v0.1 Prototype
MAIN: Request received [Batch 1, 128k Context] PREFILL: Ingesting KV Cache... >> PREFILL SPEED: 20,450 tok/s >> ASYNC_SPAWN: SubAgent_GraphRAG (PID: 304) "Retrieving semantic nodes..." MAIN: Stream active (Non-blocking)... >> ASYNC_SPAWN: SubAgent_SyntaxCheck (PID: 305) "Verifying JSON schema..." CALLBACK: SubAgent_GraphRAG finished Data merged. Speculative depth increased. METRICS: Burst generation active... THROUGHPUT: 3,105 tok/s (Speculative) >> ASYNC_SPAWN: SubAgent_Audit (PID: 306) MAIN: Request complete. LATENCY: 198ms (End-to-End) SYS: Ready for next batch.
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Faster, Cheaper, Autonomous

By combining proprietary speculative decoding (Eagle) with optimized open-source stacks (vLLM, TRT-LLM), we deliver an inference engine that is not only faster and cheaper but capable of true autonomy for developers and enterprises alike.

Eagle Decoding vLLM Optimized True Autonomy
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Specialized Sub-Agents

A fleet of RL-finetuned expert models ready to handle utility tasks asynchronously:

01. Context Compaction
02. Data Retrieval (RAG)
03. Code Testing / Linting
04. Apply Model
05. UI Testing
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Infinite Context

  • ✓ Context compaction agents
  • ✓ Vector DB integration
  • ✓ Zero-forgetting architecture
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Community Core

While our orchestration is proprietary, we are committed to contributing our kernel optimizations back to the open-source community.