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Agent systems don’t fail loudly, they fail slowly.
Tiny inefficiencies compound: a few extra milliseconds at instantiation, a little more memory per agent, one blocking call in the wrong place.
At small scale, it doesn’t matter. At agent scale, it matters a lot.
Performance is not a nice-to-have
Why is agent performance so important? Because at agent scale, overhead compounds instead of amortizes.
Agent workloads look nothing like traditional web services.
In traditional web services:
- Requests are short-lived
- Work finishes in milliseconds or seconds
- State is externalized (databases, caches)
- Processes are mostly idle, waiting for requests
- Horizontal scaling means adding stateless replicas
Sure, performance can feel like a “nice-to-have” when startup cost happens once, memory overhead is amortized, blocking calls are tolerable, and inefficiencies are masked by low concurrency.
But in modern agent workloads:
- Agents are created continuously
- Tasks are long-running
- Tool calls are frequent and execute in parallel
- Context and history are maintained over time
In agent systems, overhead isn’t amortized, it’s multiplied across every agent and every run. Latency becomes systemic, memory inefficiency blocks scale, coordination overhead kills throughput, and failures become slow and difficult to debug.
So, in this world, stateless, horizontally scalable design isn’t optional, it’s the baseline.
That’s why performance is one of Agno’s core design principles.
Three dimensions of agentic performance
Agno is optimized for agent workloads at scale, across three dimensions:
1. Agent performance
Agent overhead can compound quickly, so we make creating, running, and coordinating agents cheap enough that scale is practical.
Concretely, this shows up in a few key ways:
- Ultra-fast agent instantiation
- Small, predictable memory footprint
- Low-overhead tool calls
- Efficient history management
This keeps instantiation in the microsecond range, even with tools, storage, and memory updates involved.
2. System performance
Agent performance doesn’t exist in isolation. Agno is designed to keep the entire execution environment fast, lean, and concurrency-friendly.
In practice, this means:
- Async-first APIs for non-blocking execution
- Minimal memory usage to avoid hidden costs
- Parallel execution by default
- Background threads where they make sense
The goal isn’t theoretical throughput. It’s predictable, scalable behavior under real workloads.
3. Reliability and accuracy
Performance without reliability is meaningless, so Agno provides multiple dimensions for evaluating agent behavior so you can ensure speed doesn’t come at the cost of correctness.
Agno delivers best-in-class agent performance
All of our careful performance-first design work shows up under real workloads.
Agno delivers both the fastest agent instantiation and the lowest memory footprint among comparable frameworks. Take a look:
We benchmarked agent instantiation time and memory footprint for a simple agent with one tool, run 1,000 times:
Performance benchmarking: Apple M4 MacBook Pro, October 2025
With Agno you can run more agents, more predictably, at lower cost.
Run the benchmarks yourself
If you want to run these agent benchmarks on your own machine to inspect the results yourself, here's the code:
# Setup virtual environment
./scripts/perf_setup.sh
source .venvs/perfenv/bin/activate
# Run benchmarks
python cookbook/evals/performance/instantiate_agent_with_tool.py # Agno
python cookbook/evals/performance/comparison/langgraph_instantiation.py # LangGraph
python cookbook/evals/performance/comparison/crewai_instantiation.py # CrewAI
python cookbook/evals/performance/comparison/pydantic_ai_instantiation.py # PydanticAI
Different machines, Python versions, and environments will produce different absolute numbers. What matters is the relative overhead, where Agno consistently comes out on top.
Performance enables everything else
When agent systems are small, performance can feel secondary. As they grow, it becomes a hard constraint. Discovering performance limits late is costly. By the time they become visible, it’s often too late to fix them cheaply.
Performance matters to us because it matters to agent systems. This isn’t about bragging rights. It’s about making agent systems practical.