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Adam v23.5: Your AI-Powered Partner

Note: This document describes the current active version of the Adam system (v23.0). For details on the legacy stable version, please see the v21.0 Documentation.

• Cyclical Reasoning Graph: A self-correcting neuro-symbolic engine.
• Neural Dashboard: Real-time visualization of agent thought processes.
• Hybrid Architecture: Combining v21's reliability with v22's speed and v23's intelligence.
• Gold Standard Data Pipeline: A rigorous "universal ingestion" process that scrubs and certifies all system knowledge.

🧠 Adam v23.0: The Adaptive Hive Mind

Mission: Autonomous Financial Analysis & Adaptive Reasoning

Adam has evolved. v23.0 introduces the "Adaptive System" architecture—a self-correcting, neuro-symbolic engine designed to perform deep financial deep dives, risk assessments, and market simulations with human-like reasoning and machine-speed execution. Unlike traditional chatbots, Adam "thinks" in graphs, critiquing its own work before presenting it to you.

🚀 Launch Neural Dashboard | 📖 Read the User Guide | ⚡ Quick Start

🚀 Mission Control

Launch Neural Dashboard Monitor real-time agent reasoning, knowledge graph updates, and risk simulations.

🏗️ System Architecture

Adam v23.0 moves beyond linear chains to a dynamic, graph-based execution model. The system creates a "Cyclical Reasoning Graph" for every query, allowing it to draft, critique, and refine its own analysis before presenting results.

graph TD
    User[User / API] -->|Query| Meta[Meta Orchestrator]

    subgraph "The Brain (v23 Graph Engine)"
        Meta --> Planner[Neuro-Symbolic Planner]
        Planner -->|Generates Path| Graph[Dynamic Reasoning Graph]

        Graph --> Node1[Data Retrieval]
        Graph --> Node2[Analysis Agent]
        Graph --> Node3[Risk Simulation]

        Node1 -->|Evidence| Critic[Self-Correction Loop]
        Node2 -->|Draft| Critic
        Node3 -->|Scenarios| Critic

        Critic -->|Refinement Needed| Graph
        Critic -->|Approved| Synthesis[Final Synthesis]
    end

    subgraph "Memory & Knowledge"
        KG[(Unified Knowledge Graph)]
        Vec[(Vector Store)]
    end

    Node1 <--> KG
    Node1 <--> Vec

    Synthesis -->|Final Report| Output[JSON / HTML / PDF]
````

#### Core Components

  * **Meta Orchestrator** (`core/engine/meta_orchestrator.py`): The central "cortex" that routes tasks, manages state, and orchestrates the swarm of specialized agents.
  * **Neuro-Symbolic Planner** (`core/engine/neuro_symbolic_planner.py`): Combines the creativity of LLMs with the logical rigor of Knowledge Graphs to plan execution paths that are both novel and grounded in fact.
  * **Cyclical Reasoning Engine** (`core/engine/cyclical_reasoning_graph.py`): A feedback loop (Draft -\> Critique -\> Refine) that ensures high conviction. It detects logical fallacies or missing data and automatically schedules remedial tasks.

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### 📊 Data & The Gold Standard Pipeline

Garbage in, garbage out. Adam v23.0 utilizes a rigorous **Gold Standard Data Pipeline** ("The Universal Ingestor") to ensure all insights are based on verified, high-quality data.

  * **Ingestion Sources:** Financial news APIs, SEC filings (XBRL), market data feeds (Bloomberg/AlphaVantage connectors), and government statistics.
  * **Scrubbing & Validation:** Every data point is scored for "Conviction" (0-100%) based on source reliability and cross-verification against the Knowledge Graph.
  * **Unified Format:** Data is normalized into a standard JSONL format for agent consumption.

[View Data Pipeline Documentation](https://www.google.com/search?q=%23)

-----

### 💡 Example Outputs

### 3. Gold Standard Data Pipeline
A new "Universal Ingestor" ensures that every piece of data in the system is high-quality.

*   **Ingest & Scrub:** Recursively scans reports, prompts, code, and data.
*   **Conviction Scoring:** Automatically assesses the quality and "conviction" of data (0-100%).
*   **Unified Access:** All data is normalized into a standard JSONL format accessible by any agent.
*   [Read the Pipeline Documentation](./docs/GOLD_STANDARD_PIPELINE.md)
Adam doesn't just chat; it produces structured, professional-grade financial artifacts ready for investment committees.

#### 1\. Strategic Deep Dive (JSON Snippet)

*Generated by the Omniscient Analyst (v23.5) - Full Template*

```json
{
  "report_id": "RPT-NVDA-2025-03",
  "entity": "NVIDIA Corp",
  "conviction_score": 94.5,
  "strategic_synthesis": {
    "outlook": "Bullish",
    "key_driver": "Sovereign AI adoption and B200 backlog saturation.",
    "risks": ["Supply chain concentration (TSMC)", "Geopolitical export controls"]
  },
  "valuation_models": {
    "dcf_implied_price": 1450.00,
    "peer_multiple_target": 1380.00,
    "sensitivity_analysis": "High sensitivity to datacenter capex reduction."
  },
  "generated_at": "2025-03-15T14:30:00Z"
}

2. Risk Assessment Matrix

Adam automatically generates risk matrices for portfolio stress testing using the Monte Carlo Risk Agent.

1. Run Adam: bash python scripts/run_adam.py

2. View the Showcase: Open showcase/index.html in your browser.

💰 Financial Engineering Platform (v23.5)

A modular, portable, and configurable Financial Engine for DCF Valuation, VC/LBO Sponsor Modeling, and Regulatory Credit Risk Analysis (SNC/Rating).

🚀 Launch Dashboard

Launch Financial Engine (Client Side) Interactive dashboard for valuation, credit ratings, and sensitivity analysis.

🐍 Python Core

The core logic is available in src/ and can be run as a Streamlit app.

# Run the Interactive Streamlit App
streamlit run app.py

Modules

• src/core_valuation.py: Discounted Cash Flow (DCF), WACC, and Terminal Value logic.
• src/credit_risk.py: Credit Sponsor Model, Downside Sensitivity, and Regulatory Ratings (SNC).
• src/config.py: Global financial assumptions (Tax rates, Risk-free rates).

📂 Repository Structure

• core/engine/: The heart of the new system.
  • cyclical_reasoning_graph.py: The self-correcting analysis loop.
  • neuro_symbolic_planner.py: The logic for pathfinding in the KG.
  • meta_orchestrator.py: The central brain routing tasks.
• core/data_processing/: Data ingestion and standardisation.
  • universal_ingestor.py: The Gold Standard Pipeline.
• showcase/: The "Mission Control" UI assets.
• data/: Knowledge base and artisanal training sets.
• docs/: Comprehensive documentation.

🚀 Key Capabilities

• Cyclical Reasoning: Unlike standard chatbots, Adam iterates. If data is missing, it creates a sub-task to find it. If logic is flawed, it self-corrects.
• Quantum Risk Modeling: (v23.5) Uses simulated quantum annealing (via core/v22_quantum_pipeline/) to model "Black Swan" events and their impact on complex portfolios.
• Traceability: Every conclusion is back-linked to source documents in the Knowledge Graph (PROV-O ontology compliant).
• Multi-Modal Output: Generates interactive HTML dashboards (showcase/), PDF reports, and raw JSON data streams.
• Specialized Agent Swarms:
  • Fundamental Analyst: Deep value investing analysis.
  • Technical Analyst: Chart patterns and momentum indicators.
  • SNC Analyst: Regulatory credit risk grading (Shared National Credit).

🛠️ Getting Started

Prerequisites

• Python 3.10+
• Node.js (Required for the Neural Dashboard UI)
• API Keys: OpenAI (or compatible LLM provider), Neo4j (optional for full Knowledge Graph).

Quick Start Guide

1. Clone and Enter Repository bash git clone https://github.com/adamvangrover/adam.git cd adam

2. One-Click Launch Run the automated launcher script. It checks for Docker or Python, installs dependencies, and starts the system. bash ./run_adam.sh

   Alternatively, for manual setup: bash pip install -e . # Install as a package python core/main.py # Run the engine

3. Configure API Keys (Optional but Recommended) The launcher creates a .env file if missing. Edit it to add your keys: bash OPENAI_API_KEY=sk-...

4. Open Mission Control

   • UI: http://localhost:80 (Docker) or http://localhost:3000 (Local)
   • Neural Dashboard: showcase/index.html

Developer Experience

We provide standard tooling for developers:

• Install Dependencies: make install
• Run Tests: make test
• Lint Code: make lint
• CI/CD: Automated testing via GitHub Actions is configured in .github/workflows/ci.yml.

See CONTRIBUTING.md for more details.

📂 Repository Structure

A high-level overview of the "Hive Mind" structure:

📚 Resources & Documentation

• Architecture Vision: Adam v23.0 "Adaptive Hive" Vision
• Pipeline Details: [Gold Standard Data Pipeline]((https://github.com/adamvangrover/adam/tree/main/docs/GOLD_STANDARD_PIPELINE.md)
• API Reference: API Documentation
• User Manual: Comprehensive User Guide
• Demo Guide: Showcase Walkthrough
• Prompt Library: v23.5 Autonomous Analyst Prompt

🤝 Contributing

We welcome contributions from the community! Whether it's a new agent skill, a data connector, or a UI enhancement.

1. Read our Contribution Guidelines.
2. Fork the repo and create your branch (git checkout -b feature/amazing-feature).
3. Commit your changes (git commit -m 'Add some amazing feature').
4. Push to the branch (git push origin feature/amazing-feature).
5. Open a Pull Request.

📄 License

Distributed under the MIT License. See LICENSE for more information.

⚡ Modernization & Optimization (v23.5+)

As part of the 2025 Strategic Technical Modernization, the following high-performance components have been added:

• Modern Build System: pyproject.toml and uv support for hermetic builds.
• Optimization Service: A dedicated stateful microservice (src/adam/api) providing "Optimizer as a Service" via FastAPI and Redis.
• State-of-the-Art Optimizers:
  • AdamW: Decoupled Weight Decay.
  • Lion: Evolved Sign Momentum (Google ADK).
  • Adam-mini: Memory-efficient block-wise optimization (2025 Frontier).

Quick Start (Modern Stack)

1. Build with Docker: bash docker build -f Dockerfile.modern -t adam-optimizer .

2. Run the API: bash docker run -p 8000:8000 -e REDIS_URL=redis://host.docker.internal:6379/0 adam-optimizer

For details, see Modernization Report.