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 repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It goes beyond basic chat checks to test full agent workflows (tool loops, streaming, URL normalization), and provides machine-readable reports and capability requirements for CI/CD.
Who it is for
- Developers integrating with LLM relays
- AI agent builders
- DevOps/CI engineers
- Quality assurance teams testing endpoint compatibility
Use cases
- Validate an LLM relay before building an agent
- Generate client config for Codex or OpenAI SDK
- Automate endpoint capability checks in CI pipeline
- Diagnose why a relay works for chat but fails for tool calls
Strengths
- Zero runtime dependencies for CLI
- Supports multiple protocols (OpenAI Chat, Responses, Anthropic)
- Agent-friendly local JSON config with .gitignore
- Machine-enforced capability requirements with exit codes
Considerations
- Does not validate long-run JSON Schema / Structured Outputs stability
- Does not generate dedicated Cline and OpenCode configuration templates
- Does not support hosted MCP or provider-hosted tools (planned for future releases)
README quick start
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
Description
Diagnose OpenAI-compatible and Anthropic LLM endpoints: discover URLs/models, verify SSE and tool loops, and generate Codex configs.
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