Debugging my first LangChain agent

LangChain agents are notorious for "looping" or hallucinating tool calls, and after spending the last 48 hours trying to get a custom SQL agent to stop eating my API credits, I’ve realized that the choice of LLM is 90% of the battle. If you're debugging an agent that keeps calling the same tool with the same wrong arguments, stop tweaking your prompt and switch your model to see if it's a reasoning failure.

I ran a head-to-head benchmark between GPT-4o, Claude 3.5 Sonnet, and DeepSeek-V2.5 specifically on tool-calling reliability. Here is how they actually behave when the agent hits a wall:

Claude 3.5 Sonnet is currently the gold standard for agentic workflows. In my tests, it has the lowest "loop rate." When it fails, it usually fails gracefully—it realizes the tool output didn't provide the answer and tries a different search query. It follows complex system instructions without needing a massive few-shot example block.

GPT-4o is incredibly fast and generally reliable, but I've noticed it tends to "over-confidently hallucinate" tool arguments if the documentation provided in the tool description is slightly ambiguous. It's great for simple agents, but for multi-step reasoning, it occasionally skips a step it was supposed to take.

DeepSeek-V2.5 is the dark horse here. For a model that's significantly cheaper, its ability to handle structured output is surprisingly close to GPT-4o. However, it struggles more with "state drift"—where it forgets the original goal after 3 or 4 tool interactions.

If you're stuck in a debug loop, the first thing you should do is enable LangSmith or use a simple Verbose=True flag to see the raw thought process. Most of the time, the agent isn't "broken"; it's just that the LLM is misinterpreting the tool's return string.

I found that wrapping my tool outputs in a very specific format helped GPT-4o stop looping:

# Instead of returning a raw string, give the agent a clear signal
def my_custom_tool(input_text):
    try:
        result = perform_action(input_text)
        return f"SUCCESS: The data retrieved is {result}. Proceed to next step."
    except Exception as e:
        return f"ERROR: {str(e)}. Please try a different search term or check your syntax."

The "thought" chain is where the failure happens. If you see the agent repeating Action: [Tool Name] three times in a row, it's usually because the model doesn't think the tool output provided any new information.

Pros and Cons for Agentic use:

• Claude 3.5 Sonnet: Best reasoning, lowest hallucination rate in tools, but slightly slower than GPT-4o.
• GPT-4o: Fastest response time, great ecosystem integration, but prone to "lazy" tool calls if the prompt is too long.
• DeepSeek-V2.5: Insane price-to-performance ratio, strong coding capabilities, but loses the thread in long-running agent conversations.
• Gemini 1.5 Pro: Massive context window is a godsend for RAG-heavy agents, but it can be overly cautious/refuse to call tools if it triggers a safety filter.

My current stack for production agents is Claude 3.5 Sonnet for the orchestration layer and DeepSeek for the smaller, repetitive sub-tasks to keep costs down. If you're just starting, don't waste time debugging a complex prompt on a weaker model; move to Sonnet, get the logic working, and then try to optimize downwards.