{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ai4finance-foundation--finrobot","slug":"ai4finance-foundation--finrobot","name":"FinRobot","type":"agent","url":"https://finrobot.ai","page_url":"https://unfragile.ai/ai4finance-foundation--finrobot","categories":["ai-agents"],"tags":["aiagent","chatgpt","finance","fingpt","large-language-models","multimodal-deep-learning","prompt-engineering","robo-advisor"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ai4finance-foundation--finrobot__cap_0","uri":"capability://planning.reasoning.financial.chain.of.thought.reasoning.with.domain.specific.prompting","name":"financial chain-of-thought reasoning with domain-specific prompting","description":"Implements specialized chain-of-thought prompting optimized for financial analysis tasks, where LLMs decompose complex financial problems into structured reasoning steps using domain vocabulary and financial logic patterns. The system routes financial queries through a Brain Module that generates intermediate reasoning steps before producing final analytical conclusions, enabling more accurate financial decision-making than generic CoT approaches.","intents":["I need an AI agent to break down complex stock valuation problems into step-by-step financial reasoning","I want to generate structured financial analysis that shows its reasoning process for portfolio decisions","I need to ensure financial AI decisions are explainable and auditable through visible reasoning chains"],"best_for":["Financial analysts building AI-assisted research workflows","Fintech teams implementing explainable AI for compliance and audit trails","Quantitative researchers developing AI-driven trading strategies"],"limitations":["Financial CoT prompting adds latency per reasoning step compared to direct inference","Accuracy depends on quality of financial domain knowledge in underlying LLM","Requires careful prompt engineering for domain-specific financial terminology and logic"],"requires":["LLM API access (OpenAI, Anthropic, or compatible provider)","Financial domain knowledge for prompt design","Python 3.8+"],"input_types":["financial queries (text)","market data (structured)","financial documents (text)"],"output_types":["reasoning chains (structured text)","financial conclusions (text)","decision justifications (structured)"],"categories":["planning-reasoning","financial-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_1","uri":"capability://automation.workflow.multi.agent.task.orchestration.with.director.based.scheduling","name":"multi-agent task orchestration with director-based scheduling","description":"Implements a Smart Scheduler that coordinates multiple specialized financial agents through a Director Agent that assigns tasks based on agent performance metrics and capabilities. The system maintains an Agent Registry tracking agent availability and specializations, uses an Agent Adaptor to tailor agent functionalities to specific tasks, and routes work through a Task Manager that selects optimal LLM-based agents for different financial analysis types. This enables dynamic load balancing and agent selection without manual configuration.","intents":["I need multiple AI agents to collaborate on different financial analysis tasks simultaneously","I want the system to automatically route tasks to the best-suited agent based on performance history","I need to scale financial analysis across different domains (equity research, fixed income, derivatives) with specialized agents"],"best_for":["Large financial institutions running multi-domain analysis workflows","Fintech platforms needing to scale agent-based analysis across asset classes","Teams building production financial AI systems with multiple specialized agents"],"limitations":["Director Agent overhead adds ~100-200ms per task assignment decision","Agent performance metrics require historical data collection and may lag in dynamic markets","No built-in persistence for agent state — requires external state management for long-running workflows","Agent Adaptor complexity increases with number of specialized agents and task types"],"requires":["Python 3.8+","Multiple LLM API keys or local model endpoints","Agent performance monitoring infrastructure","Task queue system (Redis, RabbitMQ, or similar)"],"input_types":["financial analysis tasks (structured)","agent capability definitions (configuration)","performance metrics (structured data)"],"output_types":["task assignments (structured)","agent selection decisions (metadata)","analysis results from selected agents (variable)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_10","uri":"capability://automation.workflow.annual.report.generation.with.multi.source.financial.analysis","name":"annual report generation with multi-source financial analysis","description":"Implements an end-to-end use case that combines multiple FinRobot capabilities to automatically generate comprehensive annual reports. The system orchestrates agents to gather financial data from multiple sources, perform fundamental analysis, retrieve relevant SEC filings via RAG, generate narrative analysis, create visualizations, and compile results into a formatted annual report. This demonstrates the full Perception → Brain → Action workflow applied to a complex financial document generation task.","intents":["I need to automatically generate annual reports for portfolio companies using AI analysis","I want to create comprehensive financial analysis documents that cite SEC filings and market data","I need to produce annual reports at scale without manual analysis and formatting"],"best_for":["Private equity firms analyzing portfolio companies","Investment banks automating equity research reports","Financial advisory firms generating client annual reviews"],"limitations":["Report quality depends on quality of underlying financial data and agent analysis","Generating comprehensive reports requires orchestrating multiple agents and data sources, adding latency (5-30 seconds per report)","Requires careful prompt engineering to ensure report coherence and accuracy","Manual review still recommended for client-facing reports due to potential AI hallucinations"],"requires":["Python 3.8+","FinRobot framework with all data source integrations","LLM API access","Report template definitions","Financial data source API keys"],"input_types":["company ticker or identifier (text)","report parameters (date range, sections, format)","report template (Jinja2/HTML)"],"output_types":["formatted annual reports (PDF, HTML)","report data (JSON)","analysis artifacts (charts, tables)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_11","uri":"capability://planning.reasoning.market.forecasting.with.multi.agent.consensus","name":"market forecasting with multi-agent consensus","description":"Implements a use case where multiple specialized agents analyze market conditions from different perspectives (technical analysis, fundamental analysis, sentiment analysis, macroeconomic factors) and generate forecasts that are aggregated into a consensus prediction. The MultiAssistantWithLeader pattern coordinates agents, with a leader agent synthesizing individual forecasts into a final market outlook. This approach reduces individual agent bias and improves forecast robustness through ensemble reasoning.","intents":["I need multiple AI agents to analyze a stock from different angles and provide consensus forecast","I want to combine technical, fundamental, and sentiment analysis into a single market outlook","I need to reduce individual agent bias by aggregating multiple independent analyses"],"best_for":["Quantitative research teams building ensemble forecasting systems","Trading platforms generating multi-perspective market analysis","Financial advisory firms producing consensus market outlooks"],"limitations":["Multi-agent coordination adds 2-3x latency compared to single-agent forecasting","Consensus aggregation requires careful weighting of agent predictions","Forecast accuracy depends on quality of individual agent analyses","Requires managing disagreement between agents when forecasts diverge significantly"],"requires":["Python 3.8+","FinRobot framework with multi-agent support","Multiple LLM API keys or endpoints","Financial data sources for technical, fundamental, and sentiment analysis"],"input_types":["market symbols or assets (text)","analysis period (date range)","agent specializations (configuration)"],"output_types":["individual agent forecasts (structured)","consensus forecast (structured)","confidence metrics (numeric)","reasoning from each agent (text)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_12","uri":"capability://planning.reasoning.portfolio.optimization.with.constraint.aware.agent.reasoning","name":"portfolio optimization with constraint-aware agent reasoning","description":"Implements a use case where agents perform portfolio optimization by reasoning over investment constraints (risk tolerance, regulatory limits, ESG criteria, liquidity requirements) and generating optimized allocations. Agents use financial analysis to evaluate securities, apply constraints through structured reasoning, and generate portfolio recommendations with justifications. The system integrates with backtesting to validate optimized portfolios against historical performance.","intents":["I need an AI agent to optimize my portfolio subject to risk and regulatory constraints","I want to generate ESG-compliant portfolio allocations with AI-driven security selection","I need to validate optimized portfolios through backtesting before implementation"],"best_for":["Wealth management platforms automating portfolio construction","Robo-advisor systems generating personalized allocations","Institutional asset managers optimizing large portfolios with constraints"],"limitations":["Constraint satisfaction requires careful prompt engineering to ensure compliance","Portfolio optimization complexity increases with number of constraints and securities","Backtesting validation adds latency (10-60 seconds depending on portfolio size)","Market regime changes can invalidate historical optimization results"],"requires":["Python 3.8+","FinRobot framework with backtesting support","Optimization library (scipy, cvxpy, or similar) for constraint handling","Financial data for security analysis and historical backtesting"],"input_types":["investment constraints (configuration)","universe of investable securities (list)","risk parameters (numeric)","ESG criteria (configuration)"],"output_types":["optimized portfolio allocation (structured)","security recommendations (structured)","constraint satisfaction report (structured)","backtest results (metrics)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_13","uri":"capability://planning.reasoning.trading.strategy.development.with.iterative.refinement","name":"trading strategy development with iterative refinement","description":"Implements a use case where agents generate trading strategy ideas, backtest them against historical data, analyze backtest results, and iteratively refine strategies based on performance metrics. The system creates a feedback loop where agents learn from backtesting results and propose improvements (parameter tuning, rule modifications, risk controls). This enables continuous strategy improvement without manual intervention.","intents":["I need an AI agent to develop and refine trading strategies automatically","I want to iterate on strategy parameters based on backtest performance metrics","I need to generate trading strategies that improve over time through feedback loops"],"best_for":["Quantitative traders developing algorithmic strategies","Fintech platforms automating strategy development","Hedge funds using AI to generate and optimize trading ideas"],"limitations":["Iterative refinement can lead to overfitting on historical data","Strategy development latency increases with number of iterations and backtest periods","Requires careful validation to prevent strategies optimized for specific market regimes","No guarantee that refined strategies will perform well in live trading"],"requires":["Python 3.8+","FinRobot framework with backtesting support","Historical market data for backtesting","Strategy parameter optimization library"],"input_types":["strategy template or rules (code/configuration)","parameter ranges (numeric)","backtest period (date range)","performance targets (metrics)"],"output_types":["refined strategy definition (code/configuration)","parameter recommendations (numeric)","backtest results for each iteration (metrics)","strategy improvement analysis (text)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_2","uri":"capability://data.processing.analysis.multimodal.financial.data.perception.and.integration","name":"multimodal financial data perception and integration","description":"Implements a Perception Module that captures and interprets multimodal financial data from heterogeneous sources including market feeds, news streams, economic indicators, and alternative data sources. The system integrates data from multiple APIs (Finnhub, SEC filings, alternative data providers) and normalizes them into a unified representation that agents can reason over. This enables agents to make decisions based on comprehensive market context rather than single data sources.","intents":["I need to feed my AI agent real-time market data, news, and economic indicators simultaneously","I want to integrate SEC filings, earnings transcripts, and alternative data into a single financial analysis context","I need to normalize data from multiple financial APIs into a consistent format for agent consumption"],"best_for":["Quantitative research teams building comprehensive market analysis systems","Fintech platforms aggregating multiple data sources for AI-driven insights","Financial institutions needing unified data pipelines for agent-based analysis"],"limitations":["Data integration latency varies by source (real-time feeds vs batch SEC filings)","API rate limits from multiple providers require careful request batching and caching","Data quality inconsistencies across sources require normalization and validation logic","Multimodal context can exceed LLM token limits for comprehensive analysis"],"requires":["API keys for Finnhub, SEC Edgar, and alternative data providers","Python 3.8+","Data normalization/ETL framework (pandas, Polars, or similar)","Caching layer for API responses (Redis recommended)"],"input_types":["market data feeds (real-time, structured)","news streams (text)","SEC filings (HTML/text)","economic indicators (structured)","alternative data (variable formats)"],"output_types":["unified financial context (structured)","normalized market data (JSON/structured)","integrated analysis context (text/structured)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_3","uri":"capability://memory.knowledge.retrieval.augmented.generation.for.financial.document.analysis","name":"retrieval-augmented generation for financial document analysis","description":"Implements RAG integration that enables agents to retrieve and reason over financial documents (SEC filings, earnings transcripts, annual reports) without loading entire documents into LLM context. The system indexes financial documents into a vector store, performs semantic search to retrieve relevant passages, and augments agent prompts with retrieved context. This enables agents to cite specific sources and maintain accuracy when analyzing large financial documents that exceed token limits.","intents":["I need my AI agent to analyze 10-K filings and cite specific sections in its analysis","I want to retrieve relevant passages from earnings transcripts without loading the entire transcript","I need to build a financial document Q&A system that cites sources and maintains accuracy"],"best_for":["Equity research teams analyzing SEC filings and earnings calls","Compliance teams building document-aware AI systems with audit trails","Financial institutions implementing source-cited AI analysis for client reports"],"limitations":["Vector search retrieval quality depends on embedding model and document chunking strategy","Semantic search may miss relevant passages with different terminology than query","Requires maintaining vector index and managing document updates/versioning","Retrieved context still consumes LLM tokens, limiting total document size analyzable per query"],"requires":["Vector database (Pinecone, Weaviate, Milvus, or similar)","Embedding model (OpenAI, Sentence Transformers, or similar)","Document parser for SEC filings/financial documents","Python 3.8+"],"input_types":["financial documents (PDF, HTML, text)","user queries (text)","document metadata (structured)"],"output_types":["retrieved passages (text with source citations)","augmented prompts (text)","analysis with source references (structured)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_4","uri":"capability://automation.workflow.single.agent.and.multi.agent.workflow.templates","name":"single-agent and multi-agent workflow templates","description":"Provides pre-configured agent workflow templates (SingleAssistant, SingleAssistantRAG, SingleAssistantShadow, MultiAssistant, MultiAssistantWithLeader) that encapsulate common financial analysis patterns. Each template implements the three-phase workflow (Perception → Brain → Action) with different configurations: single agents for focused tasks, RAG-enhanced agents for document analysis, shadow-thinking agents for reasoning verification, and multi-agent systems with leader coordination. Templates reduce boilerplate and enable rapid deployment of financial analysis workflows.","intents":["I want to quickly deploy a single AI agent for stock analysis without building from scratch","I need a multi-agent system where one agent coordinates others for complex financial analysis","I want an agent that can verify its own reasoning through shadow-thinking before producing output"],"best_for":["Developers prototyping financial AI applications quickly","Teams deploying standardized financial analysis workflows across multiple use cases","Organizations needing pre-validated agent configurations for compliance and consistency"],"limitations":["Templates are opinionated and may require customization for domain-specific workflows","Shadow-thinking template adds 2x latency due to parallel reasoning verification","MultiAssistantWithLeader adds coordination overhead compared to single-agent workflows","Limited flexibility for non-standard financial analysis patterns outside template scope"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible)","FinRobot framework installed","Financial data source API keys (Finnhub, etc.)"],"input_types":["financial analysis tasks (text/structured)","agent configuration (YAML/JSON)","financial data (structured)"],"output_types":["analysis results (text/structured)","agent reasoning traces (structured)","multi-agent coordination logs (structured)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_5","uri":"capability://text.generation.language.financial.report.generation.with.structured.output","name":"financial report generation with structured output","description":"Implements an Action Module capability that translates agent analytical insights into formatted financial reports (annual reports, market analysis summaries, portfolio reviews). The system generates structured output with sections, tables, and visualizations based on agent reasoning. Reports can be exported to multiple formats (PDF, HTML, Markdown) and include agent-generated explanations, data tables, and charts. This bridges the gap between agent analysis and human-readable financial documents.","intents":["I need my AI agent to generate professional annual reports from financial analysis","I want to create market analysis summaries that include both text and charts from agent insights","I need to export agent-generated portfolio reviews in PDF format for client distribution"],"best_for":["Wealth management firms automating client report generation","Equity research teams producing AI-assisted research reports","Financial advisory platforms generating personalized analysis documents"],"limitations":["Report quality depends on agent analysis quality — garbage in, garbage out","Chart generation requires additional visualization library integration","PDF generation adds latency and requires external dependencies (wkhtmltopdf, etc.)","Formatting customization requires template management and maintenance"],"requires":["Python 3.8+","Report template engine (Jinja2 or similar)","PDF generation library (ReportLab, wkhtmltopdf, or similar)","Visualization library (Matplotlib, Plotly, or similar)"],"input_types":["agent analysis results (structured)","financial data (structured)","report templates (Jinja2/HTML)"],"output_types":["formatted reports (PDF, HTML, Markdown)","structured report data (JSON)","report sections (text with tables/charts)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_6","uri":"capability://image.visual.chart.and.visualization.generation.from.financial.analysis","name":"chart and visualization generation from financial analysis","description":"Implements a Chart Generation capability within the Action Module that converts agent-generated financial insights into visualizations (price charts, portfolio allocations, performance comparisons, trend analysis). The system maps analytical conclusions to appropriate chart types (line charts for trends, pie charts for allocations, bar charts for comparisons) and generates interactive or static visualizations. Charts are embedded in reports and can be exported independently for presentations or dashboards.","intents":["I need my AI agent to generate price charts and technical analysis visualizations automatically","I want to create portfolio allocation pie charts from agent portfolio analysis","I need to visualize performance comparisons and trend analysis from agent insights"],"best_for":["Financial dashboards and reporting platforms","Wealth management systems generating visual client reports","Trading platforms visualizing AI-generated technical analysis"],"limitations":["Chart type selection requires heuristics or explicit mapping from agent output","Interactive charts (Plotly) add client-side dependencies and latency","Static charts (Matplotlib) have limited interactivity for exploration","Financial data normalization required for consistent chart scaling and formatting"],"requires":["Python 3.8+","Visualization library (Matplotlib, Plotly, or similar)","Financial data in normalized format","Chart template definitions or heuristics"],"input_types":["agent analysis results (structured)","financial time series (structured)","chart specifications (configuration)"],"output_types":["static charts (PNG, SVG)","interactive charts (HTML/JSON)","chart data (JSON)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_7","uri":"capability://data.processing.analysis.backtesting.system.for.trading.strategy.validation","name":"backtesting system for trading strategy validation","description":"Implements a Backtesting System that validates AI-generated trading strategies against historical market data. The system simulates strategy execution over historical periods, calculates performance metrics (returns, Sharpe ratio, drawdown, win rate), and generates backtest reports. Agents can use backtesting results to refine strategies or validate assumptions before live deployment. The system integrates with market data APIs to fetch historical OHLCV data and supports multiple strategy types (long/short, options, etc.).","intents":["I need to validate my AI agent's trading strategy against historical data before deploying","I want to compare performance of multiple AI-generated strategies using backtesting","I need to generate backtest reports showing strategy performance metrics and drawdowns"],"best_for":["Quantitative traders developing AI-driven trading strategies","Fintech platforms validating algorithmic trading strategies","Hedge funds using AI agents to generate and test trading ideas"],"limitations":["Backtesting results don't guarantee live performance due to market regime changes and slippage","Historical data quality issues (gaps, splits, dividends) can skew backtest results","Backtesting latency increases with strategy complexity and historical period length","Survivorship bias in historical data can overstate strategy performance","No built-in transaction cost modeling — requires manual configuration"],"requires":["Python 3.8+","Historical market data (OHLCV)","Backtesting library (Backtrader, VectorBT, or similar)","Market data API access (Finnhub, Alpha Vantage, etc.)"],"input_types":["trading strategy definition (code/configuration)","historical market data (OHLCV)","strategy parameters (configuration)"],"output_types":["backtest results (structured metrics)","performance reports (text/JSON)","equity curves (time series)","trade logs (structured)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_8","uri":"capability://tool.use.integration.plug.and.play.multi.provider.llm.integration","name":"plug-and-play multi-provider llm integration","description":"Implements a Multi-source LLM Foundation Models Layer that abstracts LLM provider differences and enables plug-and-play switching between OpenAI, Anthropic, local models, and other LLM providers. The system maintains a unified LLM interface that agents use regardless of underlying provider, handles API key management, manages rate limits and retries, and supports model-specific features (function calling, vision, etc.). This enables agents to use the best model for each task without code changes.","intents":["I want to switch between OpenAI and Anthropic models without changing agent code","I need to use local LLMs for sensitive financial data without cloud API calls","I want to route different financial analysis tasks to different LLM providers based on cost/performance"],"best_for":["Organizations with multi-cloud or hybrid LLM strategies","Teams needing cost optimization across multiple LLM providers","Financial institutions with data residency requirements preventing cloud LLM use"],"limitations":["Abstraction layer adds ~50-100ms latency per LLM call due to provider routing","Model-specific features (vision, function calling) require provider-specific code paths","Rate limiting and quota management complexity increases with multiple providers","Cost tracking and billing integration required for multi-provider scenarios"],"requires":["Python 3.8+","API keys for desired LLM providers (OpenAI, Anthropic, etc.)","Local LLM setup (Ollama, vLLM, or similar) for on-premise models","LLM abstraction library (LangChain, LiteLLM, or similar)"],"input_types":["prompts (text)","model configuration (JSON/YAML)","provider credentials (environment variables)"],"output_types":["LLM responses (text)","token usage metrics (structured)","provider metadata (structured)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai4finance-foundation--finrobot__cap_9","uri":"capability://data.processing.analysis.financial.data.source.api.integration.and.normalization","name":"financial data source api integration and normalization","description":"Implements integrations with multiple financial data providers (Finnhub, SEC Edgar, Alpha Vantage, alternative data sources) through a unified DataOps layer that normalizes heterogeneous API responses into consistent data structures. The system handles provider-specific authentication, rate limiting, data format differences, and missing data scenarios. Agents access financial data through a unified interface regardless of underlying provider, enabling seamless data source switching and multi-source aggregation.","intents":["I need my agent to fetch real-time stock prices from Finnhub and SEC filings from Edgar without handling API differences","I want to aggregate market data from multiple providers and normalize it into a single format","I need to handle API rate limits and retries transparently when fetching financial data"],"best_for":["Financial platforms aggregating data from multiple sources","Fintech applications needing unified financial data access","Quantitative research teams working with diverse data providers"],"limitations":["Data freshness varies by provider (real-time vs delayed)","API rate limits require request batching and caching strategies","Data quality inconsistencies across providers require validation and reconciliation","Provider API changes require maintenance and version management","Cost varies significantly by provider and data type"],"requires":["Python 3.8+","API keys for financial data providers (Finnhub, SEC Edgar, etc.)","Data normalization framework (pandas, Polars, or similar)","Caching layer (Redis recommended)"],"input_types":["data requests (ticker symbols, date ranges, query parameters)","provider configuration (API keys, endpoints)","data specifications (fields, formats)"],"output_types":["normalized financial data (JSON/structured)","time series data (OHLCV, fundamentals)","document data (SEC filings, news)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["LLM API access (OpenAI, Anthropic, or compatible provider)","Financial domain knowledge for prompt design","Python 3.8+","Multiple LLM API keys or local model endpoints","Agent performance monitoring infrastructure","Task queue system (Redis, RabbitMQ, or similar)","FinRobot framework with all data source integrations","LLM API access","Report template definitions","Financial data source API keys"],"failure_modes":["Financial CoT prompting adds latency per reasoning step compared to direct inference","Accuracy depends on quality of financial domain knowledge in underlying LLM","Requires careful prompt engineering for domain-specific financial terminology and logic","Director Agent overhead adds ~100-200ms per task assignment decision","Agent performance metrics require historical data collection and may lag in dynamic markets","No built-in persistence for agent state — requires external state management for long-running workflows","Agent Adaptor complexity increases with number of specialized agents and task types","Report quality depends on quality of underlying financial data and agent analysis","Generating comprehensive reports requires orchestrating multiple agents and data sources, adding latency (5-30 seconds per report)","Requires careful prompt engineering to ensure report coherence and accuracy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6370321689177483,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-04-22T08:04:41.641Z","last_commit":"2026-04-03T13:27:45Z"},"community":{"stars":6750,"forks":1130,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai4finance-foundation--finrobot","compare_url":"https://unfragile.ai/compare?artifact=ai4finance-foundation--finrobot"}},"signature":"o0KRelKbAxZtqHGiIuljUbQlANsQVrMWZOH76ff9+QdkZxQLIXqbNdpWjkg+tJNiUHcbkXDUsHcjtihrIFglBA==","signedAt":"2026-06-20T12:54:31.345Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai4finance-foundation--finrobot","artifact":"https://unfragile.ai/ai4finance-foundation--finrobot","verify":"https://unfragile.ai/api/v1/verify?slug=ai4finance-foundation--finrobot","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"}}