# Libllm

> libllm - LLM API client for OpenAI-compatible endpoints. LlmApi class handles chat completions and embeddings via HTTP. Supports GitHub Models, Azure OpenAI, and standard OpenAI endpoints. Handles streaming responses, token counting, and multi-tool parallel call fixes. Use for LLM completions, embeddings, and AI model integration.

- Skill: `majiayu000/libllm` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/libllm`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/libllm/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/libllm

---


# libllm Skill

## When to Use

- Making chat completion requests to LLM providers
- Generating text embeddings for vector search
- Integrating with OpenAI-compatible APIs
- Handling streaming LLM responses

## Key Concepts

**LlmApi**: HTTP client for OpenAI-compatible endpoints. Handles authentication,
streaming, and response parsing.

**DEFAULT_MAX_TOKENS**: Standard token limit for completions.

## Usage Patterns

### Pattern 1: Chat completion

```javascript
import { LlmApi } from "@copilot-ld/libllm";

const api = new LlmApi(config, logger);
const response = await api.completion([{ role: "user", content: "Hello" }], {
  model: "gpt-4",
  maxTokens: 1000,
});
```

### Pattern 2: Generate embeddings

```javascript
const embeddings = await api.embed(["text to embed"]);
// Returns array of vectors
```

## Integration

Used by LLM service. Configurable via environment for different providers
(OpenAI, Azure, GitHub Models).