Setting Up Your Agent Dev Environment

# Create a new project directory
mkdir my-agent && cd my-agent

# Create virtual environment
python -m venv .venv

# Activate (Windows)
.venv\Scripts\activate

# Activate (Mac/Linux)
source .venv/bin/activate

# Verify Python version
python --version  # Should be 3.11 or 3.12

# LLM SDKs
pip install openai anthropic

# LangChain ecosystem
pip install langchain langchain-openai langchain-anthropic langchain-community

# LangGraph (for multi-agent and graph-based workflows)
pip install langgraph

# Vector database clients
pip install chromadb pinecone-client

# Search and tools
pip install tavily-python  # Web search API
pip install duckduckgo-search  # Free search (lower quality)

# Utilities
pip install python-dotenv  # Load .env files
pip install pydantic  # Data validation
pip install rich  # Pretty terminal output
pip install httpx  # Async HTTP client

# Save your requirements
pip freeze > requirements.txt

# .env
OPENAI_API_KEY=sk-proj-...
ANTHROPIC_API_KEY=sk-ant-...
TAVILY_API_KEY=tvly-...
LANGCHAIN_API_KEY=...           # For LangSmith tracing (optional but valuable)
LANGCHAIN_TRACING_V2=true       # Enable LangSmith tracing
LANGCHAIN_PROJECT=my-agent      # Project name in LangSmith

# For vector databases
PINECONE_API_KEY=...
PINECONE_ENVIRONMENT=us-east-1

# For later — if you deploy
REDIS_URL=redis://localhost:6379

echo ".env" >> .gitignore
echo ".venv/" >> .gitignore
echo "__pycache__/" >> .gitignore
echo "*.pyc" >> .gitignore

from dotenv import load_dotenv
import os

load_dotenv()  # Loads .env file

openai_key = os.environ["OPENAI_API_KEY"]  # Raises error if missing
anthropic_key = os.getenv("ANTHROPIC_API_KEY")  # Returns None if missing

my-agent/
├── .env                    # API keys (never commit)
├── .gitignore
├── requirements.txt
├── README.md
│
├── agents/
│   ├── __init__.py
│   ├── research_agent.py   # Agent definitions
│   └── coding_agent.py
│
├── tools/
│   ├── __init__.py
│   ├── search.py           # Tool implementations
│   ├── code_execution.py
│   └── file_tools.py
│
├── memory/
│   ├── __init__.py
│   └── vector_store.py     # Vector database setup
│
├── prompts/
│   └── templates.py        # System prompts as constants
│
└── main.py                 # Entry point

# test_setup.py
import os
from dotenv import load_dotenv

load_dotenv()

def test_openai():
    from openai import OpenAI
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Say 'setup successful' and nothing else"}]
    )
    print(f"OpenAI: {response.choices[0].message.content}")

def test_anthropic():
    import anthropic
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=50,
        messages=[{"role": "user", "content": "Say 'setup successful' and nothing else"}]
    )
    print(f"Anthropic: {message.content[0].text}")

def test_tavily():
    from tavily import TavilyClient
    client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
    result = client.search("Python agent development", max_results=1)
    print(f"Tavily: Found {len(result['results'])} results")

def test_langchain():
    from langchain_openai import ChatOpenAI
    llm = ChatOpenAI(model="gpt-4o-mini")
    response = llm.invoke("Say 'langchain works' and nothing else")
    print(f"LangChain: {response.content}")

if __name__ == "__main__":
    print("Testing setup...\n")
    test_openai()
    test_anthropic()
    test_tavily()
    test_langchain()
    print("\nAll tests passed!")

# During development
model = "gpt-4o-mini"  # ~15x cheaper than gpt-4o
model = "claude-haiku-4-5-20251001"  # Cheapest Claude

# Switch to powerful models only when needed
model = "gpt-4o"
model = "claude-sonnet-4-6"

{
    "python.defaultInterpreterPath": ".venv/bin/python",
    "python.linting.enabled": true,
    "editor.formatOnSave": true,
    "[python]": {
        "editor.defaultFormatter": "ms-python.black-formatter"
    }
}