> For the complete documentation index, see [llms.txt](https://seekspeed.gitbook.io/seekspeed-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://seekspeed.gitbook.io/seekspeed-docs/readme.md). # Introduction > A performance terminal for AI agents. Benchmark latency, find bottlenecks, and optimize real agent performance across prompts, tools, and model backends — built on the DeepSeek inference stack and powered by DeepSpec for speculative-decoding workloads. > > **Official URL:** SeekSpeed Terminal is a full-stack workbench for teams running production LLM agents. It measures what actually matters — time-to-first-token (TTFT), inter-token latency, tail latency under concurrency, cost per task, and acceptance rate for speculative decoders — and then proves whether your "optimization" is real with a Welch's *t*-test instead of vibes. It is **not** another playground. Every benchmark is a real HTTP call against your real endpoint. Every recommendation runs through an `open → applied → measured → accepted/rejected` loop. Every speedup claim against a closed-weight provider is gated behind an honesty banner. ![SeekSpeed Terminal — Gotta Go Faster](/files/49rxfNDDLPHgDNatP1Zy) ## Why it exists The default failure mode of agent engineering is benchmarking from the README. A vendor advertises 200 tok/s, a paper claims 2.5× speculative-decoding speedup, and the team ships those numbers into a roadmap deck without ever measuring the system end-to-end. SeekSpeed Terminal closes that loop: 1. **Connect** any OpenAI-compatible endpoint — hosted (OpenAI, Anthropic via proxy, Groq, Together, Azure, OpenRouter) or self-hosted (vLLM, TGI, llama.cpp, SGLang). 2. **Profile** the agent — system prompt, tool layout, memory mode, token budget. 3. **Benchmark** with multi-iteration runs, real SSE-streamed TTFT capture, and concurrency sweeps to find the breaking point. 4. **Optimize** with a rule-based engine that proposes concrete, applicable variants (prompt slimming, model routing, semantic cache, retry/backoff, token budget). 5. **Speculate** in the Spec Lab — wire a DeepSpec or DSpark draft/target pair against your vLLM or TGI server and watch the acceptance rate, tokens-per-step, and breakeven curve in real time. 6. **Report** — export JSON, CSV, or a printable HTML snapshot with statistical significance and honesty notes attached. 7. **Guard** — pin a run as a baseline, schedule it on a `15m` / `hourly` / `daily` / `weekly` cadence, and get in-app + webhook alerts when p95, cost, throughput, or error rate regress past your thresholds (gated by Welch's *t*-test). ## Who it's for * Inference platform teams running DeepSeek, Qwen, Llama or other open-weight models behind vLLM / TGI / SGLang who need to *prove* that DSpark or DeepSpec speculative decoding pays off in their workload, not just on the benchmark suite. * Agent product teams shipping voice, code, or workflow agents who feel slow and don't know if the bottleneck is the prompt, the model, the network, or the tool layer. * Research engineers comparing draft/target model pairings, batch sizes, and quantization configs with statistical rigor rather than single-shot eyeball comparisons. ## Honesty policy SeekSpeed Terminal will not claim that a hosted, closed-weight provider can be accelerated at the inference layer. You cannot run speculative decoding on someone else's GPU. For hosted APIs we focus on **workflow acceleration** — prompt slimming, context compression, tool concurrency, model routing, semantic caching, token budget tuning, retry reduction. Inference-level acceleration lives in the Spec Lab and is restricted to self-hosted or open-weight stacks behind a gate. Continue to [What is SeekSpeed Terminal →](/seekspeed-docs/getting-started/what-is-seekspeed.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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