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Add generate_stream method to LLM clients for streaming responses: - Add StreamChunk schema for partial response chunks (thinking, content, tool_call_delta, tool_call_complete, done) - Add generate_stream abstract method to LLMClientBase - Implement streaming in OpenAIClient via chat.completions.create(stream=True) - Implement streaming in AnthropicClient via messages.stream() - Add generate_stream to LLMClient wrapper - Buffer partial tool calls and emit tool_call_complete when JSON is complete - Add tests for streaming functionality Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…back - Add `stream` parameter to Agent (default True) enabling real-time token output in the agent loop - Refactor run() to dispatch to _run_step_stream() or _run_step_nonstream() - Extract tool call execution into _execute_tool_calls() helper - Streaming: buffers thinking/content, emits tool_call_complete when JSON parses, executes all tools after done event - Add --no-stream CLI flag to disable streaming and use generate() - All 12 streaming tests pass Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Anthropic SDK sends: - type="text"/"thinking" as TOP-LEVEL event types (content in event.text/event.thinking) - type="content_block_delta" wraps content in block.delta.text / block.delta.thinking - type="signature" events carry no streamable content Also fix unit test mocks to match real SDK event structure. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…tput - Add sys.stdout.reconfigure(line_buffering=False) in cli.py to force unbuffered output at the file descriptor level - Use sys.stdout.write() + flush() instead of print() in the streaming loop in agent.py for immediate character-by-character display - This ensures thinking and content chunks appear live rather than buffering until the stream completes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
MiniMax API sends BOTH top-level text/thinking events AND content_block_delta events with identical content. Previously both were yielded, creating duplicate chunks. Fix: use top-level text/thinking events only (they arrive first with complete content), skip content_block_delta for text/thinking, use content_block_delta only for tool_use blocks. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
generate_stream()async generator method across all LLM clients (Anthropic, OpenAI)StreamChunkschema with chunk types:thinking,content,tool_call_delta,tool_call_complete,done_run_step_stream()in agent.py that streams thinking and content live--no-streamCLI flag to disable streamingsys.stdout.reconfigure(line_buffering=False)) for real-time terminal outputTest plan
Closes #71