0xwilliamortiz
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openclaude-improved
OpenClaude is an open-source CLI coding agent that runs on any platform and supports a wide range of LLM providers, offering the same tools and workflows as Claude Code.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它不仅检查基本聊天功能，还能测试完整的 agent 工作流（工具调用循环、流式传输、URL 标准化），并提供机器可读的报告和 CI/CD 的能力要求检查。
适合谁使用
- 集成 LLM 中转服务的开发者
- AI agent 构建者
- DevOps/CI 工程师
- 测试端点兼容性的质量保证团队
典型使用场景
- 在构建 agent 前验证 LLM 中转端点
- 为 Codex 或 OpenAI SDK 生成客户端配置
- 在 CI 流水线中自动化端点能力检查
- 诊断中转端点聊天正常但工具调用失败的原因
项目优势
- CLI 零运行时依赖
- 支持多种协议（OpenAI Chat、Responses、Anthropic）
- agent 友好的本地 JSON 配置文件并自动加入 .gitignore
- 通过退出码强制执行能力要求检查
使用前须知
- 不验证长期 JSON Schema / Structured Outputs 稳定性
- 不生成专门的 Cline 和 OpenCode 配置模板
- 不支持托管 MCP 或提供商托管的工具（计划未来版本）
README 快速开始
LLM Endpoint Doctor
Give it an LLM relay URL and API key. Get the correct base URL, supported protocols, models, streaming status, protocol-specific tool compatibility, and ready-to-copy client config.
Status: usable V0.2 MVP. The CLI has zero runtime dependencies.
When do you need it?
You have been given an LLM relay, a self-hosted model service, or an OpenAI-compatible API. Basic chat appears to work, but you do not know whether the endpoint can support a real agent or automated workflow.
Typical situations include:
- You want to build an agent but do not know whether the relay supports a full tool-call continuation.
- You want to connect Codex, Cline, OpenCode, or another coding agent but do not know whether it needs Chat Completions or the Responses API.
- You are preparing data labeling, batch classification, or structured extraction and need to establish that basic generation and streaming work.
- You do not know whether the Base URL needs
/v1, or whether you received a complete endpoint URL. - Normal chat succeeds, but the model cannot continue after a tool result is returned.
- You want client-side MCP but do not know whether the API has the required function-calling behavior.
- A relay claims OpenAI compatibility without saying which protocols and models actually work.
- The same model works through an official API but returns unexplained 400, 404, or streaming errors through the relay.
LLM Endpoint Doctor performs automatic discovery and small capability probes before you commit to an integration. It does more than ask the endpoint to reply to “hello”: it tests whether the endpoint can support practical applications, agents, and automation.
What it answers
- Does the base URL need
/v1? - Is the input a base URL or a full
/chat/completions//responsesendpoint? - Does the relay support OpenAI Chat Completions, OpenAI Responses, or Anthropic Messages?
- Can
/modelsbe read? Which text model should be tried? - Does SSE streaming actually emit recognizable events?
- Can each protocol complete a full function/tool loop?
- Is client-side MCP li
项目描述
Diagnose OpenAI-compatible and Anthropic LLM endpoints: discover URLs/models, verify SSE and tool loops, and generate Codex configs.
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