LangChain

• One of the most widely used frameworks for connecting LLMs with tools, memory, APIs, and workflows.
• Huge ecosystem and integrations (vector DBs, external services).
• Best as the foundation layer you build on. (Index.dev)

LangGraph

• Built on LangChain, uses graph/state‑machine workflows to manage complex, multi‑step agent logic.
• Good for agents that need branching, retries, or structured execution paths.
• Often paired with LangChain for robust workflow orchestration. (DataCamp)

Semantic Kernel

• SDK from Microsoft emphasizing skills, planners, memory, and cross‑platform support (Python, C#, Java).
• Useful for enterprise‑grade solutions with integration needs.
• Great if you’re in a Microsoft ecosystem. (JPLoft)

LlamaIndex

• Not strictly a full agent runtime, but a powerful toolkit for data/RAG-centric agents: indexing, retrieval, and grounding agents in real content.
• Excellent for knowledge‑intensive assistants. (dailybitsbyai.com)

AutoGen (Microsoft)

• Strong support for multi‑agent orchestration and async communication.
• Useful for projects requiring specialized agent roles (planner, coder, reviewer). (DataCamp)

AutoGPT

• One of the earliest autonomous agent frameworks. Automatically decomposes goals into subtasks.
• Great for experimentation and understanding autonomous workflows.
• Simpler and less structured — good for learning/prototyping. (LinkedIn)

CrewAI

• Focuses on role‑based agent “crews” that collaborate on tasks.
• Easier to start with than some graph‑based frameworks. (DataCamp)

Phidata / Agno

• Clean Python APIs and built‑in features for launching multi‑agent systems.
• Ideal if you want a fast prototype or a lightweight orchestration layer. (DataCamp)

Letta

• Focused on stateful agents with memory and long‑term context handling.
• Often considered beginner‑friendly for building agents with persistent state. (Codecademy)

DSPy

• A Pythonic DSL for defining prompt flows and agent logic declaratively.
• Efficient for structured prompt pipelines. (Geek Bacon)

Smolagents (Hugging Face)

• Ultra‑lightweight, code‑centric agent framework for building minimal agents quickly.
• Good for hackathons or learning how agents operate under the hood. (jacoblog.com)

Pydantic AI

• Adds type safety and structured I/O to agent workflows — helpful for correctness and debugging.
• Works particularly well with Python developers who like fast, validated agent logic. (Bright Data)

OpenAI Agents (SDK)

• Not in your list, but worth mentioning: native framework from OpenAI for building agent behaviors with memory and tools.
• Best for teams already committed to the OpenAI ecosystem. (Reddit)

Beginner / Prototyping

• Start with AutoGPT, CrewAI, or Smolagents — easy to try.
• Letta or DSPy if you want simple workflows with persistent state. (Codecademy)

General Agents & Apps

• Use LangChain as the backbone.
• Add LlamaIndex for data‑centric agents.
• Introduce LangGraph when workflows become complex. (DataCamp)

Multi‑Agent / Enterprise

• AutoGen or Semantic Kernel for robust orchestration.
• Combine frameworks (e.g., LangChain + Semantic Kernel for enterprise, or LangGraph + PydanticAI for type‑safe workflows). (dailybitsbyai.com)