Pre-seed fit Near-flat -0.3 acceleration but 26.7 daily velocity on orchestration features fits pre-seed with its Python framework's agent focus, evidenced by recent PRs for Mistral integration and a presumed small team avoiding corporate maintenance. Momentum trails llama.cpp's 43 but exceeds AutoGen'

Pre-seed fit vLLM's +5.0 acceleration to 27.3 daily stars from zero base aligns with pre-seed via high momentum in throughput-focused commits for Phi models, implying a compact team positioning against established frameworks. Its CUDA and ROCm emphasis, outpacing PyTorch's 9.0, points to recent

Pre-seed fit With flat acceleration at +0.0 but steady 36.4 daily stars on Go-based inference, Ollama fits pre-seed through its small-team vibe and recent Metal support commits, emphasizing local LLM deployment without big-tech ties. Velocity matches LangChain's 26.7, driven by any-GGUF compatibility, suggesting early-stage funding

Pre-seed fit This project's zero-star base paired with 43 daily velocity and -11.6 acceleration from recent GGUF commits suggests a small-team operation hitting early traction in CPU inference, fitting pre-seed for its hardware-agnostic positioning without corporate backing. The consistent release cadence on Qwen and Phi models indicates