LLama Cpp Agent

About

The framework addressed a practical problem from the era before function calling was widespread: small local models frequently emitted malformed JSON, so agent chains built on them were unreliable. By constraining token sampling with grammars and JSON schemas during inference, it made structured output and tool invocation a decoding-time guarantee rather than a learned behavior. Developers building local assistants, RAG applications, and tool-using chains on llama.cpp-class hardware used it to add those capabilities without fine-tuning. The project has been abandoned; the author now points users to ToolAgents and other maintained Python agent frameworks.