fix: updated the multi-agent researcher to use teams in Agno

advanced_ai_agents/multi_agent_apps/multi_agent_researcher/README.md

@@ -31,13 +31,17 @@ streamlit run research_agent.py
 ### How it works?
 
 - Upon running the app, you will be prompted to enter your OpenAI API key. This key is used to authenticate and access the OpenAI language models.
-- Once you provide a valid API key, three instances of the Assistant class are created:
+- Once you provide a valid API key, three specialized AI agents are created:
-    - **story_researcher**: Specializes in researching HackerNews stories.
+    - **HackerNews Researcher**: Specializes in getting top stories from HackerNews using the HackerNews API.
-    - **user_researcher**: Focuses on researching HackerNews users and reading articles from URLs.
+    - **Web Searcher**: Searches the web for additional information on topics using DuckDuckGo search.
-    - **hn_assistant**: A team assistant that coordinates the research efforts of the story and user researchers.
+    - **Article Reader**: Reads and extracts content from article URLs using newspaper4k tools.
 
+- These agents work together as a coordinated team under the **HackerNews Team** which orchestrates the research process.
 - Enter your research query in the provided text input field. This could be a topic, keyword, or specific question related to HackerNews stories or users.
-- The hn_assistant will orchestrate the research process by delegating tasks to the story_researcher and user_researcher based on your query.
+- The HackerNews Team follows a structured workflow:
-- The AI assistants will gather relevant information from HackerNews using the provided tools and generate a comprehensive response using the GPT-4 language model.
+    1. First searches HackerNews for relevant stories based on your query
-- The generated content, which could be a blog post, report, or social media post, will be displayed in the app for you to review and use.
+    2. Uses the Article Reader to extract detailed content from the story URLs
+    3. Leverages the Web Searcher to gather additional context and information
+    4. Finally provides a thoughtful and engaging summary with title, summary, and reference links
+- The generated content is structured as an Article with a title, summary, and reference links for easy review and use.
 

advanced_ai_agents/multi_agent_apps/multi_agent_researcher/research_agent.py

@@ -1,8 +1,14 @@
 # Import the required libraries
 import streamlit as st
 from agno.agent import Agent
-from agno.tools.hackernews import HackerNewsTools
 from agno.models.openai import OpenAIChat
+from agno.team import Team
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.tools.hackernews import HackerNewsTools
+from agno.tools.newspaper4k import Newspaper4kTools
+from pydantic import BaseModel
+from typing import List         
+import os
 
 # Set up the Streamlit app
 st.title("Multi-Agent AI Researcher 🔍🤖")
@@ -10,30 +16,47 @@ st.caption("This app allows you to research top stories and users on HackerNews
 
 # Get OpenAI API key from user
 openai_api_key = st.text_input("OpenAI API Key", type="password")
+os.environ["OPENAI_API_KEY"] = openai_api_key
 
 if openai_api_key:
-    # Create instances of the Assistant
+    hn_researcher = Agent(
-    story_researcher = Agent(
+        name="HackerNews Researcher",
-        name="HackerNews Story Researcher",
+        model=OpenAIChat(id="gpt-4o-mini"),
-        role="Researches hackernews stories and users.",
+        role="Gets top stories from hackernews.",
         tools=[HackerNewsTools()],
     )
 
-    user_researcher = Agent(
+    web_searcher = Agent(
-        name="HackerNews User Researcher",
+        name="Web Searcher",
+        model=OpenAIChat(id="gpt-4o-mini"),
+        role="Searches the web for information on a topic",
+        tools=[DuckDuckGoTools()],
+        add_datetime_to_instructions=True,
+    )
+
+    article_reader = Agent(
+        name="Article Reader",
+        model=OpenAIChat(id="gpt-4o-mini"),
         role="Reads articles from URLs.",
-        tools=[HackerNewsTools()],
+        tools=[Newspaper4kTools()],
     )
 
-    hn_assistant = Agent(
+    hackernews_team = Team(
-        name="Hackernews Team",
+        name="HackerNews Team",
-        team=[story_researcher, user_researcher],
+        mode="coordinate",
-        model=OpenAIChat(
+        model=OpenAIChat(id="gpt-4o-mini"),
-            id="gpt-4o",
+        members=[hn_researcher, web_searcher, article_reader],
-            max_tokens=1024,
+        instructions=[
-            temperature=0.5,
+            "First, search hackernews for what the user is asking about.",
-            api_key=openai_api_key
+            "Then, ask the article reader to read the links for the stories to get more information.",
-        )
+            "Important: you must provide the article reader with the links to read.",
+            "Then, ask the web searcher to search for each story to get more information.",
+            "Finally, provide a thoughtful and engaging summary.",
+        ],
+        show_tool_calls=True,
+        markdown=True,
+        debug_mode=True,
+        show_members_responses=True,
     )
 
     # Input field for the report query
@@ -41,5 +64,5 @@ if openai_api_key:
 
     if query:
         # Get the response from the assistant
-        response = hn_assistant.run(query, stream=False)
+        response = hackernews_team.run(query, stream=False)
         st.write(response.content)

advanced_ai_agents/multi_agent_apps/multi_agent_researcher/research_agent_llama3.py

@@ -1,32 +1,55 @@
 # Import the required libraries
 import streamlit as st
 from agno.agent import Agent
-from agno.tools.hackernews import HackerNews
+from agno.team import Team
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.tools.hackernews import HackerNewsTools
+from agno.tools.newspaper4k import Newspaper4kTools
 from agno.models.ollama import Ollama
 
 # Set up the Streamlit app
 st.title("Multi-Agent AI Researcher using Llama-3 🔍🤖")
 st.caption("This app allows you to research top stories and users on HackerNews and write blogs, reports and social posts.")
 
-# Create instances of the Assistant
+# Create the specialized agents
-story_researcher = Agent(
+hn_researcher = Agent(
-    name="HackerNews Story Researcher",
+    name="HackerNews Researcher",
-    role="Researches hackernews stories and users.",
+    model=Ollama(id="llama3.2", max_tokens=1024),
-    tools=[HackerNews()],
+    role="Gets top stories from hackernews.",
-    model=Ollama(id="llama3.2", max_tokens=1024)
+    tools=[HackerNewsTools()],
 )
 
-user_researcher = Agent(
+web_searcher = Agent(
-    name="HackerNews User Researcher",
+    name="Web Searcher",
+    model=Ollama(id="llama3.2", max_tokens=1024),
+    role="Searches the web for information on a topic",
+    tools=[DuckDuckGoTools()],
+    add_datetime_to_instructions=True,
+)
+
+article_reader = Agent(
+    name="Article Reader",
+    model=Ollama(id="llama3.2", max_tokens=1024),
     role="Reads articles from URLs.",
-    tools=[HackerNews()],
+    tools=[Newspaper4kTools()],
-    model=Ollama(id="llama3.2", max_tokens=1024)
 )
 
-hn_assistant = Agent(
+hackernews_team = Team(
-    name="Hackernews Team",
+    name="HackerNews Team",
-    team=[story_researcher, user_researcher],
+    mode="coordinate",
-    model=Ollama(id="llama3.2", max_tokens=1024)
+    model=Ollama(id="llama3.2", max_tokens=1024),
+    members=[hn_researcher, web_searcher, article_reader],
+    instructions=[
+        "First, search hackernews for what the user is asking about.",
+        "Then, ask the article reader to read the links for the stories to get more information.",
+        "Important: you must provide the article reader with the links to read.",
+        "Then, ask the web searcher to search for each story to get more information.",
+        "Finally, provide a thoughtful and engaging summary.",
+    ],
+    show_tool_calls=True,
+    markdown=True,
+    debug_mode=True,
+    show_members_responses=True,
 )
 
 # Input field for the report query
@@ -34,5 +57,5 @@ query = st.text_input("Enter your report query")
 
 if query:
     # Get the response from the assistant
-    response = hn_assistant.run(query, stream=False)
+    response = hackernews_team.run(query, stream=False)
     st.write(response.content)