Want Built a React-style looping agent with small LLMs (Qwen 3.5 9B / Gemma4) + LangGraph?

Currently experimenting with building a React-style looping agent system using small LLMs like Qwen 3.5 9B and Gemma 4 (E2B), and I wanted to ask if anyone here has worked on something similar.

Current setup:

• Using LangGraph
• Around 5 tools available to the agent
• Input includes both instructions and images
• Agent runs in a loop where one tool’s output may become another tool’s input
• Planning to later extend this into a multi-agent system with 2 subagents

Right now I’m only testing a single-agent workflow before moving to multi-agent orchestration.

The main issue I’m facing:

• Qwen 9B starts generating huge amounts of thinking/reasoning tokens during loops
• Sometimes the output never properly returns or gets truncated
• Recursive/react loops become unstable after a few iterations

I’m trying to understand:

• How people usually control tool-calling loops with smaller models
• Whether I should limit reasoning depth / iterations
• Better patterns for tool dependency handling in LangGraph
• Whether planner/executor separation is necessary even for small systems
• If there are known strategies to reduce unnecessary “thinking token” generation in Qwen

Would really appreciate:

• Architecture suggestions
• Open-source repos/examples
• Best practices for LangGraph recursive agents
• Tips for making small models stable in tool loops