xLLM is an inference engine, meaning software that runs trained AI models to produce outputs from inputs, for large language, vision-language, diffusion, and recommendation models on different AI accelerators. Organizations use it to deploy these models with high-throughput and low-latency inference. The catalogue entries provide skills and instructions for working with xLLM.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/xllm-ai/xllm/agents-mdgit clone --depth 1 https://github.com/xLLM-AI/xllmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/xllm-ai/xllm/agents-md)<a href="https://agentmods.dev/instructions/xllm-ai/xllm/agents-md"><img src="https://agentmods.dev/badge/instructions/xllm-ai/xllm/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00805 | $0.00805 |
| Opus 5 | $0.00402 | $0.00402 |
| Sonnet 5 | $0.00161 | $0.00161 |
| Haiku 4.5 | $0.00081 | $0.00081 |
Grade A, and why
xllm AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
xLLM Coding Agent Instructions
Directory Structure
├── xllm/
| : main source folder
│ ├── api_service/ # code for api services
│ ├── c_api/ # code for c api
│ ├── cc_api/ # code for cc api
│ ├── core/
│ │ : xllm core features folder
│ │ ├── common/
│ │ ├── distributed_runtime/ # code for distributed and pd serving
│ │ ├── framework/ # code for execution orchestration
│ │ ├── kernels/ # adaption for npu kernels adaption
│ │ ├── layers/ # model layers impl
│ │ ├── platform/ # adaption for various platform
│ │ ├── runtime/ # code for worker and executor
│ │ ├── scheduler/ # code for batch and pd scheduler
│ │ └── util/
│ ├── function_call # code for tool call parser
│ ├── models/ # models impl
│ ├── parser/ # parser reasoning
│ ├── processors/ # code for vlm pre-processing
│ ├── proto/ # communication protocol
│ ├── pybind/ # code for python bind
| └── server/ # xLLM server
├── examples/ # examples of calling xLLM
├── tools/ # code for npu time generations
└── xllm.cpp # entrypoint of xLLM
Code Style Guide
- Before editing, creating, refactoring, or reviewing any file under
xllm/, you MUST read custom-code-style.md. - The file above is a required instruction file, not an optional reference. Do not skip reading it.
- After editing any C++ file, run clang-format 20.1.6 with the repo
.clang-formaton every touched file before finishing. Do not leave formatting for the user to remind you. - Apply the rules in custom-code-style.md to both code generation and code review.
- Follow DDD (Domain Driven Design) principles, and keep the codebase clean and maintainable.
- If custom-code-style.md specifies a rule, that rule takes precedence over the Google C++/Python Style Guide.
- Use the Google C++/Python Style Guide only for cases not specified in custom-code-style.md.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +1 lines · +42 tokens per session 42af87fcc699
- 5d ago First seen · 51 lines · 763 tokens per session scan A acf2fc8c680f
xllm AGENTS.md is an instructions file published in the GitHub repository xLLM-AI/xllm (1,557 stars, last pushed yesterday), licensed Apache-2.0. It adds 805 tokens to every session, about $0.0040 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
emmy AGENTS.md
AGENTS.md instructions for cloudrift-ai/emmy, covering agents.md, project overview, prerequisites, running tests and the realization corpus.
openinterpreter AGENTS.md
Instructions for openinterpreter/openinterpreter, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
emmy CLAUDE.md
Claude Code instructions for cloudrift-ai/emmy, a project described as: Optimized GPU compiler for LLM inference. Choose from a list of optimized recipes or optimize your own model via kernel fusion, autotuning, and advanced scheduling. Run benchmarks across different GPU types and configurations, track results and…
plano CLAUDE.md
Instructions for katanemo/plano, covering claude.md, build & test commands, rust — wasm plugins (must target wasm32-wasip1), rust — brightstaff binary (native target) and rust — tests, format, lint.
Tianshu-harness CLAUDE.md
Claude Code instructions for huiliyi37/Tianshu-harness, covering 天枢 (tianshu) / rivet, build & test, architecture, conventions and known constraints.
cherry-studio AGENTS.md
AGENTS.md instructions for CherryHQ/cherry-studio, a project described as: AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs.