TradingAgents is a multi-agent LLM framework for financial trading research and backtesting. It orchestrates specialized agents (analysts, researchers, debaters, trader, portfolio manager) through a LangGraph state machine to produce trading decisions. The system emphasizes point-in-time data isolation to prevent lookahead bias, supports multiple LLM providers and data vendors, and includes a comprehensive backtesting engine. It is structured as a Python library with a rich CLI interface. The architecture is a hybrid of a software application and a research framework. It demonstrates high craftsmanship in its data pipeline (vendor routing, symbol normalization, date windowing), LLM integration (capability registry, structured output fallback), and concurrency handling (atomic file writes). While the core abstractions are clean and reusable, the codebase is tightly coupled to the specific behaviors of its external dependencies (LLM APIs, Yahoo Finance, SEC EDGAR), making it sensitive to ecosystem drift. The extensive test suite provides good coverage for unit and integration scenarios, but the reliance on mocking means regressions in vendor adapters may go undetected without manual integration runs.
Neuron AI is a PHP framework for building AI-powered applications with support for multiple large language model providers (OpenAI, Anthropic, Gemini, Mistral, Ollama, AWS Bedrock, etc.), tool integration, structured output, retrieval-augmented generation (RAG), and complex workflow orchestration. It provides a composable agent class that can operate in chat, stream, and structured output modes, a workflow engine with interrupt/resume and persistence, a built-in evaluation framework with LLM-based judges and assertions, and a Model Context Protocol (MCP) client for connecting to external tool servers. The architecture is modular: agents are built on top of workflows, tools are defined via PHP classes with JSON schema generation, and providers are abstracted behind a common interface with traits for shared logic.
Promptify is a Python library for performing NLP tasks using large language models (LLMs) with structured, type-safe outputs. It provides a scikit-learn-like interface where users instantiate task classes (e.g., NER, Classify, QA, Summarize) and call them with plain text. The library uses LiteLLM as a universal backend to support 100+ providers (OpenAI, Anthropic, Ollama, etc.), Pydantic v2 for output schemas, Jinja2 for prompt templates, and tenacity for retries. Architecture follows a clean separation: Task classes (promptify/tasks) build prompts via PromptBuilder, send them via LLMEngine (which wraps LiteLLM), and parse responses via a safe Parser that never uses eval(). The package also includes a built-in evaluation framework (promptify/eval) with metrics for precision, recall, F1, accuracy, exact match, and ROUGE, plus dataset loading utilities. The code is well-structured, typed, and includes comprehensive tests. The primary artifact is the "promptify" Python package, installable via pip. The repository is not a data research project; it is a software library for developers.