update

Author: ashpreetbedi

introduction.mdx

@@ -3,9 +3,11 @@ title: What is Agno
 sidebarTitle: What is Agno
 ---
 
-**Agno is a lightweight framework for building Agents with memory, knowledge, tools and reasoning.**
+**Agno is a lightweight library for building Reasoning Agents that have long-term memory, domain-specific knowledge, and can use tools to achieve tasks.**
 
-Use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows. Here's an Agent that writes a financial report by reasoning through each step:
+Developers use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows.
+
+Here's an Agent that researches and writes a report on a stock, reasoning through each step:
 
 ```python reasoning_finance_agent.py
 from agno.agent import Agent
@@ -28,34 +30,34 @@ agent = Agent(
 agent.print_response("Write a report on NVDA", stream=True, show_full_reasoning=True, stream_intermediate_steps=True)
 ```
 
-<Frame caption="The Reasoning Agent in action">
-<video
-  autoPlay
-  muted
-  controls
-  className="w-full aspect-video"
-  style={{ borderRadius: '8px' }}
-  src="/videos/reasoning_finance_agent_demo.mp4"
-></video>
+<Frame caption="Here's the Reasoning Agent in action">
+  <video
+    autoPlay
+    muted
+    controls
+    className="w-full aspect-video"
+    style={{ borderRadius: '8px' }}
+    src="/videos/reasoning_finance_agent_demo.mp4"
+  ></video>
 </Frame>
 
 # Key features
 
-Agno is simple, fast and model agnostic. Here are some key features:
+Agno is simple, fast and model-agnostic. Here are some key features:
 
-- **Model Agnostic**: Agno provides a unified interface for 23+ model providers, no lock-in.
-- **Lightning Fast**: Agents instantiate 10,000x faster than LangGraph and use 50x less memory (see [benchmarks](https://github.com/agno-agi/agno#performance)).
-- **First class support for Reasoning**: Build Agents that can "think" and "analyze" using Reasoning Models, Reasoning Tools or our custom `CoT+Tool-use` approach.
-- **Natively Multi Modal**: Agents can take in text, image, audio and video and generate text, image, audio and video as output.
-- **Advanced Multi Agent Architecture**: Industry leading multi-agent architecture with 3 different modes: `route`, `collaborate` and `coordinate`.
-- **Long-term Memory**: Built in support for long-term memory with our `Storage` and `Memory` classes.
-- **20+ Vector Databases for Knowledge**: Add domain knowledge to your Agents by integrating with 20+ vector databases. Fully async and highly performant.
-- **Structured Outputs**: First class support for structured outputs using native structured outputs or `json_mode`.
-- **Monitoring**: Track agent sessions and performance in real-time on [agno.com](https://app.agno.com).
+- **Model Agnostic**: Agno provides a unified interface to 23+ model providers, no lock-in.
+- **Lightning Fast**: Agents instantiate in **~2μs** on average (10,000x faster than LangGraph) and use **~3.75Kib** memory on average (50x less than LangGraph) (see [benchmarks](https://github.com/agno-agi/agno#performance)).
+- **Reasoning is a first class citizen**: Build Agents that can "think" and "analyze" using Reasoning Models, `ReasoningTools` or our custom `CoT+Tool-use` approach.
+- **Natively Multi Modal**: Agno Agents are natively multi modal, they can take in text, image, audio and video and generate text, image, audio and video as output.
+- **Advanced Multi Agent Architecture**: Agno provides an industry leading multi-agent architecture with 3 different modes: `route`, `collaborate` and `coordinate`.
+- **Long-term Memory & Session Storage**: Agno provides `Storage` & `Memory` classes to provide your Agents with long-term memory and session storage.
+- **20+ Vector Databases for Knowledge**: Add domain knowledge to your Agents by integrating with 20+ vector databases. **Fully async and highly performant.**
+- **Structured Outputs**: Agno Agents have first class support for structured outputs using native structured outputs or `json_mode`.
+- **Monitoring**: Monitor agent sessions and performance in real-time on [agno.com](https://app.agno.com).
 
 # Getting Started
 
-If you're new to Agno, start by building your first Agent.
+If you're new to Agno, start by building your [first Agent](/introduction/agents), then chat with it on the [Agent playground](/introduction/playground) and finally monitor it on [agno.com](/introduction/monitoring).
 
 <CardGroup cols={3}>
   <Card
@@ -88,7 +90,7 @@ After that, checkout the [Examples Gallery](/examples) and build real-world appl
 
 # Dive deeper
 
-Agno is a battle-tested framework with best-in-class performance, checkout the following guides to dive-in:
+Agno is a battle-tested framework with a state-of-the-art multi-agent architecture and ridiculous performance, checkout the following guides to dive-in:
 
 <CardGroup cols={3}>
   <Card title="Agents" icon="user-astronaut" iconType="duotone" href="/agents">

introduction/agents.mdx

@@ -4,20 +4,24 @@ title: Your first Agent
 
 ## What are Agents?
 
-**Agents** are intelligent programs that solve problems autonomously.
+**Agents** are AI programs that operate autonomously.
 
-Agents have memory, domain knowledge and the ability to use tools (like searching the web, querying a database, making API calls). Unlike traditional programs that follow a predefined execution path, Agents dynamically adapt their approach based on the context and tool results.
+- The **brain** of an Agent is a model that it uses to reason, execute, and respond to the user.
+- The **body** of an Agent is the tools it uses to interact with the real world.
+- The **behavior** of an Agent is defined by instructions; the better the model, the better it is at following instructions.
 
-Instead of a rigid binary definition, let's think of Agents in terms of agency and autonomy.
+Agents also have **memory**, **knowledge**, **storage** and the ability to **reason**:
 
-- **Level 0**: Agents with no tools (basic inference tasks).
-- **Level 1**: Agents with tools for autonomous task execution.
-- **Level 2**: Agents with knowledge, combining memory and reasoning.
-- **Level 3**: Teams of specialized agents collaborating on complex workflows.
+- **knowledge:** is domain-specific information the Agent can **_search on demand_** to make better decisions and provide accurate responses. Knowledge is stored in a vector database and the **_search on demand_** pattern is known as Agentic RAG.
+- **storage:** is used by Agents to save session history and state in a database. Model APIs are stateless and storage enables multi-turn conversations by making Agents stateful.
+- **memory:** gives Agents the ability to store and recall users information from previous interactions, allowing them to learn user preferences and personalize their responses.
+- **reasoning:** enables Agents to "think" before responding and "analyze" the results of their actions (i.e. tool calls), this greatly improves the Agents capabilities.
+
+<Check>Let's build a few Agents to see how they work.</Check>
 
 ## Basic Agent
 
-The simplest Agent is just an inference task, no tools, no memory, no knowledge.
+The simplest Agent is just an inference task, no reasoning, tools, memory or knowledge.
 
 ```python basic_agent.py
 from agno.agent import Agent