开源MCP代理

Install

Quickstart

npm install opencode-llm-proxy

Add to opencode.json:

{
  "plugin": ["opencode-llm-proxy"]
}

Start OpenCode — the proxy starts automatically:

opencode

This package is an OpenCode plugin, not a standalone server. It intentionally has no npm start command; load it through OpenCode as shown above.

Send a request:

curl http://127.0.0.1:4010/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "github-copilot/claude-sonnet-4.6",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

---

Configuration

Use x-opencode-variant to select an OpenCode model variant for a request. The proxy accepts multimodal image, document, and file inputs in each API's native content shape, using embedded data URLs and validating model capabilities. Remote URLs are rejected unless the SSRF-safe remote-media fetcher is explicitly enabled; fetched content is converted to a data URL before it reaches OpenCode. Structured JSON output is supported through OpenAI response_format.json_schema, Responses API text.format.schema, and Gemini generationConfig.responseSchema.

Generation temperature, top-p (top_p/topP), and top-k (topK) values are validated and applied through the plugin's chat.params hook. Maximum-token fields (max_tokens, max_completion_tokens, max_output_tokens, and Gemini maxOutputTokens) are accepted where clients require them, but the current OpenCode SDK cannot enforce them. OpenAI and Anthropic requests reject unsupported controls (stop, seed, frequency_penalty, presence_penalty, logprobs, and n) with 400 instead of silently ignoring them.

OPENCODE_LLM_PROXY_HOST=0.0.0.0 \
OPENCODE_LLM_PROXY_TOKEN=my-secret \
opencode

---

SDKs & clients

See also: Security · Comparisons · All recipes

---

Using with SDKs and tools

OpenAI SDK (JS/TS)

import OpenAI from "openai"

const client = new OpenAI({
  baseURL: "http://127.0.0.1:4010/v1",
  apiKey: "unused",
})

const response = await client.chat.completions.create({
  model: "github-copilot/claude-sonnet-4.6",
  messages: [{ role: "user", content: "Explain recursion." }],
})

OpenAI SDK (Python)

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:4010/v1", api_key="unused")

response = client.chat.completions.create(
    model="ollama/qwen2.5-coder",
    messages=[{"role": "user", "content": "Write a Python function to reverse a string."}],
)
print(response.choices[0].message.content)

Anthropic SDK (Python)

import anthropic

client = anthropic.Anthropic(
    base_url="http://127.0.0.1:4010",
    api_key="unused",
)

message = client.messages.create(
    model="anthropic/claude-3-5-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What is the Pythagorean theorem?"}],
)
print(message.content[0].text)

Anthropic SDK (JS/TS)

import Anthropic from "@anthropic-ai/sdk"

const client = new Anthropic({
  baseURL: "http://127.0.0.1:4010",
  apiKey: "unused",
})

const message = await client.messages.create({
  model: "anthropic/claude-opus-4",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain async/await." }],
})

Google Generative AI SDK (JS/TS)

import { GoogleGenerativeAI } from "@google/generative-ai"

const genAI = new GoogleGenerativeAI("unused", {
  baseUrl: "http://127.0.0.1:4010",
})

const model = genAI.getGenerativeModel({ model: "google/gemini-2.0-flash" })
const result = await model.generateContent("What is machine learning?")
console.log(result.response.text())

API reference

npm plugin (recommended)

npm install opencode-llm-proxy

Add to your global ~/.config/opencode/opencode.json (works everywhere) or a project-level opencode.json:

{
  "plugin": ["opencode-llm-proxy"]
}

Continue (VS Code / JetBrains)

In ~/.continue/config.json:

{
  "models": [
    {
      "title": "Claude via OpenCode",
      "provider": "openai",
      "model": "anthropic/claude-3-5-sonnet",
      "apiBase": "http://127.0.0.1:4010/v1",
      "apiKey": "unused"
    }
  ]
}