introduction

Author: ashpreetbedi

agents/memory.mdx

@@ -2,15 +2,22 @@
 title: Memory
 ---
 
-Agno provides 3 types of memory for Agents:
+If you're building intelligent agents, you need to give them memory which is the ability to **remember**, **reason** and **personalize** responses to users. Memory comes in 3 shapes:
 
-1. **Chat History:** The message history of the session. Agno will store the sessions in a database for you, and retrieve them when you resume a session.
-2. **User Memories:** Notes and insights about the user, this helps the model personalize the response to the user.
-3. **Summaries:** A summary of the conversation, which is added to the prompt when chat history gets too long.
+1. **Chat History:** This is the current conversation between users and the agent, stored as sessions in chronological order. This is the most basic form of memory and **called "Storage" in Agno**.
+2. **User Memories:** Insights and facts about users extracted from conversations, helping agents personalize their responses to users. Think of this as adding a "ChatGPT like memory" to your agent. **This is called "Memory" in Agno**.
+3. **Session Summaries:** Condensed representations of conversations, useful when chat histories grow too long. **This is called "Summary" in Agno**.
+
+Memory helps Agents:
+ - Manage conversation state (chat history)
+ - Personalize responses to users (user memories)
+ - Maintain long-session context (session summaries)
+
+Memory is critical for personal assistants, it allows Agents to "Remember" and "Personalize" their responses to users.
 
 ## Built-in Memory
 
-Every Agent comes with built-in memory that can be used to access the historical **runs** per session. 
+Every Agent comes with built-in memory that can be used to access the historical **runs** per session.
 
 You can give your agent access to chat history in the following ways:
 
@@ -256,9 +263,9 @@ To enable session summaries, set `enable_session_summaries=True` on the `Agent`.
         )
 
         agent.print_response(
-            "I would also like to know about LLMs?", 
-            stream=True, 
-            user_id=user_id, 
+            "I would also like to know about LLMs?",
+            stream=True,
+            user_id=user_id,
             session_id=session_id
         )
 
@@ -292,7 +299,7 @@ To enable session summaries, set `enable_session_summaries=True` on the `Agent`.
 
 ## Agentic Memory Management
 
-You can also enable an agent to manage the user memories for you. Enable agentic memory management by setting `enable_agentic_memory=True` on the `Agent`. 
+You can also enable an agent to manage the user memories for you. Enable agentic memory management by setting `enable_agentic_memory=True` on the `Agent`.
 
 <Note>
 Enabling agentic memory will also add all existing user memories to the agent's system prompt.

agents/teams.mdx

@@ -1,9 +1,12 @@
 ---
-title: Teams
+title: Agent Teams [Deprecated]
 ---
-<Warning>
-  `Agent.team` is deprecated. We suggest using our [Team](/teams) feature instead, which is much more flexible and powerful.
-</Warning>
+
+<Note>
+  Agent Teams was an initial implementation of our multi-agent architecture (2023-2025) that uses a transfer/handoff mechanism. After 2 years of experimentation, we've learned that this mechanism is not scalable and is not the best way to build multi-agent systems.
+
+  With our learning over 2 years, we released a new multi-agent reasoning architecture in 2025, please use the new [Teams](/teams) architecture instead.
+</Note>
 
 We can combine multiple Agents to form a team and tackle tasks as a cohesive unit. Here's a simple example that converts an agent into a team to write an article about the top stories on hackernews.
 

introduction.mdx

@@ -5,7 +5,7 @@ sidebarTitle: What is Agno
 
 **Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.**
 
-Developers use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows. Agno provides a beautiful UI to chat with Agents and tools to monitor and evaluate their performance.
+Developers use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows. Agno also provides a beautiful UI to chat with Agents and tools to monitor and evaluate their performance.
 
 Here's an Agent that researches and writes a report on a stock, reasoning through each step:
 

memory/introduction.mdx

@@ -6,7 +6,7 @@ title: Introduction
 
 If you're building intelligent agents, you need to give them memory which is the ability to **remember**, **reason** and **personalize** responses to users. Memory comes in 3 shapes:
 
-1. **Chat History:** The current conversation between users and the agent, stored as sessions in chronological order. This is the most basic form of memory and **called "Storage" in Agno**.
+1. **Chat History:** This is the current conversation between users and the agent, stored as sessions in chronological order. This is the most basic form of memory and **called "Storage" in Agno**.
 2. **User Memories:** Insights and facts about users extracted from conversations, helping agents personalize their responses to users. Think of this as adding a "ChatGPT like memory" to your agent. **This is called "Memory" in Agno**.
 3. **Session Summaries:** Condensed representations of conversations, useful when chat histories grow too long. **This is called "Summary" in Agno**.
 
@@ -15,7 +15,7 @@ Memory helps Agents:
  - Personalize responses to users (user memories)
  - Maintain long-session context (session summaries)
 
-Not only that, Agents can search over chat history and user memories to reason over time. This is critical for intelligence, it allows Agents to "Remember" and "Personalize" their responses to users. Learn more about agent memory [here](/agents/memory) and about managing sessions [here](/agents/sessions).
+Memory is critical for personal assistants, it allows Agents to "Remember" and "Personalize" their responses to users. Learn more about agent memory [here](/agents/memory) and about managing sessions [here](/agents/sessions).
 
 ## Memory Architecture