High-performance LLM inference engine with structured generation primitives. Apache-2.0. RadixAttention for prefix caching; first-class support for structured output, agent workflows, multi-LLM pipelines.
SGLang is the OSS high-performance LLM inference engine with structured generation primitives — Apache-2.0 license. RadixAttention for prefix caching + first-class structured output + agent workflows. Newer than vLLM/TGI. Pick SGLang for agent-heavy workloads where prefix-cache reuse beats vLLM's per-request KV cache.
SGLang's specialty (RadixAttention + structured generation) addresses two specific LLM trust pain points: prefix-cache reuse for agent multi-turn (reduces cost + latency) and structured output (reduces hallucination via grammar-constrained sampling). From a Trust Before Intelligence lens, both are trust-relevant features for production agents.
RadixAttention prefix-cache reuse beats vLLM on agent workloads.
Programming primitives + OpenAI-compat.
No auth. Cap applied.
GPU-only; multi-cloud.
Trace metadata for agent workflows.
Less mature observability than vLLM/TGI. Cap applied.
0/6 -> 2.
1/6 -> 3.
5/6 -> 4.
Structured-output specialty.
Newer; smaller community. 4/6 -> 3.
Best suited for
Compliance certifications
OSS Apache-2.0 only.
Use with caution for
vLLM for production maturity. SGLang for agent specialty.
View analysis →TGI for HF ecosystem. SGLang for structured generation.
View analysis →Role: L4 LLM inference with agent specialty.
Upstream: Agent runtime requests.
Downstream: Completions + traces.
Mitigation: POC with representative agent workload.
RadixAttention specialty.
vLLM fits.
This analysis is AI-generated using the INPACT and GOALS frameworks from "Trust Before Intelligence." Scores and assessments are algorithmic and may not reflect the vendor's complete capabilities. Always validate with your own evaluation.