Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 12 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Integrate LangSmith into your AutoGen multi-agent system to trace the entire conversation and tool execution flow. Configure the `LANGCHAIN_API_KEY` and `LANGCHAIN_TRACING_V2` environment variables. Demonstrate how LangSmith visualizes the interactions between different agents, LLM calls, and tool uses, which is critical for debugging complex multi-agent reasoning. Provide a code snippet showing how to enable LangSmith for your AutoGen `GroupChat` or individual agents.
```python
import os
os.environ["LANGCHAIN_API_KEY"] = "YOUR_LANGSMITH_API_KEY"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "AutoGen_CRE_Analysis"
# (After defining agents and tools)
# groupchat = autogen.GroupChat(agents=[user_proxy, analyst_agent], messages=[], max_round=12)
# manager = autogen.GroupChatManager(groupchat=groupchat, llm_config={"config_list": config_list_claude})
# (Then initiate the conversation)
# user_proxy.initiate_chat(manager, message="Analyze the retail property market in Austin, TX.")
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Multi-Agent System for Commercial Real Estate Analysis
Develop a sophisticated multi-agent system using AutoGen to act as an expert commercial real estate (CRE) analyst for institutional investors. The system will comprise specialized agents (e.g., a 'Data Fetcher', an 'Economic Analyst', a 'Valuation Specialist', a 'Report Generator') that collaborate autonomously to research, analyze, and synthesize insights on CRE investment opportunities. This challenge emphasizes complex agent-to-agent communication, tool orchestration, and the generation of comprehensive, data-driven reports. The agents should be able to query external (simulated) CRE data APIs, perform financial modeling, and provide reasoned investment recommendations. Focus on designing robust communication protocols between agents to resolve conflicts and refine analyses. The solution must also incorporate an observability framework to trace the multi-agent deliberation process and provide a user-friendly interface for querying the system and visualizing its output.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.