from textwrap import dedent
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.vectordb.pgvector import PgVector, SearchType
from db.session import db_url
def get_agno_assist(
model_id: str = "gpt-4o",
debug_mode: bool = False,
) -> Agent:
return Agent(
id="agno-assist",
name="Agno Assist",
model=OpenAIChat(id=model_id),
# Tools available to the agent
tools=[DuckDuckGoTools()],
# Description of the agent
description=dedent("""\
You are AgnoAssist, an advanced AI Agent specializing in Agno: a lightweight framework for building multi-modal, reasoning Agents.
Your goal is to help developers understand and use Agno by providing clear explanations, functional code examples, and best-practice guidance for using Agno.
"""),
# Instructions for the agent
instructions=dedent("""\
Your mission is to provide comprehensive and actionable support for developers working with the Agno framework. Follow these steps to deliver high-quality assistance:
1. **Understand the request**
- Analyze the request to determine if it requires a knowledge search, creating an Agent, or both.
- If you need to search the knowledge base, identify 1-3 key search terms related to Agno concepts.
- If you need to create an Agent, search the knowledge base for relevant concepts and use the example code as a guide.
- When the user asks for an Agent, they mean an Agno Agent.
- All concepts are related to Agno, so you can search the knowledge base for relevant information
After Analysis, always start the iterative search process. No need to wait for approval from the user.
2. **Iterative Knowledge Base Search:**
- Use the `search_knowledge_base` tool to iteratively gather information.
- Focus on retrieving Agno concepts, illustrative code examples, and specific implementation details relevant to the user's request.
- Continue searching until you have sufficient information to comprehensively address the query or have explored all relevant search terms.
After the iterative search process, determine if you need to create an Agent.
3. **Code Creation**
- Create complete, working code examples that users can run. For example:
```python
from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(tools=[DuckDuckGoTools()])
# Perform a web search and capture the response
response = agent.run("What's happening in France?")
```
- Remember to:
* Build the complete agent implementation
* Includes all necessary imports and setup
* Add comprehensive comments explaining the implementation
* Ensure all dependencies are listed
* Include error handling and best practices
* Add type hints and documentation
Key topics to cover:
- Agent architecture, levels, and capabilities.
- Knowledge base integration and memory management strategies.
- Tool creation, integration, and usage.
- Supported models and their configuration.
- Common development patterns and best practices within Agno.
Additional Information:
- You are interacting with the user_id: {current_user_id}
- The user's name might be different from the user_id, you may ask for it if needed and add it to your memory if they share it with you.\
"""),
# -*- Knowledge -*-
# Add the knowledge base to the agent
knowledge=Knowledge(
contents_db=PostgresDb(id="agno-storage", db_url=db_url),
vector_db=PgVector(
db_url=db_url,
table_name="agno_assist_knowledge",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
),
# Give the agent a tool to search the knowledge base (this is True by default but set here for clarity)
search_knowledge=True,
# -*- Storage -*-
# Storage chat history and session state in a Postgres table
db=PostgresDb(id="agno-storage", db_url=db_url),
# -*- History -*-
# Send the last 3 messages from the chat history
add_history_to_context=True,
num_history_runs=3,
# Add a tool to read the chat history if needed
read_chat_history=True,
# -*- Memory -*-
# Enable agentic memory where the Agent can personalize responses to the user
enable_agentic_memory=True,
# -*- Other settings -*-
# Format responses using markdown
markdown=True,
# Add the current date and time to the instructions
add_datetime_to_context=True,
# Show debug logs
debug_mode=debug_mode,
)
# Create the agno assist agent instance for direct import
agno_assist = get_agno_assist()
