Comparison
These benchmarks compare agent instantiation patterns across non-Agno frameworks.
PerformanceEval runs the callable separately for each enabled metric: warm-ups first, then runtime measurements, then memory measurements. num_iterations applies to each metric, and the default is 10 additional warm-up calls. Model retries, tools, delegation, and memory extraction can add provider requests beyond the callable count.
Memory measurements use Python’s tracemalloc; they do not measure process RSS, GPU memory, database-server memory, or remote model memory. Record dependency versions, database state, and model settings when comparing results.
| Example | Description |
|---|---|
| Autogen Instantiation | Demonstrates agent instantiation benchmarking with AutoGen. |
| Crewai Instantiation | Demonstrates agent instantiation benchmarking with CrewAI. |
| Langgraph Instantiation | Demonstrates agent instantiation benchmarking with LangGraph. |
| Openai Agents Instantiation | Demonstrates agent instantiation benchmarking with OpenAI Agents SDK. |
| Pydantic Ai Instantiation | Demonstrates agent instantiation benchmarking with PydanticAI. |
| Smolagents Instantiation | Demonstrates agent instantiation benchmarking with Smolagents. |