations not available on Starter tier","Full-text search performance depends on trace volume and Brainstore query optimization (specific latency SLAs unknown)","Topic classification depends on custom prompt quality; no pre-built topic templates documented"],"requires":["Production traces already ingested into Braintrust","Braintrust Pro or Enterprise tier for Topics feature","Custom topic prompts (LLM-based classification rules)"],"input_types":["production traces (prompts, responses, metadata)","search queries (full-text or structured filters)","topic classification prompts (custom rules)"],"output_types":["filtered trace results with metadata","topic-classified trace buckets","alert notifications (email, webhook)","trace statistics (error rate, latency percentiles, cost distribution)"],"categories":["search-retrieval","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_6","uri":"capability://planning.reasoning.loop.agent.for.autonomous.prompt.and.dataset.optimization","name":"loop agent for autonomous prompt and dataset optimization","description":"AI agent that autonomously iterates on prompts, scorers, and datasets to improve evaluation scores. Given a high-level optimization goal (e.g., 'improve accuracy on customer support responses'), Loop generates new prompt variations, creates additional test cases, and runs evaluations to find improvements. Operates in a feedback loop: evaluate → analyze results → generate improvements → re-evaluate. Results are tracked with version history and can be reviewed/approved before deployment. Available on Pro/Enterprise tiers only; Starter tier excluded.","intents":["I want to automatically improve my prompt without manually iterating through dozens of variations","I need to generate additional test cases to cover edge cases my current dataset misses","I want an AI agent to explore the prompt optimization space and suggest the best changes"],"best_for":["Teams with limited prompt engineering expertise seeking automated optimization","Organizations running continuous improvement pipelines for LLM applications","Prompt engineers wanting to explore optimization space faster than manual iteration"],"limitations":["Loop agent available only on Pro/Enterprise tiers; Starter tier excluded","Optimization goal specification unclear; no documentation on how to define optimization objectives","No guarantees on optimization quality or convergence; results depend on scorer quality and dataset coverage","Loop-generated prompts require human review before deployment; no auto-deployment capability","Optimization cost (LLM API calls for generation + evaluation) not included in Braintrust pricing"],"requires":["Braintrust Pro or Enterprise subscription","Evaluation dataset with scorers configured","Clear optimization objective (e.g., 'maximize accuracy', 'minimize latency')","API keys for external LLM providers (for prompt generation)"],"input_types":["optimization goal (natural language description)","current prompt version","evaluation dataset and scorers","constraints (e.g., 'max tokens: 500')"],"output_types":["generated prompt variations with scores","generated test cases for dataset expansion","optimization report with before/after comparisons","recommended best prompt with justification"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_7","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.framework.agnostic.sdk.instrumentation","name":"multi-provider llm integration with framework-agnostic sdk instrumentation","description":"Framework-agnostic SDKs (Python, TypeScript, Go, Ruby, C#) that instrument AI applications to send traces to Braintrust without requiring framework-specific adapters. Supports any LLM provider (OpenAI, Anthropic, Cohere, local models) and any AI framework (LangChain, LlamaIndex, custom code). Instrumentation is non-invasive: add a few lines of code to initialize the Braintrust client and wrap LLM calls. SDKs automatically capture prompts, completions, latency, cost, and tool calls. No vendor lock-in at the SDK level; traces can be exported to S3 (Pro/Enterprise only).","intents":["I want to add observability to my LLM application without rewriting code or adopting a specific framework","I need to capture traces from multiple LLM providers (OpenAI for some tasks, Anthropic for others) in a single system","I want to instrument my application with minimal code changes and no framework dependencies"],"best_for":["Teams using heterogeneous LLM stacks (multiple providers and frameworks)","Developers wanting observability without framework lock-in","Organizations with existing codebases that need minimal instrumentation overhead"],"limitations":["SDK version numbers not documented; unclear which Python/TypeScript versions are supported","Instrumentation overhead (latency added per trace) not quantified","No built-in batching or async trace ingestion documented; unclear if traces are sent synchronously or asynchronously","Framework-agnostic approach may lack deep integrations with specific frameworks (e.g., LangChain callback hooks)"],"requires":["Python 3.7+ or TypeScript/Node.js 14+ (exact versions not specified)","Braintrust API key","Network connectivity to Braintrust cloud","Minimal code changes to initialize client and wrap LLM calls"],"input_types":["LLM API calls (prompts, model selection, parameters)","tool invocations and results","application metadata (user_id, session_id, environment)"],"output_types":["structured trace records sent to Braintrust","trace metadata (latency, cost, tokens, model)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_8","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.for.ide.integrated.observability.and.optimization","name":"mcp (model context protocol) server for ide-integrated observability and optimization","description":"Braintrust exposes a Model Context Protocol (MCP) server that connects coding agents and IDEs to the Braintrust platform, enabling queries and operations from within development environments. Supports querying logs/traces, running evaluations, and updating prompts directly from IDE or agent context. Enables use cases like 'ask Claude to analyze my production traces' or 'have an agent automatically run evals and suggest prompt improvements'. MCP integration allows AI agents to autonomously interact with Braintrust data and workflows.","intents":["I want my coding agent to query production traces and suggest fixes without leaving the IDE","I need to run evaluations and get results directly in my development environment","I want an AI agent to autonomously analyze my traces, identify issues, and suggest prompt improvements"],"best_for":["Developers using AI coding agents (Claude, ChatGPT with plugins) for development","Teams integrating Braintrust into agentic workflows","Organizations wanting AI-assisted debugging and optimization"],"limitations":["MCP server capabilities not fully documented; unclear which Braintrust operations are exposed","Requires MCP-compatible IDE or agent (limited adoption as of 2024)","Security implications of exposing Braintrust API via MCP not discussed; unclear if rate limiting or access controls are enforced","No examples or documentation of MCP usage patterns provided"],"requires":["MCP-compatible IDE or AI agent (Claude, ChatGPT, etc.)","Braintrust API key configured in MCP server","Network connectivity to Braintrust cloud"],"input_types":["natural language queries (e.g., 'show me traces with latency > 5s')","evaluation run requests","prompt update commands"],"output_types":["trace query results","evaluation reports","prompt update confirmations"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__cap_9","uri":"capability://data.processing.analysis.s3.export.for.long.term.trace.archival.and.downstream.analysis","name":"s3 export for long-term trace archival and downstream analysis","description":"Automatically exports traces to customer-owned S3 buckets for long-term storage and analysis outside Braintrust. Enables data retention beyond Braintrust's limits (14/30 days default) and allows integration with downstream analytics tools (Snowflake, BigQuery, custom data pipelines). Export is asynchronous and can be scheduled. Exported traces are in JSON format with full metadata. Available on Pro/Enterprise tiers only; Starter tier excluded.","intents":["I need to retain production traces longer than 30 days for compliance or historical analysis","I want to analyze traces in my own data warehouse (Snowflake, BigQuery) alongside other business data","I need to ensure data sovereignty by storing traces in my own AWS account"],"best_for":["Organizations with compliance requirements (HIPAA, SOC 2) requiring long-term data retention","Teams with existing data warehouses wanting to integrate Braintrust traces","Companies needing data sovereignty or avoiding vendor lock-in"],"limitations":["S3 export available only on Pro/Enterprise tiers; Starter tier excluded","Export frequency and scheduling not documented; unclear if exports are real-time, daily, or on-demand","S3 bucket configuration and IAM permissions required; customer responsible for access control","No built-in transformation or schema mapping; exported traces are raw JSON"],"requires":["Braintrust Pro or Enterprise subscription","AWS S3 bucket with write permissions","IAM role or credentials for Braintrust to write to S3","S3 bucket configured in Braintrust settings"],"input_types":["traces stored in Braintrust","S3 bucket configuration (bucket name, region, prefix)"],"output_types":["JSON-formatted trace files in S3","export logs with status and error details"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"braintrust__headline","uri":"capability://data.processing.analysis.ai.product.evaluation.and.observability.platform","name":"ai product evaluation and observability platform","description":"Braintrust is an AI product evaluation and observability platform that provides tools for logging, tracing, and dataset management, enabling automated quality checks in CI/CD workflows.","intents":["best AI observability platform","AI evaluation tool for quality checks","AI logging and tracing solution","platform for dataset management in AI","CI/CD integration for AI products"],"best_for":["AI companies","developers seeking quality assurance"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ or TypeScript/Node.js 14+ (SDK version numbers not specified)","API key provisioned from Braintrust dashboard","Network connectivity to Braintrust cloud (or on-prem for Enterprise)","Instrumentation code added to application (framework-agnostic)","Evaluation dataset with expected outputs or rubrics","API keys for external LLM providers (OpenAI, Anthropic) if using LLM-as-judge","Python or TypeScript environment for code-based scorers","Braintrust Pro or Enterprise tier for unlimited human scoring","Braintrust Pro or Enterprise subscription","SAML identity provider (Okta, Azure AD, etc.) for SSO"],"failure_modes":["Data retention capped at 14 days on Starter tier; Pro/Enterprise required for 30+ days","Proprietary Brainstore database creates vendor lock-in; S3 export available only on Pro/Enterprise tiers","Trace ingestion latency and throughput limits unknown from documentation","No on-premises deployment available for Starter/Pro tiers","LLM-as-judge scoring depends on external model availability and cost (Claude/GPT-4 API calls not included in Braintrust pricing)","Starter tier limited to 1 human review score per project; Pro/Enterprise required for unlimited human scoring","Code-based scorers require manual implementation; no pre-built scorer library documented","Evaluation latency depends on external LLM API response times (typically 1-5 seconds per score)","RBAC available only on Pro/Enterprise tiers; Starter tier has basic roles only","SAML SSO available only on Enterprise tier; Pro tier limited to OAuth","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.35,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.013Z","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=braintrust","compare_url":"https://unfragile.ai/compare?artifact=braintrust"}},"signature":"Z9jgN1xy7ccyDHgSGgrWMHEzCLWSJMCvCiYucjElesJWPLJGVNPKD4OQRoOi/sDFkN2SyhBrNcPeUAmRPMXSAg==","signedAt":"2026-06-20T09:11:53.451Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/braintrust","artifact":"https://unfragile.ai/braintrust","verify":"https://unfragile.ai/api/v1/verify?slug=braintrust","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"}}