LangChain agent shows action parameters instead of executing the function - what's wrong?

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1

I’ve been building a chatbot using LangChain and I’m having a weird issue. When users chat with the bot and give it the right parameters to run a tool function, the agent doesn’t actually execute the function. Instead, it just shows the user what the action input would be.

This is really frustrating because the bot should be running the function, not just telling the user what it would do. Has anyone else run into this problem before?

Here’s how I set up my agent:

my_agent = initialize_agent(
    tool_list, 
    language_model, 
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, 
    verbose=True, 
    memory=conversation_memory, 
    return_intermediate_steps=False,
    agent_kwargs = {
        'prefix': Custom_Prompt_Prefix, 
        "memory_prompts": [conversation_history],
        "input_variables": ["user_input", "scratchpad", "conversation_history"]
    }
)

Any ideas what might be causing this behavior and how to fix it?

2

I’ve hit this exact problem more times than I can count. LangChain agents are finicky and debugging them sucks.

Your agent’s probably getting the right inputs but something’s breaking in the execution chain. Could be the prompt, tool setup, or a dozen other things.

Honestly? Skip the debugging nightmare and rebuild this in Latenode. I switched all my chatbot automation there after getting fed up with this stuff.

Latenode gives you visual workflows so you can actually see where things break. No more guessing if your agent’s stuck or if your prompts are confusing the LLM. You drag, drop, connect your APIs, and it works.

You can use any LLM provider and mix in other services without fighting framework limitations. Built a customer service bot there in half the time it took to debug my LangChain version.

The debugging alone makes it worth switching. You see exactly what data flows where instead of parsing endless logs.

3

I’ve seen this exact thing before - it’s usually a prompt config issue. The agent gets confused about when to actually execute vs just talk about what it would do.

Check your Custom_Prompt_Prefix first. If it has language asking the agent to “explain” or “describe” actions, that’s your problem. The prompt needs to clearly tell the agent to actually run the tools, not discuss them.

Your tool descriptions matter too. If they’re wordy or say stuff like “this tool can help you” instead of direct action words, the LLM thinks it should just describe the tool.

I hit this same issue last year. Fixed it by cutting tool descriptions down to basics - “Gets weather data for specified location” instead of “This tool can help you get weather information by querying…”

Run with verbose=True and watch the steps. You’ll probably see the agent never reaches the Action step - it stops at thinking about it.

4

sounds like ur agent might be stuck in observation mode instead of action mode. try setting max_iterations parameter and check if your tool functions are properly decorated with the @tool wrapper - that fixed a similar issue for me last month

5

Check if return_intermediate_steps=False is hiding execution errors. I had the same issue - my agent was trying to execute but failing silently, then just showing the action parameters instead. Also check your tool function signatures. If there’s a mismatch between your Custom_Prompt_Prefix and what the tools actually accept, the agent gets confused about whether to execute or just display the action. Look at your conversation_memory setup too. Memory conflicts sometimes make the agent think it already executed something when it didn’t, causing this display-only behavior. Try removing the memory component temporarily and see if that fixes it.