rubum/extrag

extrag is a 1-day-old project with zero community traction (0 stars, 0 forks). While building RAG orchestration in Rust offers significant performance benefits (lower latency, memory safety, and smaller binaries) compared to the standard Python-based stacks like LangChain or LlamaIndex, the project currently lacks any unique moat. The 'agentic RAG' space is becoming extremely crowded; established incumbents are already porting logic to Rust (e.g., Rig-rs, llm-chain) or providing highly optimized C++/Rust-backed Python libraries. Furthermore, frontier labs like OpenAI (Assistants API) and Anthropic (Claude Projects/Artifacts) are rapidly absorbing the 'agentic retrieval' layer into their platform offerings. Without a specialized niche or a breakthrough algorithm for retrieval/reasoning, this project serves primarily as a performance-oriented alternative to existing tools rather than a category-defining platform. The high platform risk is driven by the fact that hyperscalers (AWS Bedrock, Azure AI Search) are making RAG a 'check-box' feature of their infrastructure.