https://github.com/WebDevCaptain/agno-ai-agents
Exploring Agno framework for building AI agents.
https://github.com/WebDevCaptain/agno-ai-agents
ai-agents deepseek-r1 ollama openai
Last synced: about 1 month ago
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Exploring Agno framework for building AI agents.
- Host: GitHub
- URL: https://github.com/WebDevCaptain/agno-ai-agents
- Owner: WebDevCaptain
- Created: 2025-03-03T13:30:16.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-03T13:42:02.000Z (2 months ago)
- Last Synced: 2025-03-03T14:30:54.637Z (2 months ago)
- Topics: ai-agents, deepseek-r1, ollama, openai
- Language: Shell
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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- awesome-hacking-lists - WebDevCaptain/agno-ai-agents - Exploring Agno framework for building AI agents. (Python)
README
# AI Agents using `agno` and Ollama
Agents example list:
1. [Simple Ollama Agent](./ollama-agent.py)
The first basic agent that uses Deepseek R1 (8B parameter) model.
2. [Finance Agent](./finance-agent.py)
A simple finance agent using Yahoo Finance tool and Qwen 2.5 (1.5B parameter) model via Ollama.
3. [Team of Agents](./agent-teams.py)
The team of agents uses DuckDuckGo search and Yahoo finance to summarize analyst recommendation and share latest news for a company given its stock ticker symbol.
4. [Image Agent](./image-agent.py)
The image agent reads an image and uses Llama 3.2 vision model to describe the image.
5. [Knowledge Agent (WIP)](./knowledge-agent.py)
The knowledge agent reads a PDF using PyPDF and uses ChromaDB (a vector database) to store embeddings (using Ollama embedder) and retrieve knowledge based on user queries.
---
## Basic Architecture
---
## Requirements
- [uv](https://github.com/astral-sh/uv)
- [ollama](https://github.com/ollama)
---
## Commands
1. Initialize the project
```bash
uv init
```
2. Create a Python virtual environment using `uv`
```bash
uv venv --python=3.13.2
```
3. Install `agno` (previously phi data) and `ollama`
```bash
uv add agno
uv add ollama
```
4. Pull Deepseek R1 (8B) and Qwen 2.5 (1.5B) models from Ollama registry
```bash
ollama pull deepseek-r1:8b
ollama pull qwen2.5:1.5b
```
5. Run the simple ollama-agent app
```bash
uv run ollama-agent.py
```
---
## Finance agent using Yahoo Finance tool
- Run the finance-agent app
```bash
uv run finance-agent.py
```
---
## Team of Agents
Finance agent works with Search agent to find the analyst recommendation for a company.
```bash
uv run agent-teams.py
```
---
## Image Agent
Reads an image and uses Llama 3.2 vision model to describe the image.
Model used: Llama 3.2 (11B parameter)
---
## Knowledge Agent (WIP)
Reads a PDF using PyPDF and uses ChromaDB (a vector database) to store embeddings (using Ollama embedder) and retrieve knowledge based on user queries.
Model used: Llama 3.2 (3B parameter)
---
## License
This repository is licensed under the [MIT License](LICENSE).