My agent kept hitting context limits. This one function fixed it.
📰 Dev.to · Mukunda Rao Katta
Learn how to trim LLM conversation history to fit a token budget using a zero-dep Python library, ensuring tool_use/tool_result pairs stay together
Action Steps
- Install the zero-dep Python library using pip
- Import the library and initialize the conversation history trimmer
- Configure the token budget and tool_use/tool_result pair settings
- Use the trimmer to optimize conversation history and test the results
- Integrate the optimized conversation history into your LLM model
Who Needs to Know This
Developers and AI engineers working with LLMs can benefit from this solution to optimize conversation history and improve model performance
Key Insight
💡 Trimming conversation history to fit a token budget can significantly improve LLM performance
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🚀 Fix context limits in LLMs with a simple Python lib! 🤖
Key Takeaways
Learn how to trim LLM conversation history to fit a token budget using a zero-dep Python library, ensuring tool_use/tool_result pairs stay together
Full Article
A zero-dep Python lib that trims LLM conversation history to fit a token budget — and always keeps tool_use/tool_result pairs together.
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