CoexistAI is a research assistant framework implemented as a FastAPI application that provides a suite of API endpoints for web search, YouTube and Reddit retrieval, geospatial mapping, git repository analysis, text-to-speech/podcast generation, and knowledge base creation from local files or crawled websites. It integrates with an MCP server for agentic workflows and supports pluggable LLMs (Google, OpenAI, Ollama, local) and embedders (Google, HuggingFace, Infinity). The architecture is monolithic with modular utility functions, containerized via Docker with a companion SearxNG instance for privacy-respecting search. The codebase demonstrates a hybrid nature: while primarily an application with explicit API entry points, its core purpose is to support research and data-gathering tasks, making it equally relevant for data research scenarios. Configuration is centralized, and the system includes a web-based admin UI for runtime parameter changes. Active development is evident from recent commits and numerous utility modules, though some global mutable state and fallback-heavy code indicate where the system has evolved rapidly.
This is an educational repository of Python scripts, mini-projects, and markdown guides for building LLM-based AI agents applied to cybersecurity. The code is organized into five progressive parts: foundational LLM/embeddings usage, prompt templates, prompt chaining, RAG over security data, and agentic workflows. It demonstrates multiple agent frameworks (LangChain, LangGraph, CrewAI, DeepAgents) and MCP server integrations for tools like nmap and Shodan. The dominant character is application/tutorial code rather than data research, although it bundles small datasets (CVE embeddings, sample RAG corpora) and commits Chroma vector stores for offline exercises. The repo is not a single deployable service; it is a collection of entry-point scripts and small frameworks. Architecture is a set of standalone examples with shared patterns: state graphs for orchestration, YAML-configured crews, MCP adapters, and RAG ingestion/query pipelines. Most scripts require OpenAI-compatible API keys and/or local tools such as nmap/Ollama. Simulated data and placeholder responses in some examples make it a learning environment rather than production security tooling. Maintenance is current but drift is moderate: some examples use deprecated langchain vectorstore imports, documentation is duplicated across subdirectories, several referenced files are missing, and hardcoded model strings like gpt-5.4-mini appear dated or fictional. There is no root-level automated test suite; only the Shodan MCP subproject contains tests. The strongest reusable patterns are the MCP server wrapping, LangGraph conditional-state orchestration, CrewAI declarative crew configuration, and the hierarchical SOC agent supervisor.
This repository is an educational tutorial for learning LangChain, LangGraph, and Deep Agents frameworks. It contains two tracks: 101 (fundamentals) and 201 (production patterns) with Jupyter notebooks and standalone agents runnable via `langgraph dev`. The code demonstrates multi-agent supervision, human-in-the-loop interrupts, middleware hooks, MCP tool integration, and a deep research agent with skills and memory. All agents use mock or simulated data (Chinook SQLite, Tavily search) and are designed for learning rather than production. The architecture centers on a centralized model configuration (`utils/models.py`) that supports multiple LLM providers, and reusable patterns like subgraph composition, dynamic prompts, and composite backends. While the code is educational and lacks production hardening (error handling, testing, security), it provides clear, well-commented examples of advanced agent patterns.
This repository is a comprehensive educational resource for learning and building AI agents using the LangGraph framework. It consists of numerous tutorials and examples organized by chapter, covering fundamental concepts such as state graphs and tool usage, as well as advanced patterns including Retrieval-Augmented Generation (RAG), multi-agent supervisor systems, and human-in-the-loop workflows. Each lesson is designed as a runnable script that demonstrates specific LangGraph integrations with various APIs (e.g., OpenAI, Tavily, OpenWeatherMap) and techniques like vector stores with Chroma. While the repository serves its purpose as a learning tool, it exhibits some maintenance challenges due to code duplication (e.g., multiple copies of display_graph.py across chapters) and contains at least one known flawed implementation (the audit data monitor in chapter13) that suffers from schema mismatches and inefficient API usage, as indicated by log files.