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Summary
LangWatch author Rogerio Chaves evaluates 8 AI agent frameworks by implementing the same customer support agent in each. The ranking favors Agno for production use (clean API, built-in memory), DSPy for eval-driven performance, and LangGraph for graph-based stateful workflows. Google ADK and Smolagents are promising but not production-ready. The “no framework” approach is recommended for learning fundamentals.
透過實作同一個客服 Agent，比較 LangGraph、DSPy、Google ADK、InspectAI、PydanticAI、Agno、Smolagents 與無框架八種方案。Agno 適合生產環境，DSPy 適合追求效能，LangGraph 適合有狀態工作流程。
Key Points
- LangGraph: graph state + functional API + OpenAI compatibility — powerful but fragmented docs and heavy LangChain imports
- DSPy: hides prompts, optimizes for eval performance — fast outputs but hard to debug, not OpenAI-compatible
- Google ADK: ambitious Rails-like vision, but buggy silent failures and incomplete docs in current state
- InspectAI: evals-first, clean functional API — ideal for benchmarking, not production deployment
- PydanticAI: type-safe, minimal — awkward tool decorator binding and manual flow control limit ergonomics
- Agno: best docs, built-in session memory, consistent API — requires manual
json.dumpsfor tool outputs - Smolagents: open-model friendly, built-in tracing — poor documentation discoverability
- No framework: best for understanding internals, minimum viable agent with litellm + JSON parsing
Insights
The key differentiator between frameworks is how they handle memory and state: LangGraph makes state explicit as graph nodes, Agno treats it as session semantics, DSPy hides it entirely. Teams should pick based on whether they need debuggability (LangGraph/Agno) or raw performance (DSPy).
The “no framework” recommendation for learning is underrated. Understanding how tool-calling, memory, and loop control work at the raw API level prevents a class of bugs that only appear when a framework’s abstractions leak — which they always eventually do in production.
DSPy’s “don’t show the prompt” philosophy is a fundamental philosophical difference from other frameworks. It trades transparency for performance optimization, which is the right tradeoff for research but wrong for most enterprise production systems where auditability matters.
Connections
Raw Excerpt
The choice of AI agent framework should be guided by use case complexity, team experience, observability needs, and performance goals. New agentic use cases demand both speed and reliability.