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oclaw/docs/LLM_TRANSPORTS.md
oliver 4a23b715a2 重构仓库目录为统一的 runtime 分层并清理历史 openclaw 残留。 
本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。

Made-with: Cursor
2026-04-25 01:24:23 +08:00

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LLM provider/transport capability matrix (oclaw)

This project follows an Oclaw-style explicit provider/transport selection:

  • You select the transport via LLM profile mode (not by inferring from base_url).
  • base_url can be the same unified gateway URL for all providers; the mode determines the wire protocol.

Profile field mapping

  • mode: transport selector (openai, openai_responses, anthropic, google, ollama, rule)
  • model: model id/name passed to the provider transport
  • base_url: gateway host URL (may be shared across providers)
  • api_key (profile secret): primary credential source (reused across modes)

Transport selection happens in oclaw/runtime/agents/factory.py.

Modes and transports

openai (Chat Completions, OpenAI-compatible)

  • Transport: oclaw/platform/llm/transports/openai_chat_completions.py::OpenAIChatModel
  • API: /v1/chat/completions (streaming supported)
  • Tools: OpenAI tool calling (tools[] / tool_calls)
  • Streaming: token deltas via on_token → WS chat.delta
  • Key: profile secret or OPENAI_API_KEY

openai_responses (Responses API, OpenAI-compatible)

  • Transport: oclaw/platform/llm/transports/openai_responses.py::OpenAIResponsesModel
  • API: /v1/responses (streaming events)
  • Tools: function call items parsed from response output
  • Streaming: output text deltas via on_token → WS chat.delta
  • Key: profile secret or OPENAI_API_KEY

anthropic (Anthropic Messages streaming)

  • Transport: oclaw/platform/llm/transports/anthropic_messages.py::AnthropicMessagesModel
  • API: Anthropic messages.stream surface (gateway must provide Anthropic-compatible protocol)
  • Tools: tool use blocks assembled into LLMToolCall
  • Streaming: text deltas via on_token → WS chat.delta
  • Key: profile secret or ANTHROPIC_API_KEY (fallback: OPENAI_API_KEY for unified gateways)

google (Gemini native SSE)

  • Transport: oclaw/platform/llm/transports/google_gemini_sse.py::GoogleGeminiChatModel
  • API: :streamGenerateContent?alt=sse (Gemini native)
  • Tools: functionDeclarations with parametersJsonSchema; parses functionCall
  • Streaming: text deltas via on_token → WS chat.delta
  • Key: profile secret or GOOGLE_API_KEY/GEMINI_API_KEY (fallback: OPENAI_API_KEY for unified gateways)
  • Thinking controls (optional env):
    • AIA_GEMINI_THINKING=on|off
    • AIA_GEMINI_THINKING_LEVEL=<string>
    • AIA_GEMINI_THINKING_BUDGET=<int>

ollama (local OpenAI-compatible)

  • Transport: OpenAIChatModel with Ollama-compatible base url
  • API: /v1/chat/completions (Ollama OpenAI-compat)
  • Key: uses a dummy key (ollama) if needed by SDK

rule (no remote LLM)

  • Transport: oclaw/platform/llm/transports/simple.py::RuleBasedChatModel
  • Purpose: deterministic tool routing/diagnostics without any model provider

Streaming and UI contract

All transports stream assistant output through the same internal callback:

  1. Transport calls on_token(text_delta)
  2. WS gateway maps that to event=chat payload.state=delta
  3. Final persisted assistant message is emitted as:
    • event=chat payload.state=final (with message payload for folding)
    • event=session.message
  4. Tool UI signals from runtime are emitted as event=session.tool

Adding the remaining models

For additional providers, follow this pattern:

  1. Add a new transport class under oclaw/platform/llm/transports/
  2. Extend mode selection in oclaw/runtime/agents/factory.py
  3. Add an offline stream parser test under tests/
  4. Add/verify WS contract tests (delta + final + session.tool)