What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching LinkedIn with AutoGen
This integration lets your AutoGen agent search LinkedIn in real time via the Scavio API. The agent gets back structured JSON with job listings, company names, locations, posted dates -- ready for reasoning and decision-making.
Setup
Bash
pip install autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches LinkedIn using Scavio:
Python
from autogen import ConversableAgent
import json
import os
import requests
# There is no LinkedIn people or post search: LinkedIn retired it upstream. This finds LinkedIn pages through Google. For structured data call POST /api/v1/linkedin/person, /company or /post with a LinkedIn URL, or /api/v1/linkedin/search/jobs for keyword job search.
def scavio_search(query: str) -> str:
"""Query LinkedIn through the Scavio API (POST /api/v2/google)."""
response = requests.post(
"https://api.scavio.dev/api/v2/google",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
assistant = ConversableAgent(
"assistant",
llm_config={"model": "gpt-5.5"},
system_message="You are a helpful research assistant.",
)
user = ConversableAgent("user", human_input_mode="NEVER")
assistant.register_for_llm(name="search", description="Query LinkedIn")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: site:linkedin.com/in AI engineer San Francisco")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
import json
import os
import requests
# There is no LinkedIn people or post search: LinkedIn retired it upstream. This finds LinkedIn pages through Google. For structured data call POST /api/v1/linkedin/person, /company or /post with a LinkedIn URL, or /api/v1/linkedin/search/jobs for keyword job search.
def scavio_search(query: str) -> str:
"""Query LinkedIn through the Scavio API (POST /api/v2/google)."""
response = requests.post(
"https://api.scavio.dev/api/v2/google",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
assistant = ConversableAgent(
"assistant",
llm_config={"model": "gpt-5.5"},
system_message="You are a helpful research assistant with real-time search.",
)
user = ConversableAgent("user", human_input_mode="NEVER", max_consecutive_auto_reply=3)
assistant.register_for_llm(name="search", description="Query LinkedIn")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: site:linkedin.com/in AI engineer San Francisco")Pricing
Scavio offers a free tier with 50 credits on signup (1 credit per search). No credit card required. This is enough to build and test your AutoGen integration. Paid plans start at $30/month for higher volumes.