What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching Reddit Comments Tree with AutoGen
This integration lets your AutoGen agent search Reddit Comments Tree in real time via the Scavio API. The agent gets back structured JSON with comment text, authors, scores, depth -- ready for reasoning and decision-making.
Setup
Bash
pip install autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches Reddit Comments Tree using Scavio:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Comments are fetched per post: get a post_id from POST /api/v1/reddit/search, or resolve a URL with POST /api/v1/reddit/post. Deeper replies come from POST /api/v1/reddit/post/comments/replies using a row's reply_cursor.
def scavio_search(query: str) -> str:
"""Query Reddit Comments Tree through the Scavio API (POST /api/v1/reddit/post/comments)."""
response = requests.post(
"https://api.scavio.dev/api/v1/reddit/post/comments",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"post_id": query, "sort": "TOP"},
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 Reddit Comments Tree")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: t3_1u1143i")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Comments are fetched per post: get a post_id from POST /api/v1/reddit/search, or resolve a URL with POST /api/v1/reddit/post. Deeper replies come from POST /api/v1/reddit/post/comments/replies using a row's reply_cursor.
def scavio_search(query: str) -> str:
"""Query Reddit Comments Tree through the Scavio API (POST /api/v1/reddit/post/comments)."""
response = requests.post(
"https://api.scavio.dev/api/v1/reddit/post/comments",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"post_id": query, "sort": "TOP"},
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 Reddit Comments Tree")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: t3_1u1143i")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.