AI BriefingOn Agent Frameworks and Agent Observability
Key point
As LLMs advance, agent frameworks are evolving from simple chaining to autonomous workflow orchestration.
Details
As LLM performance improves, the role of agent frameworks is also changing. Agents, as systems built around models, are not disappearing—they must evolve together with the advancement of models.
Agent patterns have evolved over the past 3 years from RAG, to workflow orchestration, and now to the current tool-calling-in-a-loop approach. Along with this, three generations of frameworks have emerged.
- LangChain: Gained popularity in 2023 as the easiest way to connect LLMs with data and APIs, though it initially had a somewhat rigid structure.
- LangGraph: Provides a flexible structure optimized for human-agent and agent-to-agent collaboration, including a runtime that supports statefulness and durability.
- DeepAgents: The latest agent harness, supporting long-horizon task planning, tool-calling loops, and file-system context offloading, with model-agnostic flexibility.
Additionally, the biggest barrier to applying agents in real-world services is quality control, for which purpose-built Agent Observability tools like LangSmith are essential. LangSmith is designed to work independently, regardless of which specific framework is being used.
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