{ "agents": [ { "name": "FundamentalAnalystAgent", "type": "Agent", "docstring": "Agent for performing fundamental analysis of companies.\n\nThis agent analyzes financial statements, calculates key financial ratios,\nperforms valuation modeling (DCF and comparables), and assesses financial health.\nIt relies on DataRetrievalAgent for fetching company data via A2A communication.\n\nUpdated to support standard AgentInput/AgentOutput interface.", "file": "./core/agents/fundamental_analyst_agent.py", "lineno": 58, "verification_script": null }, { "name": "MarketSentimentAgent", "type": "Agent", "docstring": "Agent responsible for gauging market sentiment from a variety of sources,\nsuch as news articles, social media, and prediction markets.", "file": "./core/agents/market_sentiment_agent.py", "lineno": 14, "verification_script": null }, { "name": "QueryUnderstandingAgent", "type": "Agent", "docstring": "An agent responsible for understanding the user's query and\ndetermining which other agents are relevant to answer it.\nThis version incorporates LLM-based intent recognition and skill-based routing.", "file": "./core/agents/query_understanding_agent.py", "lineno": 16, "verification_script": null }, { "name": "QuantumMonteCarloAgent", "type": "Agent", "docstring": "Orchestrates Quantum-Accelerated Monte Carlo simulations.", "file": "./core/agents/quantum_monte_carlo_agent.py", "lineno": 6, "verification_script": null }, { "name": "ArchiveManagerAgent", "type": "Agent", "docstring": null, "file": "./core/agents/archive_manager_agent.py", "lineno": 8, "verification_script": "./verification/verify_archive.py" }, { "name": "EventDrivenRiskAgent", "type": "Agent", "docstring": "Agent that tracks and assesses the market impact of events.", "file": "./core/agents/event_driven_risk_agent.py", "lineno": 20, "verification_script": null }, { "name": "GeopoliticalRiskAgent", "type": "Agent", "docstring": null, "file": "./core/agents/geopolitical_risk_agent.py", "lineno": 6, "verification_script": null }, { "name": "RAGAgent", "type": "Agent", "docstring": "An agent that implements a Retrieval-Augmented Generation (RAG) pipeline.\nIt can ingest documents and answer queries based on the ingested content.", "file": "./core/agents/rag_agent.py", "lineno": 19, "verification_script": null }, { "name": "ReportGeneratorAgent", "type": "Agent", "docstring": "An agent responsible for generating final reports by synthesizing\nanalysis from other agents.", "file": "./core/agents/report_generator_agent.py", "lineno": 10, "verification_script": null }, { "name": "AlgoTradingAgent", "type": "Agent", "docstring": null, "file": "./core/agents/algo_trading_agent.py", "lineno": 9, "verification_script": null }, { "name": "ReflectorAgent", "type": "Agent", "docstring": "The Reflector Agent performs meta-cognition.\nIt analyzes the output of other agents or the system's own reasoning traces\nto identify logical fallacies, hallucination risks, or missing context.\n\nv23 Update: Wraps `ReflectorGraph` for iterative self-correction.", "file": "./core/agents/reflector_agent.py", "lineno": 18, "verification_script": null }, { "name": "RegulatoryComplianceAgent", "type": "Agent", "docstring": "Ensures adherence to all applicable financial regulations and compliance standards.\n\nCore Capabilities:\n- Monitors regulatory changes and trends across relevant jurisdictions.\n- Analyzes financial transactions and activities for compliance.\n- Identifies potential regulatory risks and provides mitigation strategies.\n- Generates compliance reports and audit trails.\n- Collaborates with other agents to incorporate compliance considerations.\n- Provides guidance on interacting with regulatory bodies.\n- Adapts to changing political landscapes and regulatory priorities.", "file": "./core/agents/regulatory_compliance_agent.py", "lineno": 26, "verification_script": null }, { "name": "CryptoAgent", "type": "Agent", "docstring": null, "file": "./core/agents/crypto_agent.py", "lineno": 32, "verification_script": null }, { "name": "MetaCognitiveAgent", "type": "Agent", "docstring": "The Meta-Cognitive Agent monitors the performance of other agents.", "file": "./core/agents/meta_cognitive_agent.py", "lineno": 6, "verification_script": null }, { "name": "KnowledgeContributionAgent", "type": "Agent", "docstring": "An agent that extracts key findings from a report and formats them as structured data.", "file": "./core/agents/knowledge_contribution_agent.py", "lineno": 9, "verification_script": null }, { "name": "ProfileAgent", "type": "Agent", "docstring": "ProfileAgent serves as the high-level interface for user-driven commands\nwithin the Adam ecosystem. It routes 'adam.*' commands to the appropriate\nsubsystems, including Industry Specialists, Developer Swarm, and the\nAutonomous Improvement Loop.", "file": "./core/agents/profile_agent.py", "lineno": 5, "verification_script": null }, { "name": "DataVerificationAgent", "type": "Agent", "docstring": null, "file": "./core/agents/data_verification_agent.py", "lineno": 7, "verification_script": null }, { "name": "PortfolioOptimizationAgent", "type": "Agent", "docstring": "Agent for portfolio optimization using both Classical (Mean-Variance) and AI (LSTM) approaches.", "file": "./core/agents/portfolio_optimization_agent.py", "lineno": 41, "verification_script": null }, { "name": "AIPoweredPortfolioOptimizationAgent", "type": "Agent", "docstring": "Legacy wrapper for PortfolioOptimizationAgent to maintain backward compatibility.", "file": "./core/agents/portfolio_optimization_agent.py", "lineno": 236, "verification_script": null }, { "name": "AgentBase", "type": "Agent", "docstring": "Abstract base class for all agents in the system.\nDefines the common interface and behavior expected of all agents.\nThis version incorporates MCP, A2A, Semantic Kernel, HNASP, Memory persistence, and Boot Protocol.", "file": "./core/agents/agent_base.py", "lineno": 38, "verification_script": null }, { "name": "StrategicForesightAgent", "type": "Agent", "docstring": "Strategic Foresight Agent\n\nA unified intelligence unit acting as the system's \"Pre-Crime\" and National Security division.\nIt integrates high-fidelity financial modeling (OSWM, Quantum, Black Swan) with \ngeopolitical strategic analysis.", "file": "./core/agents/strategic_foresight_agent.py", "lineno": 148, "verification_script": null }, { "name": "AgentBase", "type": "Agent", "docstring": null, "file": "./core/agents/strategic_foresight_agent.py", "lineno": 28, "verification_script": null }, { "name": "AdaptiveAlgoTradingAgent", "type": "Agent", "docstring": "An extension of AlgoTradingAgent that uses Reinforcement Learning (Q-Learning)\nto dynamically select the best trading strategy based on market conditions.", "file": "./core/agents/adaptive_algo_agent.py", "lineno": 7, "verification_script": null }, { "name": "Meta19Agent", "type": "Agent", "docstring": "Monitors the reasoning and outputs of other agents to ensure logical consistency,\ncoherence, and alignment with core principles. Deprecated as part of Adam v19 to v22.", "file": "./core/agents/meta_19_agent.py", "lineno": 8, "verification_script": null }, { "name": "LexicaAgent", "type": "Agent", "docstring": null, "file": "./core/agents/lexica_agent.py", "lineno": 7, "verification_script": null }, { "name": "HNASPAgent", "type": "Agent", "docstring": "An agent that implements the Hybrid Neurosymbolic Agent State Protocol (HNASP).", "file": "./core/agents/hnasp_agent.py", "lineno": 42, "verification_script": null }, { "name": "SupplyChainRiskAgent", "type": "Agent", "docstring": null, "file": "./core/agents/supply_chain_risk_agent.py", "lineno": 12, "verification_script": null }, { "name": "RiskAssessmentAgent", "type": "Agent", "docstring": "Agent responsible for assessing various types of investment risks,\nsuch as market risk, credit risk, and operational risk.\n\nPhilosophy:\nRisk is not a number; it's a distribution. We strive to quantify the tails.", "file": "./core/agents/risk_assessment_agent.py", "lineno": 20, "verification_script": null }, { "name": "RedTeamAgent", "type": "Agent", "docstring": "The Red Team Agent acts as an internal adversary to the system.\n\n### Functionality:\nIt generates novel and challenging scenarios (stress tests) to validate risk models before\nstrategies are deployed. This is a critical component of the \"Sovereign Financial Intelligence\"\narchitecture (v23.5), ensuring that the system is robust against \"Black Swan\" events.\n\n### Architecture:\nIn v23.5, this agent implements an internal **Adversarial Self-Correction Loop** using LangGraph.\nInstead of a single-shot generation, it iteratively refines its attack scenarios until they\nmeet a severity threshold.\n\n### Workflow:\n1. **Generate Attack**: Uses `CounterfactualReasoningSkill` to invert assumptions in a credit memo.\n2. **Simulate Impact**: Estimates the financial damage (e.g., VaR spike) of the scenario.\n3. **Critique**: Checks if the scenario is severe enough (Severity > Threshold).\n4. **Escalate**: If too mild, it loops back to Generate Attack with instructions to \"Escalate\".", "file": "./core/agents/red_team_agent.py", "lineno": 15, "verification_script": null }, { "name": "IndustrySpecialistAgent", "type": "Agent", "docstring": null, "file": "./core/agents/industry_specialist_agent.py", "lineno": 7, "verification_script": null }, { "name": "BehavioralEconomicsAgent", "type": "Agent", "docstring": "Analyzes market data and user interactions for signs of cognitive biases and irrational behavior.", "file": "./core/agents/behavioral_economics_agent.py", "lineno": 8, "verification_script": null }, { "name": "MarketMakingAgent", "type": "Agent", "docstring": "Agentic wrapper around the Avellaneda-Stoikov model.\nImplements a 'System Brain' component that dynamically adapts risk aversion (Gamma)\nbased on market conditions (Volatility, Inventory Risk), simulating an RL policy.", "file": "./core/agents/market_making_agent.py", "lineno": 6, "verification_script": null }, { "name": "AlternativeDataAgent", "type": "Agent", "docstring": null, "file": "./core/agents/alternative_data_agent.py", "lineno": 19, "verification_script": null }, { "name": "FraudDetectionAgent", "type": "Agent", "docstring": "A specialized agent for detecting financial anomalies and simulating restatements.", "file": "./core/agents/fraud_detection_agent.py", "lineno": 7, "verification_script": null }, { "name": "DiscussionChairAgent", "type": "Agent", "docstring": null, "file": "./core/agents/discussion_chair_agent.py", "lineno": 7, "verification_script": null }, { "name": "DiscussionChairAgent", "type": "Agent", "docstring": null, "file": "./core/agents/discussion_chair_agent.py", "lineno": 174, "verification_script": null }, { "name": "DiscussionChairAgent", "type": "Agent", "docstring": null, "file": "./core/agents/discussion_chair_agent.py", "lineno": 322, "verification_script": null }, { "name": "AgentBase", "type": "Agent", "docstring": null, "file": "./core/agents/news_bot.py", "lineno": 52, "verification_script": null }, { "name": "FinancialModelingAgent", "type": "Agent", "docstring": "Agent for performing comprehensive financial modeling, including DCF valuation, sensitivity analysis,\nstress testing, Monte Carlo simulations, and ratio analysis.", "file": "./core/agents/financial_modeling_agent.py", "lineno": 14, "verification_script": null }, { "name": "CatalystAgent", "type": "Agent", "docstring": null, "file": "./core/agents/catalyst_agent.py", "lineno": 9, "verification_script": null }, { "name": "DataVisualizationAgent", "type": "Agent", "docstring": null, "file": "./core/agents/data_visualization_agent.py", "lineno": 8, "verification_script": null }, { "name": "MachineLearningModelTrainingAgent", "type": "Agent", "docstring": null, "file": "./core/agents/machine_learning_model_training_agent.py", "lineno": 9, "verification_script": null }, { "name": "ResultAggregationAgent", "type": "Agent", "docstring": "Combines results from multiple agents. Initially uses simple concatenation,\nbut is designed for future LLM integration.", "file": "./core/agents/result_aggregation_agent.py", "lineno": 12, "verification_script": null }, { "name": "EchoAgent", "type": "Agent", "docstring": null, "file": "./core/agents/echo_agent.py", "lineno": 10, "verification_script": null }, { "name": "AdaptiveAgent", "type": "Agent", "docstring": "An agent implementation that fully embodies the 'Protocol Paradox' resolutions:\n1. Adaptive Conviction (Switching between Direct/MCP)\n2. State Anchors (Async Drift protection)\n3. Tool RAG (Context Saturation mitigation)", "file": "./core/agents/adaptive_agent.py", "lineno": 6, "verification_script": null }, { "name": "SNCAnalystAgent", "type": "Agent", "docstring": "Agent for performing Shared National Credit (SNC) analysis.\nThis agent analyzes company data based on regulatory guidelines to assign an SNC rating.\nIt retrieves data via A2A communication with DataRetrievalAgent and can use SK skills.", "file": "./core/agents/snc_analyst_agent.py", "lineno": 36, "verification_script": null }, { "name": "TechnicalAnalystAgent", "type": "Agent", "docstring": "Agent responsible for technical analysis of financial assets.", "file": "./core/agents/technical_analyst_agent.py", "lineno": 16, "verification_script": null }, { "name": "LegalAgent", "type": "Agent", "docstring": null, "file": "./core/agents/legal_agent.py", "lineno": 6, "verification_script": null }, { "name": "PromptGenerationAgent", "type": "Agent", "docstring": "An agent that generates a high-quality prompt from a user query.", "file": "./core/agents/prompt_generation_agent.py", "lineno": 7, "verification_script": null }, { "name": "QuantitativeRiskAgent", "type": "Agent", "docstring": "Agent responsible for calculating quantitative risk metrics such as Value at Risk (VaR)\nand Conditional Value at Risk (CVaR).", "file": "./core/agents/quantitative_risk_agent.py", "lineno": 16, "verification_script": null }, { "name": "BlackSwanAgent", "type": "Agent", "docstring": "Counterfactual 'Black Swan' Engine.\nAutonomously generates stress scenarios and calculates 'Probability of Default' sensitivity.", "file": "./core/agents/black_swan_agent.py", "lineno": 25, "verification_script": null }, { "name": "MacroeconomicAnalysisAgent", "type": "Agent", "docstring": "Agent responsible for analyzing macroeconomic indicators (GDP, Inflation, etc.)\nto provide a broad market context.\n\nRefactored for v23 Architecture (Path A).", "file": "./core/agents/macroeconomic_analysis_agent.py", "lineno": 10, "verification_script": "./verification/verify_macro.py" }, { "name": "NaturalLanguageGenerationAgent", "type": "Agent", "docstring": null, "file": "./core/agents/natural_language_generation_agent.py", "lineno": 6, "verification_script": null }, { "name": "AgentForge", "type": "Agent", "docstring": "The AgentForge is responsible for the dynamic creation of new agents.\nIt uses templates and configuration to generate agent code and add them\nto the system at runtime. This version incorporates advanced features\nlike skill schema generation and A2A wiring.", "file": "./core/agents/agent_forge.py", "lineno": 14, "verification_script": null }, { "name": "DataRetrievalAgent", "type": "Agent", "docstring": "Agent responsible for retrieving data from various configured sources.\nNow integrates with DataFetcher for live market data.", "file": "./core/agents/data_retrieval_agent.py", "lineno": 19, "verification_script": null }, { "name": "AnomalyDetectionAgent", "type": "Agent", "docstring": "Detects anomalies and unusual patterns in financial markets and company data.\n\nCore Capabilities:\n- Leverages various statistical methods and machine learning algorithms for comprehensive anomaly detection.\n- Integrates with Adam's knowledge base for context-aware analysis.\n- Employs XAI techniques to provide explanations for detected anomalies.\n- Collaborates with other agents for in-depth investigation and reporting.\n\nAgent Network Interactions:\n- DataRetrievalAgent: Accesses market and company data from the knowledge graph.\n- FundamentalAnalystAgent: Receives alerts for potential anomalies in financial statements.\n- RiskAssessmentAgent: Provides risk scores and context for detected anomalies.\n- AlertGenerationAgent: Generates alerts for significant anomalies.\n\nDynamic Adaptation and Evolution:\n- Continuously learns and adapts based on feedback and new data.\n- Automated testing and monitoring ensure accuracy and reliability.", "file": "./core/agents/anomaly_detection_agent.py", "lineno": 23, "verification_script": null }, { "name": "PredictionMarketAgent", "type": "Agent", "docstring": null, "file": "./core/agents/prediction_market_agent.py", "lineno": 19, "verification_script": null }, { "name": "CyclicalReasoningAgent", "type": "Agent", "docstring": "An agent capable of cyclical reasoning, routing its output back to itself\nor other agents for iterative improvement.", "file": "./core/agents/cyclical_reasoning_agent.py", "lineno": 12, "verification_script": null }, { "name": "ImpactAnalysisAgent", "type": "Agent", "docstring": "Analyzes cross-sector correlations and systemic risks.", "file": "./core/agents/analytics/impact_analysis_agent.py", "lineno": 4, "verification_script": null }, { "name": "ArchitectAgent", "type": "Agent", "docstring": "The Architect Agent is responsible for maintaining, optimizing, and evolving\nthe system infrastructure and reasoning logic.", "file": "./core/agents/architect_agent/agent.py", "lineno": 5, "verification_script": null }, { "name": "ManagementAssessmentAgent", "type": "Agent", "docstring": "Phase 1: Entity & Management Assessment.\nAnalyzes capital allocation, insider alignment, and CEO tone.", "file": "./core/agents/specialized/management_assessment_agent.py", "lineno": 9, "verification_script": null }, { "name": "OptimizedGalleryAgent", "type": "Agent", "docstring": "Demonstration agent that uses RedundancyOptimizationMixin to securely and efficiently\nfetch 'gallery data' (simulated) with fallback capabilities.", "file": "./core/agents/specialized/optimized_gallery_agent.py", "lineno": 8, "verification_script": "./verification/verify_gallery.py" }, { "name": "RegulatorySNCAgent", "type": "Agent", "docstring": "Specialized Agent: The Regulator (Government Employee Persona).\n\nThis agent strictly applies the \"Interagency Guidance on Leveraged Lending\" (2013).\nIt does NOT use flexible cash flow models or future projections.\nIt focuses on rigid compliance: Leverage < 6x, Ability to Repay < 50% of Free Cash Flow.\n\nRole: \"The Brake\"", "file": "./core/agents/specialized/regulatory_snc_agent.py", "lineno": 11, "verification_script": null }, { "name": "CreditSentryAgent", "type": "Agent", "docstring": "\"The Hawk\" - Solvency Assessment Engine.\nResponsibility: Stress testing, FCCR calculation, Cycle Detection (Fractured Ouroboros), J.Crew Detection.", "file": "./core/agents/specialized/credit_sentry_agent.py", "lineno": 9, "verification_script": null }, { "name": "MarketRegimeAgent", "type": "Agent", "docstring": "Agent responsible for classifying the current market regime (e.g., Bull, Bear, Choppy, Volatile)\nusing statistical metrics such as Hurst Exponent, ADX, and Volatility ratios.\nThis acts as a 'Force Multiplier' for other agents by providing context on *how* to trade.", "file": "./core/agents/specialized/market_regime_agent.py", "lineno": 11, "verification_script": null }, { "name": "DeepSectorAnalyst", "type": "Agent", "docstring": "A Deep Vertical Agent specialized in generating detailed sector-specific\nstress scenarios using the Generative Risk Engine.", "file": "./core/agents/specialized/deep_sector_analyst.py", "lineno": 10, "verification_script": null }, { "name": "SovereignAIAnalystAgent", "type": "Agent", "docstring": "Agent for analyzing the 'Sovereign AI' landscape.\nIt focuses on the intersection of National Security, AI Infrastructure (Capex),\nand Geopolitical fragmentation.", "file": "./core/agents/specialized/sovereign_ai_analyst_agent.py", "lineno": 6, "verification_script": null }, { "name": "CovenantAnalystAgent", "type": "Agent", "docstring": "Phase 3 Helper: Covenant Analysis.\nParses credit agreements (or simulates them) for maintenance covenants.\n\nEnhanced Capabilities:\n- Technical Default Prediction (Headroom Compression)\n- Springing Covenant Monitoring (Revolver Utilization)", "file": "./core/agents/specialized/financial_covenant_agent.py", "lineno": 9, "verification_script": null }, { "name": "CryptoArbitrageAgent", "type": "Agent", "docstring": "A specialized agent that monitors cryptocurrency prices across multiple exchanges\nto identify arbitrage opportunities.", "file": "./core/agents/specialized/crypto_arbitrage_agent.py", "lineno": 33, "verification_script": null }, { "name": "StrategicSNCAgent", "type": "Agent", "docstring": "Specialized Agent for performing Shared National Credit (SNC) simulations.\n\nActs as a virtual 'Senior Credit Officer', orchestrating the debate between:\n1. The Regulator (RegulatorySNCAgent) - \"The Brake\"\n2. The Strategist (Internal Logic) - \"The Gas\"\n\nIt uses the Risk Consensus Engine to simulate a dialogue and determine the final outcome.", "file": "./core/agents/specialized/strategic_snc_agent.py", "lineno": 14, "verification_script": null }, { "name": "CreditConformanceAgent", "type": "Agent", "docstring": "Tier-2 Generative AI Agent for Credit Risk Conformance.\nImplements a multi-layered architecture for regulatory and policy conformance.", "file": "./core/agents/specialized/credit_conformance_agent.py", "lineno": 19, "verification_script": null }, { "name": "SNCRatingAgent", "type": "Agent", "docstring": "Specialized Agent for performing Shared National Credit (SNC) simulations.\n\nActs as a virtual 'Senior Credit Officer', applying regulatory frameworks\n(OCC/Fed/FDIC) to classify debt facilities based on:\n1. Primary Repayment Source (Cash Flow/EBITDA)\n2. Secondary Repayment Source (Collateral/Enterprise Value)\n\nDeveloper Note:\n---------------\nThis agent implements the \"Interagency Guidance on Leveraged Lending\" logic.\nIt separates the borrower-level rating (Ability to Repay) from the facility-level\nrating (Loss Given Default), allowing for \"notching up\" based on collateral.", "file": "./core/agents/specialized/credit_snc.py", "lineno": 10, "verification_script": null }, { "name": "QuantumSearchAgent", "type": "Agent", "docstring": "QuantumSearchAgent: A specialized agent that acts as a bridge between\nclassical search intent and the AVG (AdamVanGrover) hybrid quantum-classical\noptimization framework.\n\nIt simulates the process of finding \"needles\" (anomalies, specific keys)\nin massive datasets (haystacks) by leveraging the AVGSearch engine.", "file": "./core/agents/specialized/quantum_search_agent.py", "lineno": 10, "verification_script": "./verification/verify_quantum_search.py" }, { "name": "RootNodeAgent", "type": "Agent", "docstring": "An agent that solves complex problems by building a search tree of reasoning steps.", "file": "./core/agents/specialized/root_node_agent.py", "lineno": 36, "verification_script": null }, { "name": "AgentBase", "type": "Agent", "docstring": null, "file": "./core/agents/specialized/root_node_agent.py", "lineno": 20, "verification_script": null }, { "name": "MacroLiquidityAgent", "type": "Agent", "docstring": "Agent responsible for assessing global macro liquidity conditions by analyzing\nbond yields, credit spreads, currency strength, and commodity proxies.\n\nIt calculates a 'Liquidity Stress Index' that serves as a fundamental input\nfor Risk Agents and Portfolio Managers.", "file": "./core/agents/specialized/macro_liquidity_agent.py", "lineno": 35, "verification_script": "./verification/verify_macro.py" }, { "name": "InstitutionalRadarAgent", "type": "Agent", "docstring": "Agent responsible for executing the Institutional Radar blueprint:\nIngesting 13F data, analyzing trends, and generating narrative reports.", "file": "./core/agents/specialized/institutional_radar_agent.py", "lineno": 13, "verification_script": null }, { "name": "CounterpartyRiskAgent", "type": "Agent", "docstring": "Responsibility: PFE, Wrong-Way Risk (WWR).", "file": "./core/agents/specialized/counterparty_risk_agent.py", "lineno": 9, "verification_script": null }, { "name": "QuantumStrategyAgent", "type": "Agent", "docstring": "Specialized Agent that orchestrates the AdamVanGrover simulation and\nQuantum Recommendation Engine to generate high-level strategic advice.", "file": "./core/agents/specialized/quantum_strategy_agent.py", "lineno": 8, "verification_script": null }, { "name": "CovenantAnalystAgent", "type": "Agent", "docstring": "Phase 3 Helper: Covenant Analysis.\nParses credit agreements (or simulates them) for maintenance covenants.\n\nThis agent simulates the role of a Legal/Credit analyst reviewing the Credit Agreement.\nIt checks for Financial Maintenance Covenants (Total Net Leverage, Interest Coverage)\nand estimates the risk of a \"Foot Fault\" or technical default.", "file": "./core/agents/specialized/credit_lawyer.py", "lineno": 9, "verification_script": null }, { "name": "CreditRiskControllerAgent", "type": "Agent", "docstring": "The 'Senior Credit Risk Controller' Agent.\n\nA digital twin of a Regulatory Examiner/Senior Credit Officer.\n\nDirectives:\n1. Ingest granular facility data (SNCnet schema).\n2. Deterministically calculate implied ratings (S&P/Moody's logic).\n3. Simulate Regulatory Disagreement (SNC Review logic).\n4. Generate defense-ready eSNC Cover Pages.\n\nArchitecture:\n- Pre-Computation Layer: Python-based execution of the S&P Matrix and Conviction Score formula.\n- Inference Layer: LLM-based construction of the \"Defense Narrative\" and qualitative synthesis.", "file": "./core/agents/specialized/credit_risk_controller_agent.py", "lineno": 12, "verification_script": null }, { "name": "ForensicAccountantAgent", "type": "Agent", "docstring": "Specialized agent for detecting financial fraud and anomalies in ledger data.\nUses statistical methods (Benford's Law) and heuristic rules.", "file": "./core/agents/specialized/forensic_accountant_agent.py", "lineno": 8, "verification_script": null }, { "name": "RetailAlphaAgent", "type": "Agent", "docstring": "Retail Alpha Agent: 'The Retail Supplement'\n\nThis agent bridges the gap between institutional data (13Fs, Risk Models) and\nretail trading needs (Signals, Hype, Simple Metrics).\n\nIt generates 'Alpha Signals' by looking for divergences:\n- Smart Money Buying vs Retail Selling (Bullish Divergence)\n- Smart Money Selling vs Retail Euphoria (Bearish Trap)", "file": "./core/agents/specialized/retail_alpha_agent.py", "lineno": 23, "verification_script": null }, { "name": "BlindspotAgent", "type": "Agent", "docstring": "Protocol: ADAM-V-NEXT\nA meta-cognitive agent responsible for scanning the system's knowledge graph\nfor disconnected nodes, contradictory data points, and 'unknown unknowns'.", "file": "./core/agents/specialized/blindspot_agent.py", "lineno": 38, "verification_script": null }, { "name": "NarrativeIntelligenceAgent", "type": "Agent", "docstring": "Protocol: ADAM-V-NEXT\nSpecialized agent that moves beyond simple sentiment scoring to identify\nemerging thematic narratives (e.g., 'AI Bubble', 'Energy Crisis').", "file": "./core/agents/specialized/narrative_intelligence_agent.py", "lineno": 7, "verification_script": null }, { "name": "PeerComparisonAgent", "type": "Agent", "docstring": "Phase 2 Helper: Peer Comparison.\nFetches and calculates relative multiples.", "file": "./core/agents/specialized/peer_comparison_agent.py", "lineno": 9, "verification_script": null }, { "name": "QuantumRiskAgent", "type": "Agent", "docstring": "Specialized agent that uses Quantum Monte Carlo methods for risk analysis.\nPart of the Adam v24.0 'Quantum-Native' suite.", "file": "./core/agents/specialized/quantum_risk_agent.py", "lineno": 8, "verification_script": null }, { "name": "RiskCoPilotAgent", "type": "Agent", "docstring": "Automated Credit Risk Officer capable of diagnosing breaches and summarizing risk.", "file": "./core/agents/specialized/risk_copilot_agent.py", "lineno": 27, "verification_script": null }, { "name": "AgentBase", "type": "Agent", "docstring": null, "file": "./core/agents/specialized/risk_copilot_agent.py", "lineno": 18, "verification_script": null }, { "name": "SentinelAgent", "type": "Agent", "docstring": "The Data Integrity Guardian.\nResponsibility: Ingestion, Extraction, Validation against FIBO Schema.", "file": "./core/agents/specialized/sentinel_agent.py", "lineno": 9, "verification_script": null }, { "name": "TechnicalCovenantAgent", "type": "Agent", "docstring": "Specialized Agent: The Legal Analyst (Law Firm Associate Persona).\n\nThis agent focuses purely on the textual \"rules of the road\" within the Credit Agreement.\nIt identifies definitions, baskets, and blockers.\n\nEnhanced Capabilities:\n- Context-Aware Checking: Prioritizes checks based on borrower history (e.g., Aggressive Sponsors).\n- Historical Precedent: Flags \"Market Standard\" vs \"Outlier\" terms.", "file": "./core/agents/specialized/technical_covenant_agent.py", "lineno": 9, "verification_script": null }, { "name": "QuantumRetrievalAgent", "type": "Agent", "docstring": "An agent that uses Quantum Annealing simulations optimized by Adam\nto \"retrieve\" data from massive unstructured datasets (simulated).\nEnhanced to support Credit & Restructuring Search.", "file": "./core/agents/specialized/quantum_retrieval_agent.py", "lineno": 11, "verification_script": null }, { "name": "MonteCarloRiskAgent", "type": "Agent", "docstring": "Quantitative Risk Agent using Monte Carlo simulations.\n\nMethodology:\n1. Models EBITDA as a stochastic process (Geometric Brownian Motion, Heston, or OU).\n2. Runs iterations (default 10,000) over a defined horizon.\n3. Triggers 'Default' if EBITDA falls below Interest Expense + Maintenance Capex.\n\nDeveloper Note:\n---------------\nNow supports Heston (stochastic volatility) and OU (mean reversion).", "file": "./core/agents/specialized/monte_carlo_risk_agent.py", "lineno": 46, "verification_script": null }, { "name": "PortfolioManagerAgent", "type": "Agent", "docstring": "Phase 5: Synthesis & Conviction.\nThe 'Conviction Engine' that weighs all previous phases.", "file": "./core/agents/specialized/portfolio_manager_agent.py", "lineno": 9, "verification_script": null }, { "name": "DistressedSurveillanceAgent", "type": "Agent", "docstring": "Agent responsible for identifying 'Zombie Issuers' in the BSL market.\nWraps the SurveillanceGraph.", "file": "./core/agents/specialized/distressed_surveillance_agent.py", "lineno": 9, "verification_script": null }, { "name": "InstitutionalTrendAgent", "type": "Agent", "docstring": "Agent responsible for monitoring institutional capital flows via 13F filings\nand generating strategic market intelligence reports.\n\nArchitecture:\n1. Ingestion Layer (Hard Logic): Fetches raw 13F data via Sec13FHandler.\n2. Processing Layer (Pandas): Calculates deltas (New/Exits/Increases).\n3. Cognitive Layer (LLM): Synthesizes quantitative moves into qualitative strategy.", "file": "./core/agents/specialized/institutional_trend_agent.py", "lineno": 14, "verification_script": null }, { "name": "QuantumScenarioAgent", "type": "Agent", "docstring": "Phase 4 Helper: Quantum Scenario Generation.\n\nThis agent bridges the gap between classical risk modeling and quantum-enhanced simulation.\nIt utilizes the `QuantumMonteCarloEngine` (QMC) for structural credit modeling and the\n`GenerativeRiskEngine` (GRE) for tail-risk scenario generation.\n\nDeveloper Note:\n---------------\nIn environments without a QPU or heavy GPU dependencies, this agent gracefully degrades\nto use classical approximations (numpy-based QMC simulation) and heuristic-based\ngenerative models.", "file": "./core/agents/specialized/quantum_scenario_agent.py", "lineno": 23, "verification_script": null }, { "name": "TemplateAgent", "type": "Agent", "docstring": "A template for creating v23-compatible agents.\n\nThis class demonstrates:\n1. Asynchronous task execution.\n2. Tool usage via the tool manager.\n3. Interaction with the Unified Knowledge Graph (UKG).\n4. Structured error handling and logging.", "file": "./core/agents/templates/v23_template_agent.py", "lineno": 13, "verification_script": null }, { "name": "AdaptiveRPCAgent", "type": "Agent", "docstring": "V23.5 'Apex' Agent with Metacognitive Gating.\n\nImplements the 'Protocol Paradox' resolution:\n1. JSON-RPC 2.0 Native: Speaks standard MCP.\n2. Heuristic 1 (Ambiguity Guardrail): Reverts to text if conviction is low.\n3. Heuristic 2 (Context Budgeting): Just-in-Time tool loading.", "file": "./core/agents/templates/v23_adaptive_rpc_agent.py", "lineno": 9, "verification_script": null }, { "name": "AgentInput", "type": "Agent", "docstring": "Standard input for a v26 Agent.", "file": "./core/agents/templates/v26_template_agent.py", "lineno": 15, "verification_script": null }, { "name": "AgentOutput", "type": "Agent", "docstring": "Standard output for a v26 Agent.", "file": "./core/agents/templates/v26_template_agent.py", "lineno": 20, "verification_script": null }, { "name": "TemplateAgentV26", "type": "Agent", "docstring": "A template for creating Adam v26.0 (System 2) Agents.\n\nAdheres to:\n- Strict Typing (Pydantic)\n- Grounding (Source Citation)\n- Error Handling (Graceful Degradation)", "file": "./core/agents/templates/v26_template_agent.py", "lineno": 31, "verification_script": null }, { "name": "PlannerAgent", "type": "Agent", "docstring": "The PlannerAgent takes a high-level feature request or bug report\nand breaks it down into a detailed, structured plan with discrete,\nverifiable steps. This plan can then be executed by other agents\nin the developer swarm.", "file": "./core/agents/developer_swarm/planner_agent.py", "lineno": 11, "verification_script": null }, { "name": "SpecArchitectAgent", "type": "Agent", "docstring": "The SpecArchitectAgent is the 'Architect' in the Spec-Driven Development workflow.\nIts sole purpose is to take a high-level vision or goal and produce a rigorous,\nstructured SPEC.md file. It operates in 'Plan Mode' (read-only) and does not\nwrite application code.", "file": "./core/agents/developer_swarm/spec_architect_agent.py", "lineno": 11, "verification_script": null }, { "name": "TestAgent", "type": "Agent", "docstring": "The TestAgent writes unit tests for code generated by the CoderAgent\nand runs them to ensure correctness.", "file": "./core/agents/developer_swarm/test_agent.py", "lineno": 11, "verification_script": null }, { "name": "ReviewerAgent", "type": "Agent", "docstring": "The ReviewerAgent checks code for style guide violations (PEP 8),\npotential bugs, and adherence to architectural principles.", "file": "./core/agents/developer_swarm/reviewer_agent.py", "lineno": 11, "verification_script": null }, { "name": "IntegrationAgent", "type": "Agent", "docstring": "The IntegrationAgent merges code, tests, and documentation into the\nmain branch once all checks have passed.", "file": "./core/agents/developer_swarm/integration_agent.py", "lineno": 11, "verification_script": "./verification/verify_swarm_integration.py" }, { "name": "CoderAgent", "type": "Agent", "docstring": "The CoderAgent takes a specific task from a plan and writes the\nPython code to implement it.", "file": "./core/agents/developer_swarm/coder_agent.py", "lineno": 11, "verification_script": null }, { "name": "DocumentationAgent", "type": "Agent", "docstring": "The DocumentationAgent writes and updates documentation based on the\ncode changes made by the CoderAgent.", "file": "./core/agents/developer_swarm/documentation_agent.py", "lineno": 11, "verification_script": null }, { "name": "CreditAgentBase", "type": "Agent", "docstring": "Base class for all Credit Memo agents.\nEnforces audit logging and strict output schemas.", "file": "./core/agents/credit/credit_agent_base.py", "lineno": 9, "verification_script": null }, { "name": "ArchivistAgent", "type": "Agent", "docstring": "The Retrieval Agent.\nResponsible for fetching documents and chunks from the Vector DB (mocked).", "file": "./core/agents/credit/archivist.py", "lineno": 7, "verification_script": null }, { "name": "WriterAgent", "type": "Agent", "docstring": "The Generation Agent.\nResponsible for synthesizing the final credit memo and inserting citations.", "file": "./core/agents/credit/writer.py", "lineno": 8, "verification_script": null }, { "name": "QuantAgent", "type": "Agent", "docstring": "The Spreading Agent.\nResponsible for extracting structured financial data from unstructured documents.", "file": "./core/agents/credit/quant.py", "lineno": 6, "verification_script": "./verification/verify_quantum_simulation.py" }, { "name": "FinancialNewsSubAgent", "type": "Agent", "docstring": null, "file": "./core/agents/sub_agents/financial_news_sub_agent.py", "lineno": 5, "verification_script": null }, { "name": "InternalSystemsAgent", "type": "Agent", "docstring": "The Internal Systems Agent serves as the secure and reliable conduit to the\nfinancial institution's own internal systems of record. It acts as the \"source\nof truth\" for all data related to the institution's existing relationship\nwith the borrower.", "file": "./core/agents/sub_agents/internal_systems_agent.py", "lineno": 8, "verification_script": null }, { "name": "FinancialDocumentAgent", "type": "Agent", "docstring": "The Financial Document Agent is designed to eliminate one of the most time-consuming\nand error-prone bottlenecks in traditional credit analysis: manual data entry from\nphysical or digital documents. This agent leverages state-of-the-art AI-powered\ntechnologies to automate the ingestion and structuring of financial information.\n\nIts primary tool is an advanced Optical Character Recognition (OCR) engine,\nenhanced with machine learning models trained specifically on financial document layouts.", "file": "./core/agents/sub_agents/financial_document_agent.py", "lineno": 12, "verification_script": null }, { "name": "ComplianceKYCAgent", "type": "Agent", "docstring": "Operating as a critical gatekeeper for regulatory adherence, the Compliance & KYC\nAgent automates the essential checks required for client onboarding and ongoing\nmonitoring. This agent interfaces directly, via secure APIs, with a suite of\ninternal and external databases.", "file": "./core/agents/sub_agents/compliance_kyc_agent.py", "lineno": 8, "verification_script": null }, { "name": "DataIngestionAgent", "type": "Agent", "docstring": "Agent responsible for data ingestion tasks using the Gold Standard Toolkit.\nHandles daily history downloads, intraday snapshots, and schema validation.\n\nVersion: Adam v24 (Sprint 1: Sensory Layer)", "file": "./core/agents/sub_agents/data_ingestion_agent.py", "lineno": 10, "verification_script": null }, { "name": "MarketAlternativeDataAgent", "type": "Agent", "docstring": "To build a truly comprehensive and forward-looking risk profile, the system must\nlook beyond the borrower's own financial disclosures. The Market & Alternative\nData Agent is tasked with this \"outside-in\" view. It continuously scans and\ningests a wide spectrum of both structured and unstructured information from\nthe public domain.", "file": "./core/agents/sub_agents/market_alternative_data_agent.py", "lineno": 8, "verification_script": null }, { "name": "GitRepoSubAgent", "type": "Agent", "docstring": null, "file": "./core/agents/sub_agents/git_repo_sub_agent.py", "lineno": 6, "verification_script": null }, { "name": "RepoGuardianAgent", "type": "Agent", "docstring": "The RepoGuardian Agent serves as an automated code reviewer and gatekeeper.\nIt analyzes proposed changes against repository standards and provides\nstructured feedback.", "file": "./core/agents/governance/repo_guardian/agent.py", "lineno": 27, "verification_script": null }, { "name": "TestRepoGuardianAgent", "type": "Agent", "docstring": null, "file": "./core/agents/governance/repo_guardian/tests/test_agent.py", "lineno": 7, "verification_script": null }, { "name": "CapacityPlannerAgent", "type": "Agent", "docstring": "Monitors system telemetry (CPU, GPU, Memory) and recommends infrastructure scaling actions.\nFunctions as the \"Site Reliability Engineer\" (SRE) of the Analyst OS.", "file": "./core/agents/infrastructure/capacity_planner_agent.py", "lineno": 6, "verification_script": null }, { "name": "OdysseyHubAgent", "type": "Agent", "docstring": "Adam v25.5 (Odyssey Orchestrator)\nThe central Hub agent for the Odyssey Financial System.\nOrchestrates the 'Hub-and-Spoke' architecture and enforces semantic consistency\nvia the Odyssey Unified Knowledge Graph (OUKG).", "file": "./core/agents/orchestrators/odyssey_hub_agent.py", "lineno": 11, "verification_script": null }, { "name": "PersonaCommunicationAgent", "type": "Agent", "docstring": "The Persona & Communication Agent is the final layer in the output chain,\nacting as the system's \"finishing school.\" Its sole purpose is to tailor the\npresentation of the final output to the specific needs, role, and authority\nlevel of the human user interacting with the system.", "file": "./core/agents/meta_agents/persona_communication_agent.py", "lineno": 8, "verification_script": null }, { "name": "AutoArchitectAgent", "type": "Agent", "docstring": "Scans the repository to generate a real-time 'Current State' architectural document.\nEnsures documentation never drifts from code.", "file": "./core/agents/meta_agents/auto_architect_agent.py", "lineno": 7, "verification_script": null }, { "name": "CrisisSimulationMetaAgent", "type": "Agent", "docstring": "A meta-agent that conducts dynamic, enterprise-grade crisis simulations.\nIt uses a sophisticated prompt structure to simulate the cascading effects of\nrisks based on a user-defined scenario.", "file": "./core/agents/meta_agents/crisis_simulation_agent.py", "lineno": 18, "verification_script": "./verification/verify_crisis_sim.py" }, { "name": "ChronosAgent", "type": "Agent", "docstring": "Chronos is the Keeper of Time and Memory.\n\nIt manages the temporal state of the application, determining which memory context\n(short-term, medium-term, long-term) is most relevant via the `_retrieve_memories` logic.\nIt also draws parallels between current events and historic financial periods using\nLLM-driven historical analysis.", "file": "./core/agents/meta_agents/chronos_agent.py", "lineno": 24, "verification_script": null }, { "name": "CreditRiskAssessmentAgent", "type": "Agent", "docstring": "This agent is the central analytical engine of the system, responsible for\nconducting a comprehensive commercial credit analysis that mirrors the rigor\nof a seasoned human underwriter.", "file": "./core/agents/meta_agents/credit_risk_assessment_agent.py", "lineno": 8, "verification_script": null }, { "name": "DidacticArchitectAgent", "type": "Agent", "docstring": "The Didactic Architect Agent is a meta-agent designed to build modular,\nself-contained, portable, and complementary tutorials and setups.\nIt bridges the gap between code and comprehension.", "file": "./core/agents/meta_agents/didactic_architect_agent.py", "lineno": 16, "verification_script": null }, { "name": "OdysseyMetaAgent", "type": "Agent", "docstring": "Strategic Synthesis Agent.\nAggregates inputs from Sentinel, CreditSentry, Argus, etc. to produce final XML decision.", "file": "./core/agents/meta_agents/odyssey_meta_agent.py", "lineno": 8, "verification_script": null }, { "name": "SkillHarvesterAgent", "type": "Agent", "docstring": "Crawls the agent swarm to extract 'get_skill_schema' definitions\nand compiles a structured registry JSON for the Agent Gallery and MCP.", "file": "./core/agents/meta_agents/skill_harvester_agent.py", "lineno": 10, "verification_script": null }, { "name": "CounterpartyRiskAgent", "type": "Agent", "docstring": "For clients engaging in derivative transactions (e.g., interest rate swaps,\ncurrency forwards), the system's dedicated CounterpartyRiskAgent is activated.\nThis agent is specifically designed to quantify the complex, contingent risks\nassociated with these instruments.", "file": "./core/agents/meta_agents/counterparty_risk_agent.py", "lineno": 8, "verification_script": null }, { "name": "EvolutionaryArchitectAgent", "type": "Agent", "docstring": "The Evolutionary Architect Agent is a meta-agent predisposed for action.\nIt drives the codebase forward by proposing additive enhancements, refactors,\nand optimizations. It uses 'Active Inference' principles to minimize the\ndivergence between the current codebase state and the desired goal state.", "file": "./core/agents/meta_agents/evolutionary_architect_agent.py", "lineno": 17, "verification_script": "./verification/verify_evolution.py" }, { "name": "SentimentAnalysisMetaAgent", "type": "Agent", "docstring": null, "file": "./core/agents/meta_agents/sentiment_analysis_meta_agent.py", "lineno": 5, "verification_script": null }, { "name": "PortfolioMonitoringEWSAgent", "type": "Agent", "docstring": "This agent is the system's vigilant sentinel, responsible for continuous,\nreal-time surveillance of the entire credit portfolio. Its function is to\nmove the institution from a reactive to a proactive risk management posture.", "file": "./core/agents/meta_agents/portfolio_monitoring_ews_agent.py", "lineno": 8, "verification_script": null }, { "name": "NarrativeSummarizationAgent", "type": "Agent", "docstring": "This agent functions as the system's dedicated writer, editor, and communicator.\nIts purpose is to bridge the gap between complex, quantitative machine output\nand the need for clear, concise, and context-rich human understanding.", "file": "./core/agents/meta_agents/narrative_summarization_agent.py", "lineno": 8, "verification_script": null } ], "verifications": [ { "name": "verify_reports.py", "path": "./verification/verify_reports.py", "target": "reports" }, { "name": "verify_agent_intercom.py", "path": "./verification/verify_agent_intercom.py", "target": "agent_intercom" }, { "name": "verify_market_mayhem_v24.py", "path": "./verification/verify_market_mayhem_v24.py", "target": "market_mayhem_v24" }, { "name": "verify_showcase_libraries.py", "path": "./verification/verify_showcase_libraries.py", "target": "showcase_libraries" }, { "name": "verify_narrative_dashboard.py", "path": "./verification/verify_narrative_dashboard.py", "target": "narrative_dashboard" }, { "name": "verify_credit_ui.py", "path": "./verification/verify_credit_ui.py", "target": "credit_ui" }, { "name": "verify_fraud_frontend.py", "path": "./verification/verify_fraud_frontend.py", "target": "fraud_frontend" }, { "name": "verify_quantum_simulation.py", "path": "./verification/verify_quantum_simulation.py", "target": "quantum_simulation" }, { "name": "verify_credit_memo_new_features.py", "path": "./verification/verify_credit_memo_new_features.py", "target": "credit_memo_new_features" }, { "name": "verify_trading_terminal.py", "path": "./verification/verify_trading_terminal.py", "target": "trading_terminal" }, { "name": "verify_intercom.py", "path": "./verification/verify_intercom.py", "target": "intercom" }, { "name": "verify_credit_memo_library.py", "path": "./verification/verify_credit_memo_library.py", "target": "credit_memo_library" }, { "name": "verify_system_brain.py", "path": "./verification/verify_system_brain.py", "target": "system_brain" }, { "name": "verify_war_room_enhanced.py", "path": "./verification/verify_war_room_enhanced.py", "target": "war_room_enhanced" }, { "name": "verify_war_room_final.py", "path": "./verification/verify_war_room_final.py", "target": "war_room_final" }, { "name": "verify_macro.py", "path": "./verification/verify_macro.py", "target": "macro" }, { "name": "verify_swarm_matrix.py", "path": "./verification/verify_swarm_matrix.py", "target": "swarm_matrix" }, { "name": "verify_empty_state.py", "path": "./verification/verify_empty_state.py", "target": "empty_state" }, { "name": "verify_credit_backend.py", "path": "./verification/verify_credit_backend.py", "target": "credit_backend" }, { "name": "verify_governance.py", "path": 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