# Research Log

## Wed Nov 26 03:23:35 PM PST 2025 Research Session

### Found: LangGraph
- **URL:** https://github.com/langchain-ai/langgraph
- **Description:** A library for building stateful, multi-actor applications with LLMs, built on top of LangChain. It uses a graph-based approach to orchestration.
- **Key Features:**
  - Graph-based workflow definition (nodes and edges).
  - Built-in state management and persistence.
  - Support for cycles (loops) in agent reasoning.
  - "Human-in-the-loop" capabilities.
- **Why Interesting:** It addresses the need for fine-grained control over agent loops and state, which is often difficult in chain-based frameworks.

### Found: CrewAI
- **URL:** https://github.com/crewAIInc/crewAI
- **Description:** A framework for orchestrating role-playing, autonomous AI agents. It focuses on collaborative intelligence where agents work together to solve tasks.
- **Key Features:**
  - Role-based agent design.
  - Inter-agent delegation and collaboration.
  - Integration with various LLM providers.
  - High-level abstraction for multi-agent teams.
- **Why Interesting:** It simplifies the creation of multi-agent systems by using a "crew" metaphor, making it accessible for building complex collaborative workflows.

### Found: Smolagents
- **URL:** https://github.com/huggingface/smolagents
- **Description:** A lightweight library from Hugging Face for building agents that "think in code".
- **Key Features:**
  - "CodeAgent" architecture where agents write Python code to perform actions.
  - Minimalistic and lightweight design.
  - Tight integration with Hugging Face Hub and inference.
  - Secure sandboxed execution environment mentions (needs verification in analysis).
- **Why Interesting:** The "code-as-reasoning" approach is gaining traction as a more robust alternative to JSON-based tool calling for complex logic.

### Found: PydanticAI
- **URL:** https://github.com/pydantic/pydantic-ai
- **Description:** A new agent framework from the Pydantic team, focusing on type safety and structured data.
- **Key Features:**
  - Heavily leverages Pydantic for validation and type safety.
  - Model-agnostic design.
  - Focus on production-grade reliability.
  - "Vigilant mode" and other safety features.
- **Why Interesting:** Bringing strong typing and validation (Pydantic's strength) to the often chaotic world of LLM outputs is a critical step for production engineering.

### Found: Bee Agent Framework
- **URL:** https://github.com/i-am-bee/beeai-framework
- **Description:** An open-source framework (formerly associated with IBM) for building production-ready scalable agent workflows.
- **Key Features:**
  - Support for Python and TypeScript.
  - Designed for scalability and robustness.
  - "Agent Stack" for deployment.
  - Native support for Granite and Llama 3.x models.
- **Why Interesting:** It targets the enterprise/production niche with a focus on scalability and multi-language support (TS/Python).
