It is straightforward to get into. Easy write an agent. Small feature set. No memory or retriever support currently.
More comprehensive than Smolagents, has built-in tool set. It is easy to write RAG applications with built-in RAG tools and retrievers.
Complex, requires more setup step. Achieves the result but it takes more time to built what you want to do.
Note: Do not forget to fill api key for your gemini or custom llm apis.
ollama install llama3
ollama install qwen2.5-coder
ollama install openhermes
conda create -n agents python=3.10
conda activate agents
pip install smolagents
pip install -U phidata
pip install crewai crewai-tools
pip install pypdf
pip install ollama
pip install pgvector
pip install pypdf
pip install psycopg2
conda install -c conda-forge psycopg
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
phidata/pgvector:16
cd smolagents
python3 main.py
cd phidata
python3 main.py
cd crewai
crewai run