How to Fix 'OOM error during inference during development' in AutoGen (Python)

When you see OOM error during inference during development in AutoGen, it usually means your Python process ran out of memory while the model was generating a response or while AutoGen was building up too much conversation state. In practice, this shows up when you use a large model locally, keep too much chat history, or let an agent loop run without hard limits.

The fix is usually not “buy more RAM.” It’s almost always about reducing what gets sent to the model, controlling history growth, or tightening your AutoGen config.

The Most Common Cause

The #1 cause is unbounded conversation growth.

In AutoGen, AssistantAgent and UserProxyAgent can accumulate message history across turns. If you keep appending full transcripts, tool outputs, and long system prompts, inference memory balloons until the process dies with an OOM error.

Here’s the wrong pattern versus the right pattern.

# WRONG: unbounded history grows until OOM
from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="assistant",
    llm_config={
        "config_list": [{"model": "gpt-4o-mini", "api_key": "YOUR_KEY"}],
        "temperature": 0,
    },
)

user = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
)

# This can explode if each turn appends large outputs
chat_result = user.initiate_chat(
    assistant,
    message="Analyze this dataset and keep iterating until done.",
)

# RIGHT: cap context and keep messages small
from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="assistant",
    llm_config={
        "config_list": [{"model": "gpt-4o-mini", "api_key": "YOUR_KEY"}],
        "temperature": 0,
        "max_tokens": 800,
    },
)

user = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,
)

# Keep prompts short and summarize external data before sending it
summary = "Dataset summary: 12k rows, 18 columns, 2% missing values."
chat_result = user.initiate_chat(
    assistant,
    message=f"Review this summary and suggest next steps:\n{summary}",
)

If you are using GroupChat, this gets worse because every agent sees more messages. A long-running GroupChatManager session can grow fast enough to trigger:

• •RuntimeError: CUDA out of memory
• •MemoryError
• •OOM error during inference during development

Other Possible Causes

1) Your model context window is too small for the prompt

If you’re using a smaller local model through Ollama, vLLM, LM Studio, or a hosted endpoint with strict limits, AutoGen may send more tokens than the backend can handle.

llm_config = {
    "config_list": [{
        "model": "llama3.1:8b",
        "base_url": "http://localhost:11434/v1",
        "api_key": "ollama",
    }],
    # Missing max_tokens / truncation discipline
}

Fix it by shrinking system prompts and summaries before they hit the model.

llm_config = {
    "config_list": [{
        "model": "llama3.1:8b",
        "base_url": "http://localhost:11434/v1",
        "api_key": "ollama",
    }],
    "max_tokens": 512,
}

2) Tool output is too large

A common AutoGen failure mode is passing raw logs, CSVs, HTML pages, or JSON blobs directly back into chat. That data gets re-sent on every turn.

# BAD: dumping full tool output into chat history
tool_result = open("big_report.json").read()
message = f"Here is the report:\n{tool_result}"

Instead, extract only what the agent needs.

# GOOD: send a compact summary
message = (
    "Report summary:\n"
    "- total_accounts: 12043\n"
    "- failed_checks: 17\n"
    "- top_error_codes: E102, E204\n"
)

3) You are running multiple agents in one process

GroupChat, nested chats, or parallel agent runs can multiply memory use quickly. Each AssistantAgent may hold its own state plus copies of shared messages.

from autogen import GroupChat, GroupChatManager

groupchat = GroupChat(
    agents=[agent1, agent2, agent3],
    messages=[],
    max_round=20,
)
manager = GroupChatManager(groupchat=groupchat)

Reduce round count and avoid carrying giant payloads between agents.

4) Local inference backend is under-provisioned

If AutoGen is talking to a local LLM server, the OOM may be happening outside Python. The symptom still surfaces during inference in your dev run.

Typical signs:

• •Python stack trace ends at an HTTP call to the model server
• •Backend logs show GPU/CPU memory exhaustion
• •The same prompt works on a larger machine but fails locally

If that’s the case:

• •lower max_tokens
• •use a smaller model
• •reduce batch size on the inference server
• •move from GPU to CPU only if the GPU allocator is fragmenting badly

How to Debug It

1. •

   Print token-sized inputs before every agent call
   Check how big your prompt actually is. If it includes full transcripts or raw tool output, that’s your culprit.

2. •

   Disable one feature at a time
   Run without tools first. Then run without group chat. Then run with a smaller prompt. The first change that stops the OOM points to the cause.

3. •

   Inspect backend logs
   If you’re using Ollama, vLLM, TGI, or Azure OpenAI proxy layers, check their logs separately. A Python-side MemoryError and a backend-side GPU OOM need different fixes.

4. •

   Add hard limits in AutoGen config
   Set:

   • •max_consecutive_auto_reply
   • •max_tokens
   • •shorter system prompts
   • •fewer rounds in GroupChat

Example:

assistant = AssistantAgent(
    name="assistant",
    llm_config={
        "config_list": [{"model": "gpt-4o-mini", "api_key": "YOUR_KEY"}],
        "max_tokens": 600,
        "temperature": 0,
    },
)

user = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=2,
)

Prevention

• •Keep prompts small and structured. Send summaries, not raw logs or full documents.
• •Put hard ceilings on conversation growth with max_consecutive_auto_reply, limited rounds, and shorter context.
• •Test against production-like payload sizes early. If your dev data fits but real bank/insurance records don’t, you will hit this again later.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.