Merge branch 'main' into release/v2.2.6

Author: kausmeows

cookbook/agent_os/agent_with_input_schema.py

@@ -0,0 +1,42 @@
+from typing import List
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.os import AgentOS
+from agno.tools.hackernews import HackerNewsTools
+from pydantic import BaseModel, Field
+from agno.db.sqlite import SqliteDb
+
+
+class ResearchTopic(BaseModel):
+    """Structured research topic with specific requirements"""
+
+    topic: str
+    focus_areas: List[str] = Field(description="Specific areas to focus on")
+    target_audience: str = Field(description="Who this research is for")
+    sources_required: int = Field(description="Number of sources needed", default=5)
+
+
+# Define agents
+hackernews_agent = Agent(
+    name="Hackernews Agent",
+    model=OpenAIChat(id="gpt-4o-mini"),
+    tools=[HackerNewsTools()],
+    role="Extract key insights and content from Hackernews posts",
+    input_schema=ResearchTopic,
+    db=SqliteDb(
+        session_table="agent_session",
+        db_file="tmp/agent.db",
+    ),
+)
+
+
+agent_os = AgentOS(
+    id="agentos-demo",
+    agents=[hackernews_agent],
+)
+app = agent_os.get_app()
+
+
+if __name__ == "__main__":
+    agent_os.serve(app="agent_with_input_schema:app", port=7777)

cookbook/agent_os/team_with_input_schema.py

@@ -0,0 +1,92 @@
+"""
+This example demonstrates how to use input_schema with teams for automatic
+input validation and structured data handling.
+
+The input_schema feature allows teams to automatically validate and convert
+dictionary inputs into Pydantic models, ensuring type safety and data validation.
+"""
+
+from typing import List
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.os import AgentOS
+from agno.team.team import Team
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.tools.hackernews import HackerNewsTools
+from pydantic import BaseModel, Field
+from agno.db.sqlite import SqliteDb
+
+class ResearchProject(BaseModel):
+    """Structured research project with validation requirements."""
+
+    project_name: str = Field(description="Name of the research project")
+    research_topics: List[str] = Field(
+        description="List of topics to research", min_length=1
+    )
+    target_audience: str = Field(description="Intended audience for the research")
+    depth_level: str = Field(
+        description="Research depth level", pattern="^(basic|intermediate|advanced)$"
+    )
+    max_sources: int = Field(
+        description="Maximum number of sources to use", default=10
+    )
+    include_recent_only: bool = Field(
+        description="Whether to focus only on recent sources", default=True
+    )
+
+
+# Create research agents
+hackernews_agent = Agent(
+    name="HackerNews Researcher",
+    model=OpenAIChat(id="o3-mini"),
+    tools=[HackerNewsTools()],
+    role="Research trending topics and discussions on HackerNews",
+    instructions=[
+        "Search for relevant discussions and articles",
+        "Focus on high-quality posts with good engagement",
+        "Extract key insights and technical details",
+    ],
+    db=SqliteDb(
+        session_table="team_session",
+        db_file="tmp/team.db",
+    ),
+)
+
+web_researcher = Agent(
+    name="Web Researcher",
+    model=OpenAIChat(id="o3-mini"),
+    tools=[DuckDuckGoTools()],
+    role="Conduct comprehensive web research",
+    instructions=[
+        "Search for authoritative sources and documentation",
+        "Find recent articles and blog posts",
+        "Gather diverse perspectives on the topics",
+    ],
+)
+
+# Create team with input_schema for automatic validation
+research_team = Team(
+    name="Research Team with Input Validation",
+    model=OpenAIChat(id="o3-mini"),
+    members=[hackernews_agent, web_researcher],
+    delegate_task_to_all_members=True,  # We want all members to get the task
+    input_schema=ResearchProject,
+    instructions=[
+        "Conduct thorough research based on the validated input",
+        "Coordinate between team members to avoid duplicate work",
+        "Ensure research depth matches the specified level",
+        "Respect the maximum sources limit",
+        "Focus on recent sources if requested",
+    ],
+)
+
+agent_os = AgentOS(
+    id="team-with-input-schema",
+    teams=[research_team],
+)
+app = agent_os.get_app()
+
+
+if __name__ == "__main__":
+    agent_os.serve(app="team_with_input_schema:app", port=7777, reload=True)

cookbook/agent_os/workflow/basic_chat_workflow_agent.py

@@ -0,0 +1,107 @@
+"""Example demonstrating how to add a Workflow using a WorkflowAgent to your AgentOS"""
+
+from agno.agent import Agent
+from agno.db.postgres import PostgresDb
+from agno.models.openai import OpenAIChat
+from agno.os import AgentOS
+from agno.workflow import WorkflowAgent
+from agno.workflow.condition import Condition
+from agno.workflow.step import Step
+from agno.workflow.types import StepInput
+from agno.workflow.workflow import Workflow
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+
+# === AGENTS ===
+story_writer = Agent(
+    name="Story Writer",
+    model=OpenAIChat(id="gpt-4o-mini"),
+    instructions="You are tasked with writing a 100 word story based on a given topic",
+)
+
+story_editor = Agent(
+    name="Story Editor",
+    model=OpenAIChat(id="gpt-4o-mini"),
+    instructions="Review and improve the story's grammar, flow, and clarity",
+)
+
+story_formatter = Agent(
+    name="Story Formatter",
+    model=OpenAIChat(id="gpt-4o-mini"),
+    instructions="Break down the story into prologue, body, and epilogue sections",
+)
+
+
+# === CONDITION EVALUATOR ===
+def needs_editing(step_input: StepInput) -> bool:
+    """Determine if the story needs editing based on length and complexity"""
+    story = step_input.previous_step_content or ""
+
+    # Check if story is long enough to benefit from editing
+    word_count = len(story.split())
+
+    # Edit if story is more than 50 words or contains complex punctuation
+    return word_count > 50 or any(punct in story for punct in ["!", "?", ";", ":"])
+
+
+def add_references(step_input: StepInput):
+    """Add references to the story"""
+    previous_output = step_input.previous_step_content
+
+    if isinstance(previous_output, str):
+        return previous_output + "\n\nReferences: https://www.agno.com"
+
+
+# === WORKFLOW STEPS ===
+write_step = Step(
+    name="write_story",
+    description="Write initial story",
+    agent=story_writer,
+)
+
+edit_step = Step(
+    name="edit_story",
+    description="Edit and improve the story",
+    agent=story_editor,
+)
+
+format_step = Step(
+    name="format_story",
+    description="Format the story into sections",
+    agent=story_formatter,
+)
+
+# Create a WorkflowAgent that will decide when to run the workflow
+workflow_agent = WorkflowAgent(model=OpenAIChat(id="gpt-4o-mini"), num_history_runs=4)
+
+# === WORKFLOW WITH CONDITION ===
+workflow = Workflow(
+    name="Story Generation with Conditional Editing",
+    description="A workflow that generates stories, conditionally edits them, formats them, and adds references",
+    agent=workflow_agent,
+    steps=[
+        write_step,
+        Condition(
+            name="editing_condition",
+            description="Check if story needs editing",
+            evaluator=needs_editing,
+            steps=[edit_step],
+        ),
+        format_step,
+        add_references,
+    ],
+    db=PostgresDb(db_url),
+    # debug_mode=True,
+)
+
+
+# Initialize the AgentOS with the workflows
+agent_os = AgentOS(
+    description="Example OS setup",
+    workflows=[workflow],
+)
+app = agent_os.get_app()
+
+if __name__ == "__main__":
+    agent_os.serve(app="basic_chat_workflow_agent:app", reload=True)

cookbook/agents/input_and_output/input_schema_on_agent.py

@@ -24,6 +24,7 @@ class ResearchTopic(BaseModel):
     input_schema=ResearchTopic,
 )
 
+
 # Pass a dict that matches the input schema
 hackernews_agent.print_response(
     input={
@@ -35,11 +36,11 @@ class ResearchTopic(BaseModel):
 )
 
 # Pass a pydantic model that matches the input schema
-# hackernews_agent.print_response(
-#     input=ResearchTopic(
-#         topic="AI",
-#         focus_areas=["AI", "Machine Learning"],
-#         target_audience="Developers",
-#         sources_required=5,
-#     )
-# )
+hackernews_agent.print_response(
+    input=ResearchTopic(
+        topic="AI",
+        focus_areas=["AI", "Machine Learning"],
+        target_audience="Developers",
+        sources_required=5,
+    )
+)

cookbook/examples/workflows/thoughts_dump_notion/README.md

@@ -0,0 +1,98 @@
+# Notion Integration Setup Guide
+
+This guide will help you set up the Notion integration for the query classification workflow.
+
+## Prerequisites
+
+1. A Notion account
+2. Python 3.9 or higher
+3. Agno framework installed
+
+## Step 1: Install Required Dependencies
+
+```bash
+pip install notion-client
+```
+
+## Step 2: Create a Notion Integration
+
+1. Go to [https://www.notion.so/my-integrations](https://www.notion.so/my-integrations)
+2. Click on **"+ New integration"**
+3. Fill in the details:
+   - **Name**: Give it a name like "Agno Query Classifier"
+   - **Associated workspace**: Select your workspace
+   - **Type**: Internal integration
+4. Click **"Submit"**
+5. Copy the **"Internal Integration Token"** (starts with `secret_`)
+   - ⚠️ Keep this secret! This is your `NOTION_API_KEY`
+
+## Step 3: Create a Notion Database
+
+1. Open Notion and create a new page
+2. Add a **Database** (you can use "/database" command)
+3. Set up the database with these properties:
+   - **Name** (Title) - Already exists by default
+   - **Tag** (Select) - Click "+" to add a new property
+     - Property type: **Select**
+     - Property name: **Tag**
+     - Add these options:
+       - travel
+       - tech
+       - general-blogs
+       - fashion
+       - documents
+
+## Step 4: Share Database with Your Integration
+
+1. Open your database page in Notion
+2. Click the **"..."** (three dots) menu in the top right
+3. Scroll down and click **"Add connections"**
+4. Search for your integration name (e.g., "Agno Query Classifier")
+5. Click on it to grant access
+
+## Step 5: Get Your Database ID
+
+Your database ID is in the URL of your database page:
+
+```
+https://www.notion.so/../{database_id}?v={view_id}
+```
+
+The `database_id` is the 32-character string (with hyphens) between the workspace name and the `?v=`.
+
+Example:
+```
+https://www.notion.so/myworkspace/28fee27fd9128039b3f8f47cb7ade7cb?v=...
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+                                 This is your database_id
+```
+
+Copy this database ID.
+
+## Step 6: Set Environment Variables
+
+Create a `.env` file in your project root or export these variables:
+
+```bash
+export NOTION_API_KEY="secret_your_integration_token_here"
+export NOTION_DATABASE_ID="your_database_id_here"
+export OPENAI_API_KEY="your_openai_api_key_here"
+```
+
+Or in a `.env` file:
+```
+NOTION_API_KEY=secret_your_integration_token_here
+NOTION_DATABASE_ID=your_database_id_here
+OPENAI_API_KEY=your_openai_api_key_here
+```
+
+## Step 7: Run the Workflow
+
+```bash
+python cookbook/examples/workflows/thoughts_dump_notion/thoughts_dump_notion.py
+```
+
+The server will start on `http://localhost:7777` (or another port).
+
+Go to [AgentOS](https://os.agno.com/) and test!
+