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.
Best use case
openclaude-multi-llm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.
Teams using openclaude-multi-llm should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openclaude-multi-llm/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How openclaude-multi-llm Compares
| Feature / Agent | openclaude-multi-llm | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# OpenClaude Multi-LLM Skill
> Skill by [ara.so](https://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)
```bash
npm install -g @gitlawb/openclaude
# CLI command installed: openclaude
```
### From source (requires Bun)
```bash
git clone https://node.gitlawb.com/z6MkqDnb7Siv3Cwj7pGJq4T5EsUisECqR8KpnDLwcaZq5TPr/openclaude.git
cd openclaude
bun install
bun run build
# optionally link globally
npm link
```
### Run without build
```bash
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.
| Variable | Required | Purpose |
|---|---|---|
| `CLAUDE_CODE_USE_OPENAI` | Yes | Set to `1` to activate OpenAI provider |
| `OPENAI_API_KEY` | Yes* | API key (*omit for local Ollama/LM Studio) |
| `OPENAI_MODEL` | Yes | Model identifier |
| `OPENAI_BASE_URL` | No | Custom endpoint (default: `https://api.openai.com/v1`) |
| `CODEX_API_KEY` | Codex only | ChatGPT/Codex access token |
| `CODEX_AUTH_JSON_PATH` | Codex only | Path to Codex CLI `auth.json` |
`OPENAI_MODEL` takes priority over `ANTHROPIC_MODEL` if both are set.
---
## Provider Configuration Examples
### OpenAI
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$OPENAI_API_KEY
export OPENAI_MODEL=gpt-4o
openclaude
```
### DeepSeek
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$DEEPSEEK_API_KEY
export OPENAI_BASE_URL=https://api.deepseek.com/v1
export OPENAI_MODEL=deepseek-chat
openclaude
```
### Google Gemini (via OpenRouter)
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$OPENROUTER_API_KEY
export OPENAI_BASE_URL=https://openrouter.ai/api/v1
export OPENAI_MODEL=google/gemini-2.0-flash
openclaude
```
### Ollama (local, no API key needed)
```bash
ollama pull llama3.3:70b
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:11434/v1
export OPENAI_MODEL=llama3.3:70b
openclaude
```
### Groq
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$GROQ_API_KEY
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
export OPENAI_MODEL=llama-3.3-70b-versatile
openclaude
```
### Mistral
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$MISTRAL_API_KEY
export OPENAI_BASE_URL=https://api.mistral.ai/v1
export OPENAI_MODEL=mistral-large-latest
openclaude
```
### Azure OpenAI
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$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)
```bash
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=$CODEX_TOKEN
openclaude
```
### LM Studio (local)
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:1234/v1
export OPENAI_MODEL=your-model-name
openclaude
```
### Together AI
```bash
export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=$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
```bash
# 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.
```bash
# 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:
```bash
bun run dev:profile
```
---
## TypeScript Integration — Using the Shim Directly
If you want to use the shim in your own TypeScript code:
```typescript
// 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 });
```
```typescript
// 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
| Model | Tool Calling | Code Quality | Speed |
|---|---|---|---|
| GPT-4o | Excellent | Excellent | Fast |
| DeepSeek-V3 | Great | Great | Fast |
| Gemini 2.0 Flash | Great | Good | Very Fast |
| Llama 3.3 70B | Good | Good | Medium |
| Mistral Large | Good | Good | Fast |
| GPT-4o-mini | Good | Good | Very Fast |
| Qwen 2.5 72B | Good | Good | Medium |
| Models < 7B | Limited | Limited | Very 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:
```bash
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:
```bash
export CODEX_API_KEY=$YOUR_CODEX_TOKEN
```
Or point to a custom auth file:
```bash
export CODEX_AUTH_JSON_PATH=/path/to/auth.json
```
### Run diagnostics for any issue
```bash
bun run doctor:runtime # human-readable
bun run doctor:runtime:json # machine-readable JSON
bun run doctor:report # saves to reports/doctor-runtime.json
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