The insights table is the data source that Analysis Recommendations build upon to generate actionable cards using the project's goal context.

InsightService uses AnalysisSuggestion to generate smart analysis recommendations. InsightService generates AnalysisSuggestion using schema analysis via Ollama.

The Install_docling_elin.sh script installs Docling 2.75.0 and dependencies on the elin GPU server for mandatory extraction.

Integration requires adding the DiscoveryFlow component to the analysis page for front-end functionality. Integration requires registering and using the Backend service's API endpoints for discovery. Integration involves modifying backend/app/main.py to register discovery API routes. Integration requires modifying backend/app/services/question_router.py to incorporate consolidated views in query routing. Integration uses backend/app/services/consolidation.py for handling consolidated views in the analysis pipeline. The E2E test suite validates Integration by verifying all tests pass once components are wired together.

Intelligent consolidation is a capability within the Data Discovery feature. Intelligent consolidation is supported via the /api/v1/discovery endpoints (search, consolidate, preview). Intelligent consolidation uses TableIndex for semantic table matching. The Consolidation Mechanism produces Consolidated Unified Views by creating session-scoped joins of related tables for queries. Budget Analysis Cluster requires the Consolidation Mechanism to combine related budget tables for comprehensive queries. Payment & Commitment Cluster requires the Consolidation Mechanism to join payments and commitments data tables appropriately. Monitoring Cluster requires the Consolidation Mechanism to unify monitoring-related datasets for accurate analysis. Grant Administration Cluster relies on the Consolidation Mechanism for consolidating grant-related tables for analysis. The Data Consolidation capability creates TEMP VIEWs to unify related tables into a single schema for Arctic SQL generation. The Discovery Service implements the Schema consolidation mechanism to improve query success rate. The Schema consolidation mechanism is planned to be presented via the Schema relationship explorer UI for better transparency and customization. The Data Consolidation process stores consolidation recommendations for user reference. The Data Consolidation capability creates TEMP VIEWs to unify related tables into a single schema for Arctic SQL generation. The Discovery Service implements the Schema consolidation mechanism to improve query success rate. The Schema consolidation mechanism is planned to be presented via the Schema relationship explorer UI for better transparency and customization. The Data Consolidation process stores consolidation recommendations for user reference. Intelligent consolidation improves the query success rate from 70% to over 95%. The Data Discovery feature uses intelligent consolidation to enhance query success rate.

Built as part of DataLens' data discovery system, it automates table ranking and join discovery, enhancing query success from 70% to over 95%. It uses entity extraction, relevance scoring, and pattern recognition to pre-select related tables, creating transient views for Arctic to generate accurate SQL.

DataLens Agent Mode uses IronClaw as the underlying AI agent framework for autonomous data analysis sessions. IronClaw incorporates WASM sandboxing as a technical constraint for tool isolation. IronClaw is constrained by the use of TEE credential vault for security. IronClaw conforms to a GDPR-compatible security model to ensure data protection compliance. Agent Skills Integration renders each streamed finding as a separate IronClawMessage. IronClaw agent frontend components implement the user interface for the IronClaw agent feature. IronClaw backend endpoints expose API functionality required by the IronClaw agent feature. IronClaw agent's data and sessions are stored in specific agent session database tables. IronClaw agent depends on PostgreSQL database to store sessions, messages, findings, and skill logs. IronClaw agent back-end logic is integrated with the main app via API router registration. DataLens Agent Mode uses IronClaw as the AI agent framework to power autonomous data analysis sessions. OpenClaw is disqualified in favor of IronClaw due to security concerns preventing its use for personal data workloads.

Full implementation of IronClaw agent deployed, accessible via UI buttons, which initiate autonomous sessions using 4600+ code lines. Features include chat interface, GDPR detection, skill management, and backend integration, now online for project analysis.

The IronClaw agent feature includes autonomous agent orchestration, UI navigation buttons, and frontend user interface components. It relies on the IronClaw agent tables for storing session and message data, with high implementation priority, now deployed and integrated into DataLens, enabling the agent to process questions in Danish and generate responses using the Arctic and SQLCoder models.

The IronClaw agent feature is now accessible via UI buttons on project and analysis pages, deployed from commit e383323, with full functionality including chat, findings, and GDPR features. It is production-ready and live for user interaction. IronClaw Agent Feature relies on PostgreSQL database with 6 new tables created to function correctly. IronClaw Agent Feature uses FastAPI for backend API implementation including asynchronous generators. IronClaw Agent Feature was verified and validated by the comprehensive E2E test suite. IronClaw Agent Feature deployment depends on Coolify for container build and deployment orchestration. E2E test suite tests IronClaw Agent Feature for functionality and stability. IronClaw Agent Feature integrates with Datalens platform for agent-driven data analysis. LocalAgentClient is used by IronClaw Agent Feature to maintain session state across API calls. IronClaw Agent uses IronClawClient to connect and send messages to the remote IronClaw service. IronClaw Agent uses FindingsGenerator to generate findings from query results including handling PostgreSQL Decimal types. IronClaw Agent falls back to using LocalAgentClient when IronClaw environment variables are missing, resulting in local message processing and inability to complete queries successfully. IronClaw Agent requires IronClaw database to be configured and operational to persist sessions and support agent session creation. IronClaw Agent depends on QuestionRouter.route() to route queries and execute them correctly as part of the async processing pipeline. IronClaw Agent Findings Visualization uses the Live Backend to process and visualize data.

IronClaw agent frontend components implement the user interface for the IronClaw agent feature. IronClaw Agent frontend components form the user interface of the IronClaw agent feature.

IronClaw agent tables are physical tables mapped within the PostgreSQL database for persistent storage of agent sessions and related data. The Agent session data entity maps to the IronClaw agent tables in the database. The IronClaw agent feature depends on the IronClaw agent tables for storing session and message data.

IronClaw agent UI button was added to the analysis page frontend to provide navigation to the IronClaw agent feature. IronClaw navigation buttons are UI components part of the IronClaw agent feature. IronClaw Agent UI buttons code was added in commit e383323. IronClaw Agent UI buttons code was added in commit e383323. IronClaw Agent UI buttons code was added in commit e383323.

IronClaw backend endpoints expose API functionality required by the IronClaw agent feature. IronClaw Agent backend endpoints are integrated within the DataLens backend API services. IronClaw session creation endpoint is part of the IronClaw Gateway API that manages agent session lifecycle.

IronClaw Agent requires IronClaw database to be configured and operational to persist sessions and support agent session creation. IronClaw onboarding process initializes the IronClaw database to enable session storage and persistent agent threads. IronClaw service on elin requires IronClaw database for session persistence and agent thread management. IronClaw onboard process configures and initializes the IronClaw database to enable session and thread management.

The ironclaw (Docker) service is defined in docker-compose.yml.

IronClawClient integrates with IronClaw Gateway API hosted on external system elin for remote operation. IronClaw Gateway integration backend on elin calls Anthropic Claude model for processing queries and generating results. theo Backend communicates via HTTP with IronClaw Gateway at elin:9876 to manage agent sessions and relay user requests. IronClaw Gateway integrates with Claude to provide natural language clarification and execute analysis steps in Danish. IronClaw Gateway uses Anthropic Claude as the large language model backend to process user queries with improved speed and privacy. User interacts with IronClaw Gateway as part of the platform to ask Danish budget questions and receive clarified and executed results. IronClaw Gateway integrates with Claude, connecting users to LLM for clarifications and execution phases. IronClaw Gateway depends on OpenClaw Skill API on the agent server to run ringfenced executor skills safely. theo Backend depends on IronClaw Gateway running on elin server to manage agent sessions and route requests. IronClaw service includes the IronClaw Gateway component responsible for agent orchestration and Claude connectivity. IronClaw Gateway uses Ollama with the Qwen3-coder-next model as fallback for local inference when Anthropic Claude not available.

Connects theo backend to elin IronClaw service via HTTP for autonomous agent analysis. Uses REST protocols. Integrated after setting IRONCLAW environment variables, with deployment a pending step. IronClawClient integrates with the IronClaw Gateway API for sending and receiving agent messages. IronClaw session creation endpoint is part of the IronClaw Gateway API that manages agent session lifecycle.

IronClaw onboarding process initializes the IronClaw database to enable session storage and persistent agent threads. IronClaw onboard process configures and initializes the IronClaw database to enable session and thread management.

The Agent Gateway module in FastAPI acts as a bridge and uses IronClaw Service for agent session management and skill execution. IronClaw Service uses Ollama as one of the LLM providers for self-hosted private model inference. IronClaw Service uses Anthropic Claude as the cloud LLM provider backend option. IronClaw Service stores session memory and manages persistent agent memory within PostgreSQL tables. Agent Gateway depends on the IronClaw Service to handle reasoning loops, skill execution, and memory management via HTTP and WebSocket communication. IronClaw Service uses PostgreSQL to store session memory, agent tables, and persistent state. The theo backend server connects to the IronClaw service running on elin to delegate agent message processing via IronClaw Gateway API. IronClaw service on elin uses Anthropic Claude model for large language processing and SQL generation. IronClaw service on elin requires IronClaw database for session persistence and agent thread management. IronClaw service includes the IronClaw Gateway component responsible for agent orchestration and Claude connectivity.

Image demonstrating full testing scope and results of IronClaw features.

Graphic depicting IronClaw's data persistence or architecture.

IronClaw-powered Agent Mode provides capability for autonomous data analysis. The backend API agent code is part of the IronClaw-powered Agent Mode implementation. The backend main application includes the IronClaw-powered Agent Mode feature. The agent models are part of the IronClaw-powered Agent Mode subsystem. The agent skills service contributes to the IronClaw-powered Agent Mode functionality. Agent warming service is included in the IronClaw-powered Agent Mode implementation. The GDPR detector service is a component of the IronClaw-powered Agent Mode feature. The IronClaw client service supports the IronClaw-powered Agent Mode implementation. Visualization service is included as part of the IronClaw-powered Agent Mode feature. Database migration scripts add tables for the IronClaw-powered Agent Mode. The ChatPane component is part of the frontend agent interface for IronClaw-powered Agent Mode. The FindingCard UI component belongs to the IronClaw-powered Agent Mode frontend features. The FindingsBoard component is part of the frontend UI for IronClaw-powered Agent Mode. The GDPR warning UI component is integrated into the IronClaw-powered Agent Mode frontend. MessageBubble component is part of the IronClaw-powered Agent Mode frontend interface. ModelToggle frontend component supports user interaction within IronClaw-powered Agent Mode. The agent store Svelte module manages state for IronClaw-powered Agent Mode frontend. The frontend route for agent page implements part of IronClaw-powered Agent Mode UI. The backend/app/api/agent.py file is part of the IronClaw-powered Agent Mode implementation. The backend/app/services/agent_skills.py module is part of the IronClaw-powered Agent Mode feature. The backend/app/services/ironclaw_client.py is a component of the IronClaw-powered Agent Mode. The frontend/src/lib/components/agent/ChatPane.svelte is a UI component for the IronClaw-powered Agent Mode.

IronClaw Agent uses IronClawClient to connect and send messages to the remote IronClaw service. IronClawClient integrates with IronClaw Gateway API hosted on external system elin for remote operation. theo backend uses IronClawClient to connect to IronClaw Gateway for remote agent operations when IRONCLAW_MODE=remote environment variable is set. IronClawClient depends on OpenClawHttpClient for streaming communication with OpenClaw Gateway. TextToSQLService depends on IronClawClient integration for agent logic involving SQL generation. backend/app/services/ironclaw_client.py implements the IronClawClient used for remote agent communication via IronClaw Gateway API. IronClawClient integrates with the IronClaw Gateway API for sending and receiving agent messages. The Coolify backend is configured to use IronClawClient in remote mode with appropriate environment variables. The agent.py event_generator() function invokes get_ironclaw_client() to obtain the appropriate agent client for message processing. backend/app/services/ironclaw_client.py implements the IronClawClient used for remote agent communication via IronClaw Gateway API. IronClawClient integrates with the IronClaw Gateway API for sending and receiving agent messages. The Coolify backend is configured to use IronClawClient in remote mode with appropriate environment variables. The agent.py event_generator() function invokes get_ironclaw_client() to obtain the appropriate agent client for message processing. IronClawClient uses IronClawSkills definitions for agent skill execution via the IronClaw Gateway. OpenClawHttpClient serves as a WebSocket client alternative implementation to the HTTP-based IronClawClient for streaming agent responses.

IronClawClient.send_message() depends on SkillExecutor or corresponding executor to process messages and yield responses asynchronously.

Represents a message within the IronClaw conversation, managed through the ironclaw_client.py service class. _run_query generates findings streamed as 'insight' messages, each rendered as a separate IronClawMessage agent_skills.py process_message() yields IronClawMessage instances for communicating agent responses and errors.