from src.llm.backend import CompletionResult, ToolCallResult from src.llm.history_adapters import ( AnthropicHistoryAdapter, GeminiHistoryAdapter, OpenAIHistoryAdapter, ) def test_anthropic_history_adapter_preserves_thinking_blocks() -> None: adapter = AnthropicHistoryAdapter() result = CompletionResult( content="Done", thinking_blocks=[ { "type": "thinking", "thinking": "private reasoning", "signature": "sig_123", } ], tool_calls=[ ToolCallResult(id="tool_1", name="search", input={"query": "honcho"}) ], ) message = adapter.format_assistant_tool_message(result) assert message["role"] == "assistant" assert message["content"][0]["type"] == "thinking" assert message["content"][1] == {"type": "text", "text": "Done"} assert message["content"][2]["type"] == "tool_use" def test_gemini_history_adapter_preserves_thought_signature() -> None: adapter = GeminiHistoryAdapter() result = CompletionResult( content="Calling a tool", tool_calls=[ ToolCallResult( id="tool_1", name="search", input={"query": "honcho"}, thought_signature="sig_abc", ) ], ) message = adapter.format_assistant_tool_message(result) assert message["role"] == "model" assert message["parts"][1]["thought_signature"] == "sig_abc" def test_openai_history_adapter_preserves_reasoning_details() -> None: adapter = OpenAIHistoryAdapter() result = CompletionResult( content="Calling a tool", reasoning_details=[{"type": "reasoning", "content": "step 1"}], tool_calls=[ ToolCallResult(id="tool_1", name="search", input={"query": "honcho"}) ], ) message = adapter.format_assistant_tool_message(result) assert message["role"] == "assistant" assert message["reasoning_details"] == [{"type": "reasoning", "content": "step 1"}] assert message["tool_calls"][0]["function"]["name"] == "search"