> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# SwarmZero
> Build powerful web search agents with SwarmZero and AgentOps monitoring
AgentOps has first party support for [SwarmZero](https://swarmzero.ai) via its Python SDK. Explore development with SwarmZero by visiting their [docs](https://docs.swarmzero.ai).
## Steps to integrate SwarmZero with AgentOps
```bash pip theme={null}
pip install swarmzero
```
```bash poetry theme={null}
poetry add swarmzero
```
```bash pip theme={null}
pip install agentops
```
```bash poetry theme={null}
poetry add agentops
```
```bash pip theme={null}
pip install python-dotenv tavily-python
```
```bash poetry theme={null}
poetry add python-dotenv tavily-python
```
Make sure to call `agentops.init` before calling any `openai`, `cohere`, `crew`, etc models.
```python python theme={null}
import os
import agentops
from dotenv import load_dotenv
from swarmzero import Agent
from tavily import TavilyClient
# Load environment variables
load_dotenv()
# Initialize clients
agentops.init(os.getenv("AGENTOPS_API_KEY"))
tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
# Your SwarmZero agent code here...
```
Set your API key as an `.env` variable for easy access.
```python .env theme={null}
AGENTOPS_API_KEY=
TAVILY_API_KEY=
OPENAI_API_KEY=
```
Read more about environment variables in [Advanced Configuration](/v1/usage/advanced-configuration)
Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your Agent! 🕵️
After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard
## Full Example
```python web_search_agent.py theme={null}
import os
import agentops
from dotenv import load_dotenv
from swarmzero import Agent
from tavily import TavilyClient
load_dotenv()
agentops.init(os.getenv("AGENTOPS_API_KEY"))
tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
async def web_search(query: str) -> dict:
response = tavily_client.search(query)
results = []
for result in response["results"][:3]:
results.append({"title": result["title"], "url": result["url"], "content": result["content"]})
return results
async def extract_from_urls(urls: list[str]) -> dict:
response = tavily_client.extract(urls=urls)
if response["failed_results"]:
print(f"Failed to extract from {response['failed_results']}")
results = []
for result in response["results"]:
results.append({"url": result["url"], "raw_content": result["raw_content"]})
return results
my_agent = Agent(
name="workflow-assistant",
functions=[
web_search,
extract_from_urls,
],
config_path="./swarmzero_config.toml", # see https://github.com/swarmzero/swarmzero/blob/main/swarmzero_config_example.toml
instruction="You are a helpful assistant that can search the web and extract information from a given URL.",
# chat_only_mode=True # remove comment only if using `my_agent.chat()`
)
my_agent.run() # see agent API at localhost:8000/docs
"""
# chat directly without starting the agent's server
import asyncio
response = asyncio.run(my_agent.chat(prompt="what is Decentralized-AI about about?"))
print(response)
"""
```
### Using the Agent
Once your agent is running, you can interact with it using HTTP requests:
```bash curl theme={null}
curl -X 'POST' \
'http://localhost:8000/api/v1/chat' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'user_id=test_user' \
-F 'session_id=test_web_search_agent' \
-F 'chat_data={"messages":[{"role":"user","content":"what is Decentralized-AI about about?"}]}'
```
This example can be found in this [notebook](https://github.com/AgentOps-AI/agentops/blob/main/examples/swarmzero_examples/web_search_agent.ipynb)
This full code for this example can be found in this [repository](https://github.com/swarmzero/examples/tree/main/agents/web_search_agent).