Agno’s open-source framework is free under the Apache 2.0 license, while its managed AgentOS control plane starts at $150 per month. For a production deployment running a multi-agent research team—say 5,000 runs per day where each run involves instantiating three specialized agents—Agno’s microsecond instantiation time eliminates the latency overhead found in graph-based alternatives. In a standard Python environment, the compute cost for framework overhead is effectively zero, whereas a graph-heavy framework can add 200–500ms of initialization per request. If you are running these on AWS Lambda, that latency difference translates to roughly $45/month in additional execution costs just for the framework to wake up.
Architecturally, Agno (formerly Phidata) is built for developers who find LangGraph’s state machines and CrewAI’s abstractions too restrictive. It treats agents as standard Python objects. You don’t have to learn a domain-specific language (DSL) or map out a directed acyclic graph to get agents to talk to each other. This makes debugging significantly easier because the stack trace looks like normal Python code rather than a complex traversal of a hidden state manager. The framework is the engine of a car stripped down to the chassis—it’s built for speed and direct control.
Native multimodal support is where Agno currently leads the pack. While other frameworks treat images or video as secondary inputs to be parsed into text, Agno’s agents handle audio, video, and image inputs natively within the agent class. If you are building a video analysis tool that processes 100 clips a day, Agno allows you to swap between OpenAI, Anthropic, or local models like Ollama without rewriting the tool-calling logic. The built-in storage providers (PostgreSQL, MongoDB) and knowledge bases (Pinecone, Qdrant) are pre-integrated, meaning you can stand up a RAG-enabled agent in under 50 lines of code.
One plain downside is the rebranding. The transition from Phidata to Agno has left the ecosystem in a state of flux. You will frequently find documentation that refers to the old name, and community forum solutions from six months ago often require manual translation to the new API. Additionally, while the core framework is lean, the $150/month price point for the managed AgentOS is high for individual developers who just want a basic monitoring dashboard.
Use Agno if you are building performance-sensitive, multimodal agent teams and want to stay within standard Python patterns. Avoid it if you need the deep, community-vetted workflow templates found in the LangChain ecosystem. If your project requires complex, cyclic logic with strict state persistence across long-running sessions, LangGraph remains the more robust, albeit slower, choice.
Pricing
The core Agno SDK is open-source and free for local development and production. The 'cost cliff' appears when you want a hosted control plane for monitoring and agent management; Agno's AgentOS starts at $150/month. This is significantly more expensive than competitors like LangSmith, which offers a generous free tier for tracing. For a small team of three developers, Agno's managed layer costs $5,400 annually, whereas self-hosting a basic monitoring stack with OpenTelemetry and Arize Phoenix is essentially free. The value proposition for the $150 tier is purely 'convenience-as-a-service.' If you don't need their specific dashboard, you can run the entire framework on a $5/month VPS without hitting any functional limits.
Technical Verdict
The SDK is highly idiomatic Python with minimal boilerplate. You can initialize a functioning agent with a search tool in about 12 lines of code. Latency is the primary selling point; agent instantiation is roughly 5,000x faster than graph-based frameworks because it avoids the overhead of validating a state machine before execution. Documentation is currently fragmented due to the Phidata-to-Agno rebrand, but the type hinting in the library is excellent, making the code largely self-documenting for IDEs like VS Code or PyCharm. Integration with PostgreSQL for session memory is handled via a single class import, reducing friction for stateful apps.
Quick Start
# pip install agno openai
from agno.agent import Agent
from agno.models.openai import OpenAIChat
agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
agent.print_response("Explain quantum entanglement in one sentence.")Watch Out
- Old documentation under the 'Phidata' name often appears in search results and contains deprecated class structures.
- The local AgentOS dashboard requires a Docker installation, making it less 'portable' than the core library.
- The RAG knowledge base expects very specific PDF/CSV formatting that can cause silent failures if metadata is missing.
- Managed AgentOS pricing is per-user, which scales poorly for large engineering teams compared to flat-fee or usage-based tools.