How to Fix 'chain execution stuck during development' in AutoGen (Python)

Opening

chain execution stuck during development usually means your AutoGen agent loop is waiting forever for a message, a tool result, or a termination condition that never arrives. In practice, this shows up when you run AssistantAgent, UserProxyAgent, or a custom GroupChat and the conversation keeps cycling without producing a final answer.

This is almost always a wiring problem, not an AutoGen bug. The agent graph is valid enough to start, but one of the control points is missing: no termination condition, no human input path, or a tool call that never returns.

The Most Common Cause

The #1 cause is an infinite conversation loop caused by missing or weak termination logic.

With AutoGen, especially in Python, it’s easy to create agents that can keep replying to each other forever. If neither side knows when to stop, initiate_chat() or run() will keep going until it hits your max turn limit or appears stuck during development.

Broken vs fixed pattern

# BROKEN: endless back-and-forth
from autogen import AssistantAgent, UserProxyAgent

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

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

user.initiate_chat(
    assistant,
    message="Summarize this document and keep improving it."
)

# FIXED: explicit termination behavior
from autogen import AssistantAgent, UserProxyAgent

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

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

user.initiate_chat(
    assistant,
    message=(
        "Summarize this document. "
        "When done, reply with exactly: TERMINATE"
    )
)

If you’re using newer AutoGen agent APIs, the same idea applies: your termination condition must be explicit. For example, in group chat workflows you need a clear end rule instead of relying on “the model will know when it’s done.”

Other Possible Causes

1) human_input_mode blocks the flow

If UserProxyAgent is waiting for human input but you’re running in a non-interactive environment, the chat looks frozen.

# Problematic in CI / scripts
user = UserProxyAgent(
    name="user",
    human_input_mode="ALWAYS"
)

Fix it by disabling interactive prompts during development runs:

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

2) A tool function never returns

If you register a tool that hangs on I/O, network calls, or retries forever, AutoGen waits for the tool result and the chain appears stuck.

def fetch_policy_data(policy_id: str):
    while True:
        pass  # bad: never returns

Use timeouts and bounded retries:

import requests

def fetch_policy_data(policy_id: str):
    resp = requests.get(
        f"https://api.example.com/policies/{policy_id}",
        timeout=10,
    )
    resp.raise_for_status()
    return resp.json()

3) Your register_function / tool schema doesn’t match the function signature

A mismatched schema can cause repeated tool-call attempts because the model keeps asking for arguments that don’t validate.

# Wrong: schema expects 'policy_number', function expects 'policy_id'
def get_policy(policy_id: str):
    return {"id": policy_id}

Make the names line up exactly:

def get_policy(policy_number: str):
    return {"id": policy_number}

4) The model keeps calling itself through nested agents

This happens when an assistant agent is configured to delegate to another agent without a hard stop. You’ll see repeated internal turns and no final answer.

# Example smell: agent A delegates to B, B delegates back to A
groupchat = GroupChat(agents=[agent_a, agent_b], messages=[], max_round=20)
manager = GroupChatManager(groupchat=groupchat)

Fix by limiting rounds and defining who can speak next:

groupchat = GroupChat(
    agents=[agent_a, agent_b],
    messages=[],
    max_round=6,
)
manager = GroupChatManager(groupchat=groupchat)

How to Debug It

1. •

   Check whether the process is actually waiting for input

   • •If you used UserProxyAgent(human_input_mode="ALWAYS"), the app may be paused for stdin.
   • •Run it in a terminal and watch for prompts like Provide feedback to assistant:.
2. •

   Turn on verbose logging

   • •Print every message exchanged between agents.
   • •Look for repeating patterns like:
     • •assistant -> user
     • •user -> assistant
     • •same content over and over
3. •

   Reduce the conversation to one turn

   • •Replace your full workflow with a minimal chat.
   • •If this works:

     user.initiate_chat(assistant, message="Say hello and stop.")
     

     then your issue is in tools, delegation, or termination logic.
4. •

   Disable tools and nested agents first

   • •Remove function calling.
   • •Remove group chat routing.
   • •If the chain stops hanging after that, reintroduce components one by one until it breaks again.

Prevention

• •

  Always define an explicit stop condition.

  • •Use clear termination text like TERMINATE, plus bounded turns such as max_consecutive_auto_reply or max_round.
• •

  Keep tool functions deterministic and timeout-safe.

  • •Every external request should have a timeout.
  • •Every retry loop should have a max attempt count.
• •

  Start with the smallest possible agent graph.

  • •One assistant, one user proxy.
  • •Add group chat and tools only after the basic loop terminates correctly.

If you’re seeing this error in AutoGen Python, assume control flow first. In most cases, the model is fine; your agent orchestration is what’s stuck.

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.