{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-wren","slug":"wren","name":"Wren","type":"product","url":"https://www.getwren.ai/","page_url":"https://unfragile.ai/wren","categories":["data-pipelines"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-wren__cap_0","uri":"capability://data.processing.analysis.natural.language.to.sql.query.translation","name":"natural language to sql query translation","description":"Converts natural language questions into executable SQL queries by parsing user intent through an LLM-powered semantic understanding layer, then mapping that intent to database schema metadata. The system maintains a semantic index of table and column definitions, allowing the LLM to reason about which database objects are relevant to the user's question before generating syntactically correct SQL that executes against the target database.","intents":["Ask questions about my database without knowing SQL syntax","Generate complex queries from plain English descriptions","Explore database structure and relationships conversationally","Get instant answers to ad-hoc analytical questions"],"best_for":["Business analysts and non-technical stakeholders querying databases","Data teams reducing time spent writing boilerplate SQL","Organizations onboarding users to self-service analytics"],"limitations":["Accuracy depends on schema clarity and naming conventions — poorly documented or ambiguously-named columns reduce translation quality","Complex multi-join queries with subqueries may generate suboptimal SQL requiring manual optimization","Database-specific SQL dialects (PostgreSQL vs MySQL vs T-SQL) require explicit configuration","No built-in handling of dynamic filters or parameterized queries — requires post-processing for security"],"requires":["Active database connection (PostgreSQL, MySQL, Snowflake, BigQuery, or other supported DBMS)","Database schema introspection permissions to read table and column metadata","LLM API access (OpenAI, Anthropic, or self-hosted model endpoint)","Network connectivity between Wren service and database"],"input_types":["natural language text (English or other supported languages)","conversational follow-up questions with implicit context"],"output_types":["SQL query string","Query execution results (tabular data)","Query explanation in natural language"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_1","uri":"capability://data.processing.analysis.multi.database.schema.federation.and.querying","name":"multi-database schema federation and querying","description":"Enables querying across multiple heterogeneous databases (PostgreSQL, MySQL, Snowflake, BigQuery, etc.) through a unified natural language interface by maintaining separate semantic indexes for each database and routing queries to the appropriate backend based on table references detected in the translated SQL. The system handles cross-database join logic and result aggregation when queries span multiple sources.","intents":["Query data spread across multiple databases without writing federation logic","Ask questions that require joining data from different systems","Explore relationships between tables across database boundaries","Consolidate analytics across heterogeneous data infrastructure"],"best_for":["Enterprise organizations with polyglot data stacks","Teams managing data warehouses alongside operational databases","Analytics teams needing unified access to siloed data sources"],"limitations":["Cross-database joins require network round-trips and client-side result merging, adding latency for large result sets","No automatic optimization of join order across databases — may execute inefficient queries if tables are on different systems","Transactions spanning multiple databases are not supported","Database-specific functions and operators may not translate across systems"],"requires":["Connection credentials and network access to all target databases","Schema introspection permissions on each database","Explicit configuration mapping which tables belong to which database"],"input_types":["natural language questions referencing tables from any configured database"],"output_types":["Unified result set (merged from multiple databases)","SQL queries routed to appropriate backends"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_2","uri":"capability://memory.knowledge.semantic.schema.understanding.and.documentation.generation","name":"semantic schema understanding and documentation generation","description":"Automatically generates human-readable documentation and semantic descriptions for database schemas by analyzing table names, column names, relationships, and data types, then enriching this metadata with LLM-generated summaries of what each table represents and how tables relate to each other. Users can also manually annotate schemas with business context, which is then incorporated into the semantic index to improve query translation accuracy.","intents":["Understand what data is available in my database without reading raw schema dumps","Generate documentation for databases that lack proper documentation","Add business context and definitions to technical schema metadata","Improve NL2SQL accuracy by providing domain-specific terminology"],"best_for":["Data teams documenting legacy databases","Organizations improving data literacy across non-technical teams","Teams customizing semantic understanding for domain-specific terminology"],"limitations":["Auto-generated descriptions may be inaccurate for tables with cryptic or abbreviated names","Manual annotation requires effort and must be maintained as schema evolves","No automatic detection of deprecated or unused tables","Semantic understanding is static — does not adapt to changing data patterns over time"],"requires":["Database schema introspection access","Optional: manual annotation interface or API for adding business context"],"input_types":["database schema metadata (tables, columns, types, constraints)","optional: manual text annotations describing business meaning"],"output_types":["human-readable schema documentation","semantic index used for query translation","table and column descriptions"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_3","uri":"capability://planning.reasoning.conversational.query.refinement.and.follow.up.question.handling","name":"conversational query refinement and follow-up question handling","description":"Maintains conversation context across multiple turns, allowing users to ask follow-up questions that implicitly reference previous queries or results. The system tracks the conversation history, the last executed query, and result metadata, enabling it to resolve pronouns and relative references (e.g., 'show me the top 10' after a previous query) without requiring full re-specification. Context is managed through a sliding window of recent exchanges to keep LLM context manageable.","intents":["Ask follow-up questions that build on previous query results","Refine queries iteratively without restating the full question","Explore data through natural conversational interaction","Drill down into results with relative references like 'top 10' or 'by region'"],"best_for":["Interactive data exploration workflows","Non-technical users who expect conversational interfaces","Analysts iteratively refining queries based on results"],"limitations":["Context window limitations mean very long conversations may lose earlier context","Ambiguous follow-ups may be misinterpreted if context is insufficient","No persistent conversation history across sessions unless explicitly stored","Complex multi-turn reasoning chains may accumulate interpretation errors"],"requires":["Session management to track conversation state","LLM with sufficient context window to hold conversation history"],"input_types":["natural language follow-up questions","implicit references to previous results"],"output_types":["refined SQL queries","query results","clarification questions if intent is ambiguous"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_4","uri":"capability://text.generation.language.query.result.explanation.and.insight.generation","name":"query result explanation and insight generation","description":"Automatically generates natural language explanations of query results, including summaries of what the data shows, identification of notable patterns or outliers, and business-relevant insights. The system analyzes result statistics (row counts, value distributions, aggregations) and uses LLM reasoning to surface actionable insights without requiring users to manually interpret raw data.","intents":["Understand what my query results mean in business terms","Identify patterns or anomalies in query results automatically","Get natural language summaries of large result sets","Surface insights without manual data interpretation"],"best_for":["Business users who need insights but lack data analysis skills","Teams accelerating time-to-insight from data queries","Organizations democratizing data access to non-technical stakeholders"],"limitations":["Insight generation quality depends on result size and statistical significance — small result sets may produce generic insights","No domain-specific insight rules — insights are generic statistical observations","Cannot detect business-specific anomalies without explicit configuration","Explanations may be verbose for large result sets with many columns"],"requires":["Query results with sufficient rows and columns for statistical analysis","LLM access for natural language generation"],"input_types":["query result set (tabular data)","optional: domain context or insight rules"],"output_types":["natural language summary of results","identified patterns and outliers","business-relevant insights"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_5","uri":"capability://safety.moderation.access.control.and.query.auditing","name":"access control and query auditing","description":"Enforces row-level and column-level access control by intercepting translated SQL queries and applying security policies before execution. The system logs all queries executed through the natural language interface, including the original natural language question, translated SQL, user identity, and results, enabling audit trails and compliance reporting. Access policies are defined at the database or table level and are applied transparently during query translation.","intents":["Ensure users can only query data they have permission to access","Maintain audit trails of all database queries for compliance","Prevent accidental or intentional data exposure through NL2SQL","Enforce data governance policies at the query layer"],"best_for":["Organizations with strict data governance or compliance requirements","Teams managing sensitive data across multiple user roles","Enterprises deploying self-service analytics with access controls"],"limitations":["Row-level access control requires query rewriting which may impact performance","Complex access policies may not translate cleanly to SQL WHERE clauses","Audit logging adds overhead and requires persistent storage","No built-in integration with external identity providers — requires custom configuration"],"requires":["User authentication and identity management system","Access policy definitions (role-based or attribute-based)","Audit log storage (database or external logging service)"],"input_types":["user identity and roles","access policies","natural language queries"],"output_types":["access-controlled query results","audit logs with query metadata"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_6","uri":"capability://automation.workflow.caching.and.query.optimization.for.repeated.questions","name":"caching and query optimization for repeated questions","description":"Caches previously executed queries and their results, allowing the system to return cached results for identical or semantically similar natural language questions without re-executing against the database. The cache is indexed by semantic similarity of the natural language input, not exact string matching, so variations of the same question can hit the cache. Cache invalidation is managed based on table update frequency and explicit refresh policies.","intents":["Speed up repeated queries by serving cached results","Reduce database load from common analytical questions","Provide instant results for frequently asked questions","Optimize cost for cloud databases charged per query"],"best_for":["Organizations with repetitive analytical queries","Teams using cloud databases with per-query pricing","High-traffic analytics platforms serving many users"],"limitations":["Cache invalidation is approximate — stale results may be served if table update frequency is not accurately configured","Semantic similarity matching may incorrectly cache results for different questions","Cache storage requires additional infrastructure and memory","No built-in cache warming or precomputation of common queries"],"requires":["Cache storage backend (in-memory or distributed cache)","Table update frequency metadata for cache invalidation","Semantic similarity model for matching questions"],"input_types":["natural language questions"],"output_types":["cached query results (if hit)","fresh query results (if miss)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_7","uri":"capability://automation.workflow.scheduled.query.execution.and.reporting","name":"scheduled query execution and reporting","description":"Allows users to define natural language questions as scheduled queries that execute on a recurring basis (daily, weekly, monthly) and automatically generate reports or notifications with results. The system translates the natural language question once, stores the resulting SQL, and executes it on schedule, then formats results into reports (PDF, email, dashboard) and distributes them to specified recipients.","intents":["Automate generation of regular reports without manual query execution","Set up alerts when query results meet certain conditions","Distribute data insights to stakeholders on a schedule","Create dashboards that refresh automatically with latest data"],"best_for":["Teams generating regular reports for stakeholders","Organizations automating data delivery workflows","Analysts reducing time spent on repetitive reporting"],"limitations":["Scheduled queries are static — cannot adapt to changing business logic without manual updates","No built-in conditional logic for dynamic scheduling based on data conditions","Report formatting is limited to predefined templates","Requires persistent job scheduling infrastructure"],"requires":["Job scheduler (cron, Airflow, or similar)","Report generation and distribution service","Email or notification delivery infrastructure"],"input_types":["natural language question","schedule definition (frequency, time)","recipient list"],"output_types":["scheduled query execution","formatted reports (PDF, email, dashboard)","notifications"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_8","uri":"capability://memory.knowledge.data.lineage.and.impact.analysis.for.queries","name":"data lineage and impact analysis for queries","description":"Tracks the data lineage of query results by analyzing which tables and columns are referenced in the translated SQL, then provides impact analysis showing what downstream reports or dashboards depend on those tables. The system maintains a dependency graph of queries and their source tables, enabling users to understand data provenance and assess the impact of schema changes or data quality issues.","intents":["Understand which tables and columns my query depends on","Trace data lineage from source tables to final results","Assess impact of schema changes on existing queries","Identify data quality issues affecting downstream reports"],"best_for":["Data teams managing complex data pipelines","Organizations implementing data governance","Teams troubleshooting data quality issues"],"limitations":["Lineage tracking is limited to direct table references — does not track transformations within database views or stored procedures","Impact analysis requires maintaining a complete dependency graph which may be expensive for large query volumes","No automatic detection of indirect dependencies through shared tables","Lineage information is only available for queries executed through Wren"],"requires":["Query execution history and metadata storage","Dependency graph database or similar structure"],"input_types":["executed queries (SQL or natural language)"],"output_types":["data lineage diagram","list of source tables and columns","impact analysis (downstream dependencies)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-wren__cap_9","uri":"capability://image.visual.natural.language.to.visualization.generation","name":"natural language to visualization generation","description":"Automatically generates appropriate visualizations (charts, graphs, tables) for query results based on the structure of the data and the intent of the original natural language question. The system analyzes result columns (numeric, categorical, temporal) and the question context to recommend visualization types (bar chart, line graph, scatter plot, etc.), then renders interactive visualizations that users can customize.","intents":["Visualize query results without manually selecting chart types","Generate appropriate charts based on data structure and question intent","Create interactive dashboards from natural language questions","Explore data visually without technical visualization knowledge"],"best_for":["Business users exploring data visually","Teams accelerating dashboard creation","Organizations democratizing data visualization"],"limitations":["Automatic visualization selection may choose suboptimal chart types for complex data","No support for advanced visualization types (network graphs, geographic maps) without custom configuration","Customization of visualizations requires UI interaction or API calls","Large result sets may render slowly or be difficult to visualize effectively"],"requires":["Query result set with typed columns","Visualization rendering engine (D3.js, Plotly, or similar)"],"input_types":["query result set (tabular data)","natural language question context"],"output_types":["interactive visualizations (charts, graphs)","visualization configuration (chart type, axes, colors)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Active database connection (PostgreSQL, MySQL, Snowflake, BigQuery, or other supported DBMS)","Database schema introspection permissions to read table and column metadata","LLM API access (OpenAI, Anthropic, or self-hosted model endpoint)","Network connectivity between Wren service and database","Connection credentials and network access to all target databases","Schema introspection permissions on each database","Explicit configuration mapping which tables belong to which database","Database schema introspection access","Optional: manual annotation interface or API for adding business context","Session management to track conversation state"],"failure_modes":["Accuracy depends on schema clarity and naming conventions — poorly documented or ambiguously-named columns reduce translation quality","Complex multi-join queries with subqueries may generate suboptimal SQL requiring manual optimization","Database-specific SQL dialects (PostgreSQL vs MySQL vs T-SQL) require explicit configuration","No built-in handling of dynamic filters or parameterized queries — requires post-processing for security","Cross-database joins require network round-trips and client-side result merging, adding latency for large result sets","No automatic optimization of join order across databases — may execute inefficient queries if tables are on different systems","Transactions spanning multiple databases are not supported","Database-specific functions and operators may not translate across systems","Auto-generated descriptions may be inaccurate for tables with cryptic or abbreviated names","Manual annotation requires effort and must be maintained as schema evolves","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.25,"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":"2026-06-17T09:51:04.690Z","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=wren","compare_url":"https://unfragile.ai/compare?artifact=wren"}},"signature":"92gR6gsfij5tiA2qluFVuEKQzlhhKsqN5aNbS55NX4tR/8nuefUf33PUViOoS0OKXLzC/9qOnvbZez1jhfeKCw==","signedAt":"2026-06-21T15:41:03.999Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wren","artifact":"https://unfragile.ai/wren","verify":"https://unfragile.ai/api/v1/verify?slug=wren","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"}}