MCPInstall

openclaude-multi-llm

Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.

Installs
717
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/trending-skills --skill openclaude-multi-llm
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads openclaude-multi-llm automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

OpenClaude Multi-LLM Skill

Skill by ara.so — Daily 2026 Skills collection.

OpenClaude is a fork of Claude Code that routes all LLM calls through an OpenAI-compatible shim (openaiShim.ts), letting you use any model that speaks the OpenAI Chat Completions API — GPT-4o, DeepSeek, Gemini via OpenRouter, Ollama, Groq, Mistral, Azure, and more — while keeping every Claude Code tool intact (Bash, FileRead, FileWrite, FileEdit, Glob, Grep, WebFetch, Agent, MCP, Tasks, LSP, NotebookEdit).


Installation

npm (recommended)

npm install -g @gitlawb/openclaude
# CLI command installed: openclaude

From source (requires Bun)

git clone https://node.gitlawb.com/z6MkqDnb7Siv3Cwj7pGJq4T5EsUisECqR8KpnDLwcaZq5TPr/openclaude.git
cd openclaude
bun install
bun run build
# optionally link globally
npm link

Run without build

bun run dev       # run directly with Bun, no build step

Activation — Required Environment Variables

You must set CLAUDE_CODE_USE_OPENAI=1 to enable the shim. Without it, the tool falls back to the Anthropic SDK.

VariableRequiredPurpose
CLAUDE_CODE_USE_OPENAIYesSet to 1 to activate OpenAI provider
OPENAI_API_KEYYes*API key (*omit for local Ollama/LM Studio)
OPENAI_MODELYesModel identifier
OPENAI_BASE_URLNoCustom endpoint (default: https://api.openai.com/v1)
CODEX_API_KEYCodex onlyChatGPT/Codex access token
CODEX_AUTH_JSON_PATHCodex onlyPath to Codex CLI auth.json

OPENAI_MODEL takes priority over ANTHROPIC_MODEL if both are set.


Provider Configuration Examples

OpenAI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$OPENAI_API_KEY
export OPENAI_MODEL=gpt-4o
openclaude

DeepSeek

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$DEEPSEEK_API_KEY
export OPENAI_BASE_URL=https://api.deepseek.com/v1
export OPENAI_MODEL=deepseek-chat
openclaude

Google Gemini (via OpenRouter)

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$OPENROUTER_API_KEY
export OPENAI_BASE_URL=https://openrouter.ai/api/v1
export OPENAI_MODEL=google/gemini-2.0-flash
openclaude

Ollama (local, no API key needed)

ollama pull llama3.3:70b

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:11434/v1
export OPENAI_MODEL=llama3.3:70b
openclaude

Groq

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$GROQ_API_KEY
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
export OPENAI_MODEL=llama-3.3-70b-versatile
openclaude

Mistral

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$MISTRAL_API_KEY
export OPENAI_BASE_URL=https://api.mistral.ai/v1
export OPENAI_MODEL=mistral-large-latest
openclaude

Azure OpenAI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$AZURE_OPENAI_KEY
export OPENAI_BASE_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment/v1
export OPENAI_MODEL=gpt-4o
openclaude

Codex (ChatGPT backend)

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_MODEL=codexplan   # or codexspark for faster loops
# reads ~/.codex/auth.json automatically if present
# or set: export CODEX_API_KEY=$CODEX_TOKEN
openclaude

LM Studio (local)

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:1234/v1
export OPENAI_MODEL=your-model-name
openclaude

Together AI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$TOGETHER_API_KEY
export OPENAI_BASE_URL=https://api.together.xyz/v1
export OPENAI_MODEL=meta-llama/Llama-3.3-70B-Instruct-Turbo
openclaude

Architecture — How the Shim Works

The shim file is src/services/api/openaiShim.ts (724 lines). It duck-types the Anthropic SDK interface so the rest of Claude Code is unaware it's talking to a different provider.

Claude Code Tool System
        │
        ▼
Anthropic SDK interface (duck-typed)
        │
        ▼
openaiShim.ts  ← format translation layer
        │
        ▼
OpenAI Chat Completions API
        │
        ▼
Any compatible model

What the shim translates

  • Anthropic message content blocks → OpenAI messages array
  • Anthropic tool_use / tool_result blocks → OpenAI function_calls / tool messages
  • OpenAI SSE streaming chunks → Anthropic stream events
  • Anthropic system prompt arrays → OpenAI system role messages

Files changed from upstream

src/services/api/openaiShim.ts   ← NEW: the shim (724 lines)
src/services/api/client.ts       ← routes to shim when CLAUDE_CODE_USE_OPENAI=1
src/utils/model/providers.ts     ← added 'openai' provider type
src/utils/model/configs.ts       ← added openai model mappings
src/utils/model/model.ts         ← respects OPENAI_MODEL for defaults
src/utils/auth.ts                ← recognizes OpenAI as valid 3rd-party provider

Developer Workflow — Key Commands

# Run in dev mode (no build)
bun run dev

# Build distribution
bun run build

# Launch with persisted profile (.openclaude-profile.json)
bun run dev:profile

# Launch with OpenAI profile (requires OPENAI_API_KEY in shell)
bun run dev:openai

# Launch with Ollama profile (localhost:11434, llama3.1:8b default)
bun run dev:ollama

# Launch with Codex profile
bun run dev:codex

# Quick startup sanity check
bun run smoke

# Validate provider env + reachability
bun run doctor:runtime

# Machine-readable runtime diagnostics
bun run doctor:runtime:json

# Persist diagnostics report to reports/doctor-runtime.json
bun run doctor:report

# Full local hardening check (typecheck + smoke + runtime doctor)
bun run hardening:check

# Strict hardening (includes project-wide typecheck)
bun run hardening:strict

Profile Bootstrap — One-Time Setup

Profiles save provider config to .openclaude-profile.json so you don't repeat env exports.

# Auto-detect provider (ollama if running, otherwise openai)
bun run profile:init

# Bootstrap for OpenAI
bun run profile:init -- --provider openai --api-key $OPENAI_API_KEY

# Bootstrap for Ollama with custom model
bun run profile:init -- --provider ollama --model llama3.1:8b

# Bootstrap for Codex
bun run profile:init -- --provider codex --model codexspark
bun run profile:codex

After bootstrapping, run the app via the persisted profile:

bun run dev:profile

TypeScript Integration — Using the Shim Directly

If you want to use the shim in your own TypeScript code:

// src/services/api/client.ts pattern — routing to the shim
import { openaiShim } from './openaiShim.js';

const useOpenAI = process.env.CLAUDE_CODE_USE_OPENAI === '1';

const client = useOpenAI
  ? openaiShim({
      apiKey: process.env.OPENAI_API_KEY,
      baseURL: process.env.OPENAI_BASE_URL ?? 'https://api.openai.com/v1',
      model: process.env.OPENAI_MODEL ?? 'gpt-4o',
    })
  : new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
// Streaming usage pattern (mirrors Anthropic SDK interface)
const stream = await client.messages.stream({
  model: process.env.OPENAI_MODEL!,
  max_tokens: 32000,
  system: 'You are a helpful coding assistant.',
  messages: [
    { role: 'user', content: 'Refactor this function for readability.' }
  ],
  tools: myTools, // Anthropic-format tool definitions — shim translates them
});

for await (const event of stream) {
  // events arrive in Anthropic format regardless of underlying provider
  if (event.type === 'content_block_delta') {
    process.stdout.write(event.delta.text ?? '');
  }
}

Model Quality Reference

ModelTool CallingCode QualitySpeed
GPT-4oExcellentExcellentFast
DeepSeek-V3GreatGreatFast
Gemini 2.0 FlashGreatGoodVery Fast
Llama 3.3 70BGoodGoodMedium
Mistral LargeGoodGoodFast
GPT-4o-miniGoodGoodVery Fast
Qwen 2.5 72BGoodGoodMedium
Models < 7BLimitedLimitedVery Fast

For agentic multi-step tool use, prefer models with strong native function/tool calling (GPT-4o, DeepSeek-V3, Gemini 2.0 Flash).


What Works vs. What Doesn't

Fully supported

  • All tools: Bash, FileRead, FileWrite, FileEdit, Glob, Grep, WebFetch, WebSearch, Agent, MCP, LSP, NotebookEdit, Tasks
  • Streaming (real-time token output)
  • Multi-step tool chains
  • Vision/images (base64 and URL) for models that support them
  • Slash commands: /commit, /review, /compact, /diff, /doctor
  • Sub-agents (AgentTool spawns sub-agents using the same provider)
  • Persistent memory

Not supported (Anthropic-specific features)

  • Extended thinking / reasoning mode
  • Prompt caching (Anthropic cache headers skipped)
  • Anthropic beta feature headers
  • Token output defaults to 32K max (gracefully capped if model is lower)

Troubleshooting

doctor:runtime fails with placeholder key error

Error: OPENAI_API_KEY looks like a placeholder (SUA_CHAVE)

Set a real key: export OPENAI_API_KEY=$YOUR_ACTUAL_KEY

Ollama connection refused

Ensure Ollama is running before launching:

ollama serve &
ollama pull llama3.3:70b
bun run dev:ollama

Tool calls not working / model ignores tools

Switch to a model with strong tool calling support (GPT-4o, DeepSeek-V3). Models under 7B parameters often fail at multi-step agentic tool use.

Azure endpoint format

The OPENAI_BASE_URL for Azure must include the deployment path:

https://<resource>.openai.azure.com/openai/deployments/<deployment>/v1

Codex auth not found

If ~/.codex/auth.json doesn't exist, set the token directly:

export CODEX_API_KEY=$YOUR_CODEX_TOKEN

Or point to a custom auth file:

export CODEX_AUTH_JSON_PATH=/path/to/auth.json

Run diagnostics for any issue

bun run doctor:runtime       # human-readable
bun run doctor:runtime:json  # machine-readable JSON
bun run doctor:report        # saves to reports/doctor-runtime.json

FAQ

What does the openclaude-multi-llm skill do?

Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.

How do I install openclaude-multi-llm?

Run npx skills add https://github.com/reason-machines/trending-skills --skill openclaude-multi-llm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/openclaude-multi-llm/ (personal) or .claude/skills/openclaude-multi-llm/ (project).

Which AI agents can use openclaude-multi-llm?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is openclaude-multi-llm safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains openclaude-multi-llm?

It is published by reason-machines in reason-machines/trending-skills. It has 717 recorded installs.

More from reason-machines/trending-skills

View all →

openclaw-control-center

reason-machines/trending-skills

Local-first, security-first control center for OpenClaw agents — visibility dashboard with readonly defaults, token attribution, collaboration tracing, and safe write operations.

Data & Databases4.4K installs

inkos-multi-agent-novel-writing

reason-machines/trending-skills

Multi-agent CLI system for autonomous novel writing, auditing, and revision with human review gates

Docs & Writing2.8K installs

claude-hud-statusline

reason-machines/trending-skills

A Claude Code plugin that displays a real-time HUD showing context usage, active tools, running agents, and todo progress in your terminal statusline.

understand-anything-knowledge-graph

reason-machines/trending-skills

Turn any codebase into an interactive knowledge graph using Claude Code skills — explore, search, and ask questions about any project visually.

agency-agents-ai-specialists

reason-machines/trending-skills

A collection of specialized AI agent personalities for Claude Code, Cursor, Aider, Windsurf, and other AI coding tools — covering engineering, design, marketing, sales, and more.

AI & Agents2.1K installs

agent-browser-automation

reason-machines/trending-skills

Headless browser automation CLI for AI agents using native Rust binary with Chrome DevTools Protocol

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs