ApiClo 文档

适合 VS Code 中的自主编码任务。 ApiClo 可以通过两种方式连接。

选项 A：兼容 OpenAI

1. 1

   从 VS Code Marketplace 安装 Cline 并打开“设置/API 配置”。

2. 2

   选择 OpenAI Compatible 作为 API 提供商。

3. 3

   使用 /v1 设置完整的基本 URL：https://apiclo.com/v1。

4. 4

   API 密钥：您的 sk-hub 密钥。型号 ID：claude-sonnet-5 或 claude-opus-4-8。

5. 5

   保存配置文件并首先发送测试：仅回复即可。

选项 B：本机模式的人择消息

1. 1

   如果您的 Cline 构建需要 Anthropic 提供程序，请启用使用自定义基本 URL。

2. 2

   自定义基本 URL：https://apiclo.com。此处不要添加/v1； Cline 调用 /v1/messages 本身。

3. 3

   API 密钥：您的 sk-hub 密钥。型号：claude-sonnet-5 或 claude-opus-4-8。

4. 4

   如果 Anthropic 本机模式不稳定，请返回选项 A OpenAI 兼容。

保存后，发送：回复即可。第二次测试：查看当前文件夹，判断README.md是否存在；不要改变任何东西。

Images: select a vision-capable model and enable Supports images/Vision if your Cline version exposes that toggle. Start a new task after changing it and attach a PNG with a text question.

Roo Code 使用与 Cline 几乎相同的设置，但提供商配置文件标签可能不同。

1. 1

   从 VS Code 扩展安装 Roo Code 并打开 Roo Code 面板。

2. 2

   在 ApiClo 中的 API 密钥下创建 API 密钥。

3. 3

   打开 API 配置/提供商/配置文件。

4. 4

   默认情况下，选择 OpenAI 兼容的配置文件：基本 URL https://apiclo.com/v1 和 ApiClo 模型 ID。

5. 5

   仅针对本机/高级模式，使用 Anthropic + 自定义基本 URL https://apiclo.com，不带 /v1。

6. 6

   保存配置文件，验证“确定”，然后运行文件任务。

7. 7

   第二个测试：要求Roo读取当前文件夹并判断README.md是否存在，而不编辑文件。

Images: enable Vision/Supports images in the model profile when available. If Roo hides the attachment before the request, verify the same model through OpenCode or Harness with an explicit capability.

对于 Kilo，请使用自定义提供程序。与 Cline/Roo 不同，基本 URL 通常使用 /v1 输入。

1. 1

   从 VS Code 扩展安装 Kilo Code 并打开设置/API 配置。

2. 2

   在 ApiClo -> API 密钥中获取 sk-hub API 密钥。

3. 3

   创建自定义提供程序：提供程序 ID apiclaude、显示名称 ApiClo。

4. 4

   对于 Claude 模型，默认选择 Provider API：OpenAI Compatible 和 Base URL https://apiclo.com/v1。

5. 5

   仅当 Kilo 明确需要本机消息模式时才使用 Provider API：Anthropic Messages。

6. 6

   型号 ID：claude-sonnet-5、claude-opus-4-8 或 claude-fable-5-1。

7. 7

   保存提供程序并运行两个测试：仅回复 OK；然后读取项目文件列表而不进行编辑。

Images: set attachment: true and modalities with input [text, image], output [text] on the custom model. Without modalities, an unknown model is treated as text-only.

"attachment": true,
"modalities": {
  "input": ["text", "image"],
  "output": ["text"]
}

继续是通过config.yaml配置的。与模型进行聊天、编辑、应用和总结角色。

1. 1

   安装继续并打开配置：继续 -> 设置 -> 打开 config.yaml。

2. 2

   如果您已经有自定义模型，请在替换之前保存旧的 config.yaml。

3. 3

   粘贴下面的块并将 YOUR_API_KEY 替换为您的 sk-hub 密钥。

4. 4

   重新启动 继续或在 IDE 中运行 Reload Window。

5. 5

   在执行繁重任务之前，测试聊天、编辑和应用小文件。

name: ApiClo
version: 1.0.0
schema: v1
models:
  - name: Claude Sonnet 5
    provider: openai
    model: claude-sonnet-5
    apiBase: https://apiclo.com/v1
    apiKey: YOUR_API_KEY
    capabilities:
      - tool_use
    roles:
      - chat
      - edit
      - apply
      - summarize
    defaultCompletionOptions:
      temperature: 0
context:
  - provider: code
  - provider: docs
  - provider: diff
  - provider: terminal
  - provider: problems
  - provider: folder
  - provider: codebase

This example does not impose hard contextLength or maxTokens values. Continue uses the selected model's capabilities and shows the active configuration in Continue Console → Options.

In a long Agent session, click Compact conversation, the converging-arrows icon under the latest response. Once context reaches 60%, the same action is available from the context indicator beside the input. Continue keeps a technical summary and sends it with only the newer turns. This compacts history rather than truncating the active task. After compaction, inspect the next request in Continue Console: it should contain the summary and only newer turns, not hundreds of old tool_result blocks.

If Continue rejects provider: openai, use its OpenAI-compatible/custom provider with the same apiBase/apiKey/model. For model, use claude-sonnet-5 or claude-opus-4-8 without the apiclaude/ prefix.

Images: enable image input/vision for the selected custom model in your Continue version. If the field is unavailable, update Continue; a text-only request does not verify attachment delivery.

对于 VS Code 内置自定义端点，将模型添加到用户配置文件中。

1. 1

   打开 VS Code 用户设置文件夹并创建 chatLanguageModels.json（如果不存在）。

2. 2

   粘贴下面的 JSON 并将 YOUR_API_KEY 替换为您的 sk-hub 密钥。

3. 3

   检查 url 是否以 /v1/chat/completions 结尾。

4. 4

   运行开发人员：重新加载窗口。

5. 5

   在 VS Code 聊天中，选择 Claude Sonnet 5 并发送测试回复即可。

[
  {
    "name": "ApiClo",
    "vendor": "customendpoint",
    "apiKey": "YOUR_API_KEY",
    "apiType": "chat-completions",
    "models": [
      {
        "id": "claude-sonnet-5",
        "name": "Claude Sonnet 5",
        "url": "https://apiclo.com/v1/chat/completions",
        "toolCalling": true,
        "vision": true,
        "maxInputTokens": 1000000,
        "maxOutputTokens": 64000,
        "streaming": true
      },
      {
        "id": "claude-opus-4-8",
        "name": "Claude Opus 4.8",
        "url": "https://apiclo.com/v1/chat/completions",
        "toolCalling": true,
        "vision": true,
        "maxInputTokens": 1000000,
        "maxOutputTokens": 64000,
        "streaming": true
      }
    ]
  }
]

使用 Cursor 安全 ID，避免 Cursor 将其替换为内置模型。

1. 1

   打开 Cursor Settings → Models → API Keys，并在 OpenAI API Key 中保存 ApiClo sk-hub 密钥。

2. 2

   启用 Override OpenAI Base URL，并准确输入 https://apiclo.com/v1。

3. 3

   添加下方任一准确的自定义 ID。它们会出现在 /v1/models 中，并与原始模型使用相同的路由、回退和计费。

4. 4

   请选择准确的 apiclo-* 项，而不是 Cursor 内置的 GPT-5.6 Sol 或 Claude。

5. 5

   macOS 和 Windows 的设置相同。Cursor Tab 和其他专用功能可能仍会使用 Cursor 自己的模型。

6. 6

   发送“仅回复 OK”。如果 ApiClo Logs 中没有请求，请更新 Cursor 并确认已选择自定义 apiclo-* 模型。

这些 ID 同时支持 /v1/chat/completions 和 /v1/responses。如果仍有错误，请向支持团队发送新的 Cursor Request ID 和准确的 UTC 时间。

Trae 通过设置 -> 模型 -> 添加模型 -> 自定义配置进行连接。

1. 1

   安装 Trae 并登录；否则模型部分可能会被隐藏。

2. 2

   打开设置 -> 模型 -> 添加模型 -> 自定义配置。

3. 3

   对于 Claude，选择 API Format Anthropic Messages、URL https://apiclo.com 或 https://apiclo.com/v1（如果 Trae 要求完整的 /v1 端点）。

4. 4

   对于 OpenAI 完成，请使用 https://apiclo.com/v1 和模型 ID claude-sonnet-5。

5. 5

   粘贴 API 密钥，保存模型，然后运行仅回复 OK 测试。

6. 6

   如果 Trae 显示空答案，请减少最大输出并检查确切的 API 格式。

Images: select a vision-capable model and enable multimodal/vision/attachments in Custom Config if Trae exposes that option. After saving, always run a separate image + text test.

将 ApiClo 连接为 OpenAI 兼容提供商。

1. 1

   打开自托管 UI 的提供程序管理设置或环境配置。

2. 2

   选择与 OpenAI 兼容的提供商。

3. 3

   设置 OPENAI_API_BASE_URL=https://apiclo.com/v1 和 OPENAI_API_KEY=sk-hub-...

4. 4

   添加 DEFAULT_MODEL=claude-sonnet-5 或在 UI 中选择模型。

5. 5

   重新启动 UI 容器/进程并发送一个简短的测试。

Images: in Open WebUI or LibreChat enable vision capability on the custom model/connection. Having an API route alone does not make the UI attach a file.

如果 UI 添加 /v1 本身，请使用 root https://apiclo.com 或从设置中删除额外的 /v1。

Recommended quick path: the custom provider lives in opencode.json, and vision is declared in model capabilities.

1. 1

   Install OpenCode v2 from the official guide: macOS/Linux — npm install -g @opencode/cli; Windows — use the standalone CLI binary from OpenCode.

2. 2

   Create opencode.json in the project root and paste the configuration below.

3. 3

   Set APICLO_API_KEY in the OpenCode process environment; do not put the key in the project config.

4. 4

   Declare each model's actual capabilities. OpenCode v2 assumes text+image for an unknown model, but that is a fallback assumption, not an API check.

5. 5

   Run opencode, verify a short text response, then attach a small PNG/JPEG and ask it to read visible text.

macOS / Linux:

{
  "$schema": "https://opencode.ai/config.json",
  "providers": {
    "apiclaude": {
      "name": "ApiClo",
      "env": ["APICLO_API_KEY"],
      "package": "@opencode/ai/providers/openai-compatible",
      "settings": {
        "baseURL": "https://apiclo.com/v1"
      },
      "models": {
        "gpt-6-astra": {
          "name": "GPT 6 Astra",
          "capabilities": {
            "tools": true,
            "input": ["text", "image"],
            "output": ["text"]
          },
          "limit": {
            "context": 200000,
            "output": 128000
          }
        },
        "claude-fable-5-1": {
          "name": "Claude Fable 5.1",
          "capabilities": {
            "tools": true,
            "input": ["text", "image"],
            "output": ["text"]
          },
          "limit": {
            "context": 1000000,
            "output": 128000
          }
        }
      }
    }
  },
  "model": "apiclaude/gpt-6-astra"
}

This example targets OpenCode v2. Declare actual capabilities for other models; the v1 config is not interchangeable. On Windows, download the standalone binary below. OpenCode v2

Harness supports ApiClo as a custom OpenAI-compatible provider. Image input is enabled separately on each model.

1. 1

   Install DeepSeek Harness from its official instructions. In Settings → Models choose Add model provider → Custom model API.

2. 2

   Provider ID: apiclo; Display name: ApiClo; Base URL: https://apiclo.com/v1; API protocol: openai-completions.

3. 3

   Use the APICLO_API_KEY environment variable. If Fetch available models does not list an ID, add the model manually; configure its input types separately.

4. 4

   In Model options enable Text and Image. In the profile this is input: [text, image] on that model.

5. 5

   The change applies to the next request without a restart. If a problematic image remains in the session history, create a new session.

6. 6

   Test text first, then PNG/JPEG. If Harness says model does not support images before the request, the capability still is not saved on the selected model.

For the standard dsh web launch, use $DSH_HOME/profiles/web/cordis.patch.yml; replace web with your profile name otherwise. Preserve other entries when editing an existing file.

- id: llm-pi-ai
  config:
    providers:
      apiclo:
        apiKeyEnv: APICLO_API_KEY
        api: openai-completions
        baseURL: https://apiclo.com/v1
        models:
          - id: gpt-6-astra
            input: [text, image]
          - id: claude-fable-5-1
            input: [text, image]

# macOS / Linux
export APICLO_API_KEY="sk-hub-..."
dsh web

# Windows PowerShell
$env:APICLO_API_KEY="sk-hub-..."
dsh web

Claude Code 通常需要一个与 Anthropic 兼容的基本 URL，不带 /v1。

1. 1

   检查 Node.js 18+：node --version。

2. 2

   安装 Claude Code：npm install -g @anthropic-ai/claude-code@latest。

3. 3

   检查安装：claude --version。

4. 4

   对于 ApiClo，使用 ANTHROPIC_AUTH_TOKEN + ANTHROPIC_BASE_URL； ANTHROPIC_API_KEY 仅适用于需要 x-api-key 的客户端。

5. 5

   Set CLAUDE_CODE_AUTO_COMPACT_WINDOW=180000 so Claude Code summarizes a long session before the 200k boundary. This does not limit response size.

6. 6

   首先使用直接 /v1/messages 请求验证网关，然后从同一 shell 会话启动 claude。

7. 7

   在 Claude Code 中，运行 /status 并确认它显示 ApiClo 基本 URL 和活动令牌源。

# macOS / Linux
export ANTHROPIC_AUTH_TOKEN="sk-hub-..."
export ANTHROPIC_BASE_URL="https://apiclo.com"
export ANTHROPIC_MODEL="claude-sonnet-5"
export CLAUDE_CODE_AUTO_COMPACT_WINDOW="180000"
claude

# Windows PowerShell
$env:ANTHROPIC_AUTH_TOKEN="sk-hub-..."
$env:ANTHROPIC_BASE_URL="https://apiclo.com"
$env:ANTHROPIC_MODEL="claude-sonnet-5"
$env:CLAUDE_CODE_AUTO_COMPACT_WINDOW="180000"
claude

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://apiclo.com",
    "ANTHROPIC_AUTH_TOKEN": "sk-hub-...",
    "CLAUDE_CODE_AUTO_COMPACT_WINDOW": "180000"
  }
}

# macOS / Linux
curl -X POST "$ANTHROPIC_BASE_URL/v1/messages" \
  -H "Authorization: Bearer $ANTHROPIC_AUTH_TOKEN" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d "{\"model\":\"$ANTHROPIC_MODEL\",\"max_tokens\":1,\"messages\":[{\"role\":\"user\",\"content\":\".\"}]}"

# Windows PowerShell
Invoke-RestMethod -Method Post -Uri "$($env:ANTHROPIC_BASE_URL)/v1/messages" `
  -Headers @{ "Authorization" = "Bearer $env:ANTHROPIC_AUTH_TOKEN"; "anthropic-version" = "2023-06-01" } `
  -ContentType "application/json" `
  -Body (@{ model = $env:ANTHROPIC_MODEL; max_tokens = 1; messages = @(@{ role = "user"; content = "." }) } | ConvertTo-Json -Depth 4)

如果烟雾返回带有 msg_ 的 JSON，则 URL 和密钥有效。如果返回401，则客户端发送的密钥带有错误的标头；仅将 ANTHROPIC_API_KEY 用于 x-api-key 场景。

Images: in Claude Code attach a local file with the native command/button and select a vision-capable model. A separate provider flag is normally unnecessary, but an image + text smoke is required after changing the endpoint.

Codex CLI and the IDE extension can use ApiClo through a custom provider in config.toml.

1. 1

   In the Codex IDE extension, open Codex Settings → Open config.toml; the CLI uses the same ~/.codex/config.toml.

2. 2

   将提供程序添加到用户级配置中，因为项目 .codex/config.toml 不应定义提供程序/身份验证。

3. 3

   将 APICLAUDE_API_KEY 设置为您的 sk-hub 密钥。该变量名称由下面的 env_key 选择。

4. 4

   保持wire_api=response：在当前的Codex中，这是支持的提供者协议。

5. 5

   在测试文件夹中运行 codex 并验证仅回复 OK。

model = "claude-sonnet-5"
model_provider = "apiclaude"

[model_providers.apiclaude]
name = "ApiClo"
base_url = "https://apiclo.com/v1"
env_key = "APICLAUDE_API_KEY"
wire_api = "responses"

# macOS / Linux
export APICLAUDE_API_KEY="sk-hub-..."
codex

# Windows PowerShell
$env:APICLAUDE_API_KEY="sk-hub-..."
codex

如果 Codex 拒绝提供程序配置，请先更新 Codex。不要将wire_api切换为聊天完成：当前的Codex文档将响应描述为支持的提供者协议。

Images: in the IDE extension attach the image through the composer; in CLI provide the local path using the native flow. Test an image + text request separately: provider configuration alone does not prove delivery of the attachment.

OpenClaw 可以通过 models.json 与两种提供者模式连接。

1. 1

   为您的平台安装 OpenClaw 并检查 openclaw --version。

2. 2

   找到 OpenClaw 模型文件或在 config 文件夹中创建 models.json。

3. 3

   对于 Claude Messages 模式，请使用提供程序 apiclaude-messages 和 baseUrl https://apiclo.com。

4. 4

   对于 OpenAI Completions 模式，请使用提供程序 apiclaude-openai 和 baseUrl https://apiclo.com/v1。

5. 5

   Add input: [text, image] to every vision model, otherwise OpenClaw passes only a text media reference or omits the image.

6. 6

   Replace apiKey with an sk-hub key and run separate text, tool, and image tests in a project.

{
  "models": {
    "mode": "merge",
    "providers": {
      "apiclaude-messages": {
        "baseUrl": "https://apiclo.com",
        "apiKey": "sk-hub-...",
        "auth": "token",
        "api": "anthropic-messages",
        "models": [
          {
            "id": "claude-sonnet-5",
            "name": "claude-sonnet-5",
            "input": ["text", "image"],
            "contextWindow": 1000000,
            "maxTokens": 64000
          }
        ]
      },
      "apiclaude-openai": {
        "baseUrl": "https://apiclo.com/v1",
        "apiKey": "sk-hub-...",
        "auth": "token",
        "api": "openai-completions",
        "models": [
          { "id": "claude-opus-4-8", "name": "claude-opus-4-8", "input": ["text", "image"] }
        ]
      }
    }
  }
}

如果 Qwen Code 作为 OpenAI 兼容客户端安装，则 OPENAI_* 变量就足够了。

1. 1

   检查 Node.js：node --version。

2. 2

   安装 Qwen 代码：npm install -g @qwen-code/qwen-code@latest。

3. 3

   检查 qwen --version 和 qwen --help。

4. 4

   对于 OpenAI 兼容模式，设置 OPENAI_API_KEY、OPENAI_BASE_URL 和 OPENAI_MODEL。

5. 5

   如果您的 Qwen Code 版本支持 Anthropic env，则可以对 Claude 模型使用 ANTHROPIC_AUTH_TOKEN + https://apiclo.com。

6. 6

   Use images only if the installed Qwen Code version exposes attachment/image input for a custom endpoint; otherwise the client may send text only.

7. 7

   运行 qwen 并执行 OK/只读测试。

export OPENAI_API_KEY="sk-hub-..."
export OPENAI_BASE_URL="https://apiclo.com/v1"
export OPENAI_MODEL="claude-sonnet-5"
qwen

$env:OPENAI_API_KEY="sk-hub-..."
$env:OPENAI_BASE_URL="https://apiclo.com/v1"
$env:OPENAI_MODEL="claude-sonnet-5"
qwen

Aider 通过 OpenAI 兼容变量进行工作。

1. 1

   安装 Aider：python -m pip install -U aider-chat。

2. 2

   在 git 项目中打开一个终端，其中测试编辑是安全的。

3. 3

   设置 OPENAI_API_BASE 和 OPENAI_API_KEY。

4. 4

   运行 aider --model openai/claude-sonnet-5。

5. 5

   For an image, run /add image.png or /paste, then ask a text question; the selected model must support vision.

6. 6

   首先要求它解释一个文件；仅在验证连接后才允许编辑。

OMP connects through ~/.omp/agent/models.yml. Vision capability is declared with the model input field.

1. 1

   Install OMP with curl -fsSL https://omp.sh/install | sh; on Windows PowerShell use irm https://omp.sh/install.ps1 | iex.

2. 2

   Create ~/.omp/agent/models.yml and add the apiclo custom provider.

3. 3

   Set apiKey to the APICLO_API_KEY variable name instead of putting the secret in a repository.

4. 4

   Keep input: [text, image] on vision models: OMP checks this field before sending an attachment.

5. 5

   Run omp models find apiclo, then omp and test text, tools, and a separate image.

providers:
  apiclo:
    baseUrl: https://apiclo.com/v1
    api: openai-completions
    apiKey: APICLO_API_KEY
    models:
      - id: gpt-6-astra
        name: GPT 6 Astra
        input: [text, image]
        contextWindow: 200000
        maxTokens: 128000
      - id: claude-fable-5-1
        name: Claude Fable 5.1
        input: [text, image]
        contextWindow: 1000000
        maxTokens: 128000

Pi supports custom OpenAI-compatible models through models.json; declare image capability explicitly.

1. 1

   Install Pi using its official instructions and create ~/.pi/agent/models.json.

2. 2

   Set APICLO_API_KEY in the environment; do not store the key in a project file.

3. 3

   In /model, select apiclo/gpt-6-astra or apiclo/claude-fable-5-1.

4. 4

   Test a short text request, a tool call, and a separate image with a text question. Declaring input: image alone does not prove delivery.

{
  "providers": {
    "apiclo": {
      "baseUrl": "https://apiclo.com/v1",
      "api": "openai-completions",
      "apiKey": "$APICLO_API_KEY",
      "models": [
        { "id": "gpt-6-astra", "input": ["text", "image"], "contextWindow": 200000 },
        { "id": "claude-fable-5-1", "input": ["text", "image"], "contextWindow": 1000000 }
      ]
    }
  }
}

Kimi Code accepts a custom OpenAI-compatible provider; ApiClo compatibility is still undergoing our test matrix.

1. 1

   Install Kimi Code and open ~/.kimi-code/config.toml; preserve your existing settings.

2. 2

   Set APICLO_API_KEY in the environment and add an openai provider with a /v1 base URL.

3. 3

   Register the required model with image_in and tool_use, then select it in the client.

4. 4

   Test text, tools, reasoning, and images separately. Do not treat this integration as validated for autonomous tasks until the tests pass.

[providers.apiclo]
type = "openai"
base_url = "https://apiclo.com/v1"
api_key_env = "APICLO_API_KEY"

[models."apiclo/gpt-6-astra"]
provider = "apiclo"
model = "gpt-6-astra"
max_context_size = 200000
capabilities = ["image_in", "tool_use"]

Orca manages multiple CLI agents and isolated git worktrees. It does not replace the child agent's provider configuration.

1. 1

   First configure one working ApiClo executor: Codex, Claude Code, OpenCode, or OMP, including a separate vision test.

2. 2

   Install Orca Desktop/ADE from the official release for your OS and open the target git repository.

3. 3

   Add the already configured CLI as an agent runtime. The ApiClo key stays in the CLI's secure store or environment, not in Orca project files.

4. 4

   Use a separate worktree per agent for parallel work and cap the number of concurrent workers.

5. 5

   Send images to the specific child CLI session. Support is determined by that CLI's model capability: OpenCode modalities, Harness/OMP input, or Crush supports-images.

Crush supports OpenAI-compatible providers and a per-model supports-images flag.

1. 1

   Install current Crush and open the global crushrc at ~/.config/crush/crushrc or %USERPROFILE%\.config\crush\crushrc.

2. 2

   Add an apiclo provider of type openai-compat and disable automatic addition of unknown models.

3. 3

   Register each model with model add and set --supports-images true only for vision models.

4. 4

   Select the large model with model large and start Crush in a test project.

5. 5

   On Windows, include a text question with the image: some Crush versions reject an image-only prompt.

provider add apiclo \
  --name "ApiClo" \
  --type openai-compat \
  --base-url "https://apiclo.com/v1" \
  --api-key "$APICLO_API_KEY" \
  --discover-models false

model add apiclo/gpt-6-astra \
  --name "GPT 6 Astra" \
  --context-window 200000 \
  --default-max-tokens 128000 \
  --can-reason true \
  --supports-images true

model large apiclo/gpt-6-astra

Goose supports custom OpenAI-compatible providers in Desktop and CLI. Verify image capability in the installed version with a separate test.

1. 1

   Open Settings → Models → Configure providers → Add Custom Provider.

2. 2

   Provider Type: OpenAI Compatible; Display Name: ApiClo; API URL: https://apiclo.com/v1/chat/completions.

3. 3

   Enable Authentication, save the sk-hub key, and add gpt-6-astra and claude-fable-5-1.

4. 4

   Enable Streaming Support and verify a short text request.

5. 5

   The current Goose custom-provider screen has no universal per-model vision flag. If the attachment button is hidden or the file is removed before the request, use OpenCode, Harness, OMP, or Crush where the capability is explicit.

Gemini CLI 目前不是一个好的直接 ApiClo 目标，除非您的构建允许您设置自定义 OpenAI/Anthropic 端点​​。

1. 1

   首先检查您的 Gemini CLI 版本是否有自定义基本 URL 设置。

2. 2

   如果自定义端点不可用，请勿尝试覆盖 Google 系统变量：使用 Cline、Roo、Kilo 或 Qwen Code。

3. 3

   如果自定义 OpenAI 兼容端点可用，请使用基本 URL https://apiclo.com/v1、sk-hub API 密钥和模型 claude-sonnet-5。

4. 4

   Verify images with a separate test: the custom endpoint must receive an image part, not only a local path. If that Gemini CLI version cannot do this, use OpenCode, Harness, OMP, or Crush.

5. 5

   设置后，仅运行只读测试。如果客户端仍然调用 Google 端点，请切换到支持的 CLI。

内置 Hermes 位于 ApiClo 内部：平衡、模型、内存和历史记录均由代理管理。

1. 1

   打开代理并选择 Hermes。

2. 2

   从付费列表中选择一个模型：claude-sonnet-5、claude-opus-4-8、claude-sonnet-4-6。

3. 3

   对于网页，在聊天中启用网络上下文； URL 将通过 ApiClo 阅读器/浏览器后备读取。

4. 4

   For an image, use the composer attachment and a vision-capable model; after upload ask a text question to distinguish real image input from a filename.

5. 5

   要让 Hermes 使用您的计算机，请下载 Desktop Bridge，创建配对代码，然后仅授予所需的文件或文件夹权限。

6. 6

   对于长任务，避免无缘无故地在链中切换模型；它有助于缓存和上下文。

当客户端仅与本地主机通信或您想要将密钥集中在计算机上时，本地路由器非常有用。

1. 1

   下载 ApiClo Desktop Bridge 并在与 IDE/CLI 相同的计算机上运行它。

2. 2

   将上游 sk-hub 密钥粘贴到 Desktop Bridge 中。

3. 3

   单击“获取模型”并确保加载模型列表。

4. 4

   单击启动路由器。

5. 5

   在 IDE/CLI 中，设置本地基本 URL http://127.0.0.1:18441/v1 和任何非空客户端 API 密钥。

6. 6

   For images, enable the capability in the IDE/CLI itself: Desktop Router transparently forwards multipart/base64 image content but does not create an attachment button or declare vision for the client.

7. 7

   仅测试回复即可；如果没有应答，请检查本地路由器是否正在运行以及端口 18441 是否空闲。

首先单击“获取模型”，然后单击“启动路由器”。在 IDE 中，使用本地基本 URL，而不是公共 URL。

IDE、SDK 和大多数自托管接口的主要路线。

curl https://apiclo.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [{"role": "user", "content": "Hi"}],
    "max_tokens": 512,
    "stream": true
  }'

将此用于需要 Claude Messages API 的客户端。

curl https://apiclo.com/v1/messages \
  -H "x-api-key: YOUR_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "max_tokens": 512,
    "messages": [{"role": "user", "content": "Hi"}]
  }'

将此用于需要响应有线 API 的客户端。

curl https://apiclo.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "input": "Write a short checklist for release QA."
  }'

适合脚本、后端作业和测试。

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://apiclo.com/v1",
)

response = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "Hi"}],
    max_tokens=512,
)

print(response.choices[0].message.content)

适合前端支持的 API 路由和 Node.js 服务。

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.APICLAUDE_API_KEY,
  baseURL: "https://apiclo.com/v1",
});

const response = await client.chat.completions.create({
  model: "claude-sonnet-5",
  messages: [{ role: "user", content: "Hi" }],
  max_tokens: 512,
});

console.log(response.choices[0].message.content);