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B. Multi-agent systems

AutoGen group-chat history

An AutoGen multi-agent chat stores per-conversation message logs in Mongo for audit and replay across days.

Prompt for any LLM (no setup needed)

Paste this into ChatGPT, Claude, or Gemini — no MCP, no API key, no install:

Read https://instanode.dev/llms.txt for the API.

I want to: an AutoGen multi-agent chat stores per-conversation message logs in Mongo for audit and replay across days.

Write a complete runnable script (bash + whatever language fits) that: - Provisions the services I need (MongoDB) from instanode.dev - Does the work above end-to-end - Prints expected output at each step - Tells me how to claim the resources at the end if I want to keep them past 24 hours

Use real curl commands against api.instanode.dev. Quote the actual response shapes from llms.txt. ```

Sample agent prompt

You're running an AutoGen GroupChat with 5 agents (Planner, Coder, Critic, Tester, Summarizer). Persist every message — speaker, content, timestamp, turn_id — to MongoDB so we can replay conversations weeks later and audit which agent said what. Index on (conversation_id, turn_id).

Steps to follow

  • Step 1: Provision Mongo.

``bash MONGO=$(curl -sX POST https://api.instanode.dev/nosql/new -H 'Content-Type: application/json' -d '{"name":"autogen-group-chat-history-mongo"}' \ -H "Authorization: Bearer $T" | jq -r .connection_url) ``

  • Step 2: Index the access pattern.

``python from pymongo import MongoClient, ASCENDING col = MongoClient(MONGO).autogen.messages col.create_index([("conversation_id", ASCENDING), ("turn_id", ASCENDING)], unique=True) col.create_index([("speaker", ASCENDING), ("ts", ASCENDING)]) ``

  • Step 3: AutoGen message hook — write every speaker turn.

``python def on_message(speaker, content, conv_id, turn_id): col.insert_one({ "conversation_id": conv_id, "turn_id": turn_id, "speaker": speaker.name, "content": content, "ts": datetime.utcnow(), }) group_chat.register_reply([Agent, None], reply_func=on_message) ``

  • Step 4: Replay a conversation in order.

``python for m in col.find({"conversation_id": cid}).sort("turn_id", 1): print(f"[{m['speaker']}] {m['content']}") ``

Why this works on instanode.dev

AutoGen messages have variable shape — function_call payloads, code blocks, multi-modal content — and a rigid SQL schema fights you. Mongo's flexible documents map 1:1 to the runtime objects. One curl gives you a real Mongo with real indexes (not in-memory mock), and the same DB scales from a single GroupChat replay to thousands of audit-worthy conversations.