Dexter is a CLI-based AI agent framework specifically designed for deep financial research. It implements a sophisticated agent loop that combines task planning, autonomous tool execution, and self-validation to answer complex financial queries. The system features a modular architecture with specialized systems for memory management, tool execution, permissions, and skills. Key capabilities include access to real-time financial data through multiple APIs, web search functionality, file system operations, browser automation, and specialized workflows like DCF valuation. The agent maintains persistent memory across sessions using vector embeddings and keyword search with temporal decay weighting. Dexter can be deployed via CLI or as a WhatsApp bot, and includes an evaluation framework for testing agent performance against financial questions. The system emphasizes security through a robust permission system that controls access to potentially dangerous operations like shell commands.
Feynman is an open-source AI research assistant that provides both CLI and web-based workbench interfaces for scientific research workflows. Built on the Pi coding agent runtime, it integrates with numerous scientific databases (PubChem, ChEMBL, UniProt, etc.) and provides a skill-based system for extending functionality with specialized research tools. The system manages research sessions, artifacts, and computational workflows while maintaining provenance tracking and versioning. It features a React-based web interface for interactive work with scientific data, artifact previews, and computational job management, alongside a CLI for terminal-based research tasks.
Black Duck Detect is a Java-based Software Composition Analysis (SCA) tool that scans project dependencies to identify open source components, licenses, and security vulnerabilities. It functions as an intelligent client for the Black Duck SCA platform, sending Bill of Materials (BOM) data for risk analysis. The architecture is modular with core detector framework, ecosystem-specific detectors (Maven, NPM, Gradle, etc.), configuration system, workflow orchestration, and tool integrations. Entry points include CLI scripts (detect.sh/detect.ps1) and the DetectorApplication main class. Primary data flows involve configuration parsing, ecosystem detection, dependency extraction, BDIO generation, and upload to Black Duck server. External dependencies include package managers/build tools (mvn, npm, etc.) and libraries like Gson and Apache Commons.