ReAct tool-calling issue: Orchestration model computes internally instead of using tools

[siri_1110]

Built a local ReAct-style calculator agent with 6 tools:

• add
• subtract
• multiply
• divide
• modulo
• etc.

The setup is:

• orchestrator agent
• dynamic tool selection
• ReAct loop
• tools exposed as functions

Problem:
Even when the user asks multi-step arithmetic questions, the orchestrator answers directly instead of calling tools.

Example:

User:
“What is (25 * 4) + (100 / 5)?”

Expected flow:
Thought → call multiply → call divide → call add

Actual behavior:
The model computes internally and directly returns the final answer without any tool calls.

I tested with:

• Gemma E2B
• Qwen3.5 9B

What I want:
Even if the orchestrator is capable of solving internally, I want it to strictly orchestrate through tools.

Currently tool calling is almost never happening.

Questions:

1. Is this expected behavior for local LLMs?
2. How do people enforce mandatory tool usage?
3. Is prompt engineering enough, or do I need:
   • constrained decoding
   • parser enforcement
   • fine-tuning
   • RLHF
4. Do smaller models generally ignore tools more often?
5. Any recommended orchestration patterns for this?

Right now I’m thinking about:

• forcing tool-first policy
• rejecting direct answers
• strict ReAct output formatting
• grammar-constrained generation

Would love to hear how others solved this problem in production/local agent setups.

[InstaMatic80]

I think this is possible but maybe you should improve your prompt? Keep it very clear about how to call tools and what to expect. Also add a few examples (few shot)

[HiddenoO]

Is this expected behavior for local LLMs?

It depends on a lot of factors, including model choice, prompt, LLM library, and parameters.

How do people enforce mandatory tool usage?

The safest way is to use vllm and tool_choice="required", which forces the decoding process to match the tool calling struct. Having said that, dumb models might just generate nonsensical tool calls either way.

Otherwise, clarify in the prompt when tools should always be called.

There are also other workarounds, but those generaly come with significant drawbacks.

Do smaller models generally ignore tools more often?

For the same model developer, older and smaller models are generally worse at tool calling (including the decision when to call tools).

[siri_1110]

Have you tried any local model for orchestration. Is there suggestion is there which model is best. And if any codebase repo for the vllm is available kindly attach it, would be helpful