Frameworks manage the agent loop infrastructure — calling the LLM, detecting and executing tool calls, collecting results, and re-calling the model — so you can focus on behavior and tools rather than loop mechanics.
Tool definition — Standardized interfaces for registering tools with names, descriptions, and parameter schemas.
Memory and state — Persistent storage across steps and sessions; tracking what the agent has done and still needs to do.
Multi-agent coordination — Routing work between orchestrators and subagents; managing inter-agent communication.
Observability — Logging and tracing each step: what the model was prompted with, what it decided, what tool ran, what it returned, how long it took.
LangGraph — Graph-based multi-agent workflows. Highly flexible; can be complex to configure.
OpenAI Agents SDK — Opinionated, orchestrator-subagent pattern, tight OpenAI integration. Easy to start with.
AutoGen (Microsoft) — Multi-agent conversations via message passing. Good for dialogue-based agent collaboration.
CrewAI — High-level role/goal/backstory abstraction. Designed for rapid prototyping and readability.
Custom loop — A few hundred lines of Python. Most transparent, most debuggable, least boilerplate from the framework.
You need multi-agent coordination, built-in observability, or want to move quickly on a proof-of-concept. Frameworks provide these out of the box.
You need precise control, the framework's abstractions don't fit, or performance and transparency are critical. Start with a framework, then migrate if it becomes a liability.
Ask the AI assistant about agent frameworks, how to choose between them, or how to implement a custom agent loop.