Framework Overview

Agno offers a lean, high-performance Python toolkit designed for constructing complex AI entities that possess perception, reasoning, and action capabilities. It emphasizes simplicity for the developer while supporting advanced features like persistent state and multi-sensory input.

1. Process inputs spanning text, imagery, sound, and motion data instantly.
2. Seamlessly incorporate durable memory, external factual bases, and external functions.
3. Completely open-source and operable across diverse runtime environments.

Engineering Philosophy: Code Over Configuration

We posit that crafting robust AI products relies predominantly on standard software engineering practices (the 80%), with agentic orchestration accounting for the remainder (the 20%). Agno is architected to complement conventional Python development.

Leverage familiar control flow structures (if, for, while) rather than wrestling with abstract graph definitions or restrictive chains. Below is a minimal example of an agent configured for web lookups:

python websearch_agent.py from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.duckduckgo import DuckDuckGoTools

coordinator = Agent( model=OpenAIChat(id="gpt-4o"), tools=[DuckDuckGoTools()], markdown=True ) coordinator.respond("What is the current status of the cloud services sector?", stream=True)

Core Capabilities

Agno prioritizes speed, flexibility, and broad model compatibility:

• Blazing Fast Startup: Agent initialization is orders of magnitude quicker than comparable graph-based systems (check Performance).
• Provider Neutral: Integrate any LLM or embedding service without vendor lock-in.
• Full Sensory Input: Native handling for heterogeneous data types (text, visual, audio, video).
• Collaborative Agents: Structure solutions using teams of specialized computational entities.
• State Persistence: Session history and context can be externally persisted using various databases.
• Grounded Reasoning (RAG): Connect agents to vector stores for Retrieval-Augmented Generation or sophisticated few-shot contexts.
• Deterministic Outputs: Enforce structured responses from agent actions.
• Observability: Real-time tracing of agent interactions and performance via our application dashboard.

Setup

shell pip install -U agno

Defining Agency

Agents are autonomous computational entities tasked with problem resolution. They dynamically decide upon action sequences—invoking functions, consulting knowledge, or accessing memory—to fulfill objectives, diverging from deterministic program paths.

We categorize their capability levels based on autonomy:

• Level 0: Pure inference engines (no external capabilities).
• Level 1: Tool-enabled agents for task execution.
• Level 2: Context-aware agents integrating memory and reasoning components.
• Level 3: Orchestrators managing collaborative sub-agent teams.

Example: Fundamental Agent

python from agno.agent import Agent from agno.models.openai import OpenAIChat

reporter = Agent( model=OpenAIChat(id="gpt-4o"), description="You are an enthusiastic news reporter with a flair for storytelling!", markdown=True ) reporter.respond("Deliver a compelling report on today's primary global event.", stream=True)

Set your API key and run the script:

shell pip install agno openai

export OPENAI_API_KEY=sk-xxxx

python reporter.py

Recipe in Cookbook

Example: Augmented Agent with Tools

To prevent factual fabrication, we equip the reporter with external search capabilities.

python from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.duckduckgo import DuckDuckGoTools

verified_reporter = Agent( model=OpenAIChat(id="gpt-4o"), description="You are an enthusiastic news reporter with a flair for storytelling!", tools=[DuckDuckGoTools()], trace_tool_calls=True, markdown=True ) verified_reporter.respond("Deliver a compelling, fact-checked report on today's primary global event.", stream=True)

Install necessary components and execute:

shell pip install duckduckgo-search

python verified_reporter.py

Verification should show the agent using the search utility.

Recipe in Cookbook

Example: Knowledge-Grounded Agent

Agents can access and reason over private or domain-specific data stored in vector databases (RAG implementation).

Agno employs Agentic RAG, prioritizing focused queries against the knowledge corpus before resorting to general search.

python from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.embedder.openai import OpenAIEmbedder from agno.tools.duckduckgo import DuckDuckGoTools from agno.knowledge.pdf_url import PDFUrlKnowledgeBase from agno.vectordb.lancedb import LanceDb, SearchType

cuisine_expert = Agent( model=OpenAIChat(id="gpt-4o"), description="You are a Thai cuisine expert!", instructions=[ "Consult your internal repository for Thai recipes.", "If the query requires contemporary information, utilize web search to supplement.", "Give precedence to data retrieved from the knowledge base." ], knowledge=PDFUrlKnowledgeBase( urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"], vector_db=LanceDb( uri="tmp/lancedb", table_name="recipes", search_type=SearchType.hybrid, embedder=OpenAIEmbedder(id="text-embedding-3-small"), ), ), tools=[DuckDuckGoTools()], trace_tool_calls=True, markdown=True )

Run this once to populate the vector store

if cuisine_expert.knowledge is not None: cuisine_expert.knowledge.ingest()

cuisine_expert.respond("Detail the steps for Tom Kha Gai preparation.", stream=True) cuisine_expert.respond("Outline the historical origins of Massaman curry.", stream=True)

Dependencies:

shell pip install lancedb tantivy pypdf duckduckgo-search

python cuisine_expert.py

Recipe in Cookbook

Example: Multi-Agent Workflows

Optimal performance is achieved when agents possess focused scopes and limited toolsets. For broader tasks requiring diverse capabilities, delegate to a coordinated team.

python from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.duckduckgo import DuckDuckGoTools from agno.tools.yfinance import YFinanceTools

search_unit = Agent( name="Information Retriever", role="Locate and synthesize general web data.", model=OpenAIChat(id="gpt-4o"), tools=[DuckDuckGoTools()], directives="Cite all utilized external links.", trace_tool_calls=True, markdown=True, )

finance_unit = Agent( name="Financial Analyst", role="Retrieve market data and company fundamentals.", model=OpenAIChat(id="gpt-4o"), tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)], directives="Present quantitative findings using markdown tables.", trace_tool_calls=True, markdown=True, )

market_orchestrator = Agent( team=[search_unit, finance_unit], model=OpenAIChat(id="gpt-4o"), directives=["Cite all utilized external links", "Present quantitative findings using markdown tables."], trace_tool_calls=True, markdown=True, )

market_orchestrator.respond("Assess the current market trajectory and recent performance of leading semiconductor firms in the AI sector.", stream=True)

Run the team deployment:

shell pip install duckduckgo-search yfinance

python market_orchestrator.py

Recipe in Cookbook

Performance Benchmarks

Our commitment to efficiency stems from the reality that complex AI systems often deploy thousands of agents concurrently. Agno is engineered to minimize overhead, thus preserving budget and responsiveness:

• Startup Latency: Average initialization time is approximately 2 microseconds (vastly exceeding LangGraph benchmarks).
• Memory Footprint: Average memory overhead is around 3.75 KiB per instance (approximately 50x reduction compared to LangGraph).

Benchmarks conducted on an Apple M4 MacBook Pro.

While inference time remains the primary bottleneck in any agent run, minimizing setup latency and memory usage is crucial for scaling agentic applications. These seemingly small optimizations yield significant cumulative savings.

Initialization Speed Comparison

Measuring the time required to instantiate an Agent equipped with a single function hook, averaged over 1000 trials for baseline accuracy.

We strongly encourage independent validation of these figures on your own hardware.

shell

Environment setup

./scripts/perf_setup.sh source .venvs/perfenv/bin/activate

Manual install alternative

pip install openai agno langgraph langchain_openai

Agno test

python evals/performance/instantiation_with_tool.py

LangGraph test

python evals/performance/other/langgraph_instantiation.py

Observation (M4 MBP): The Agno initialization completes while the LangGraph measurement is less than halfway complete, with no memory profiling initiated yet.

https://github.com/user-attachments/assets/ba466d45-75dd-45ac-917b-0a56c5742e23

Ratio calculation: 0.020526s / 0.000002s ≈ 10,263

Agno's startup speed demonstrates an approximate 10,000x advantage in this scenario. This gap widens as knowledge bases and toolsets are added.

Memory Overhead Analysis

Memory consumption is quantified using Python's tracemalloc, isolating the incremental allocation during 1000 successive Agent instantiations after establishing a null baseline.

Ratio calculation: 0.137273/0.002528 ≈ 54.3

LangGraph instances consume roughly 50 times the memory footprint of Agno Agents. In high-throughput production environments, memory efficiency directly correlates with operational cost, making this metric arguably more critical than startup time.

Summary

Agno agents are optimized for operational efficiency. While performance metrics are shared against competitors, our primary focus remains on result fidelity and stability. Future releases will include independent accuracy and reliability reports generated via CI/CD pipelines.

IDE Integration (Cursor)

Integrating Agno documentation directly into the Cursor IDE accelerates development cycles:

1. Navigate to Cursor's configuration/settings panel.
2. Locate the section for managing documentation sources.
3. Add https://docs.agno.com to the list of indexed URLs.
4. Save configuration.

Cursor can now leverage our official documentation for context-aware assistance.

Support Resources

• Documentation: docs.agno.com
• Initial Tutorials: Getting Started Cookbook
• Complete Code Examples: Full Cookbook
• Community Discussion: community.agno.com
• Real-time Chat: Discord Channel

Community Involvement

We encourage external contributions. Please review our contribution guidelines to begin.

Data Collection

By default, Agno logs the LLM provider/model identifier used by an agent to help prioritize framework maintenance. This telemetry is opt-out: set the environment variable AGNO_TELEMETRY=false to disable.

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WIKIPEDIA CONTEXT: Cloud computing is defined by ISO as "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand." This entire concept is commonly referred to as "the cloud".

== Core Principles (NIST 2011) == NIST established five fundamental characteristics necessary for cloud systems. These are:

• On-demand self-service: Users procure computing resources (e.g., server capacity, storage) autonomously without requiring manual intervention from the service vendor for each request.
• Ubiquitous network access: Services are accessible via standard protocols across diverse client hardware (mobile, desktop, etc.).
• Resource pooling: Provider resources are shared among numerous clients using a multi-tenant architecture, dynamically allocating capacity based on fluctuating demands.
• Rapid elasticity: The capacity to scale resources up or down swiftly, often automatically, matching demand precisely. To the end-user, this capacity appears virtually infinite.
• Measured service: Resource consumption (processing, bandwidth, storage) is automatically tracked, reported, and optimized, ensuring transparency for both provider and consumer.

ISO later refined and expanded this catalog by 2023.

== Genesis ==

The conceptual roots of cloud computing trace back to the 1960s with the introduction of time-sharing and remote job entry (RJE). The dominant model involved users submitting jobs to operators who ran them on centralized mainframes. This period was foundational in experimenting with methods to democratize access to large computational assets through optimized infrastructure and application layering. The specific 'cloud' visual metaphor for networked services originated around 1994, utilized by General Magic to describe the addressable 'space' for their mobile software agents in the Telescript framework. David Hoffman, a specialist at General Magic, is credited with popularizing this graphical convention, drawing from its existing use in telecommunications. The term "cloud computing" gained broader recognition in 1996 following Compaq Computer Corporation's internal planning documents regarding the future trajectory of the Internet and computation.