A side-by-side of two agent frameworks for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: Agno leads LangGraph vs Agno by community traction (★ 42k vs ★ 40k). Pick LangGraph for stateful multi-agent workflows; pick Agno for full-stack agents.
✓ Live data verified
| LangGraph | Agno | |
|---|---|---|
| GitHub stars | ★ 40k | ★ 42k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | stateful multi-agent workflows | full-stack agents |
| Repository | langchain-ai/langgraph | agno-agi/agno |
LangGraph and Agno are both credible choices. By community traction, Agno leads (★ 42k). Pick LangGraph for stateful multi-agent workflows; pick Agno for full-stack agents.
Both are credible agent frameworks. By community traction Agno leads (★ 42k). Pick LangGraph for stateful multi-agent workflows; pick Agno for full-stack agents.
LangGraph is Graph-based orchestration for stateful, multi-actor agent workflows with explicit control flow and checkpointing.. Agno is Batteries-included agent runtime — built-in memory, knowledge/RAG, and AgentOS, a control plane you run in your own cloud. Formerly Phidata..
Agno has more — ★ 42k vs ★ 40k (live counts).
Often yes — many teams combine agent frameworks. Check each tool's docs for interop; they solve overlapping but not identical problems.
LangGraph is primarily Python; Agno is primarily Python.
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