- Include images from all message roles (not just user)
- Add multipart content support for tool responses
- Images from MCP tools now accessible in same agentic turn
* Move `task_result_state::update_chat_msg` to match with header
* Move `server_task_result_cmpl_partial::to_json_anthropic()` to match with header
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Co-authored-by: openingnow <>
* from previous PR
* Make instruction(system) as first message
* Convert [input_message] (text/image/file)
* Rename convert_responses_to_chatcmpl(body) -> response_body
* Initial tool call support
* Erase instructions field from chatcmpl body
* Feed reasoning texts to chat template
* Use std::vector instead of opaque json array
* Make output_item.added events consistent
* Move `server_task_result_cmpl_partial::update` from header to source
* Match ID of output_item.added and .done events
* Add function_call only if there is no "fc_" prefix
* Add function call output at non-streaming API
* Test if ID is persistent
* Add doc
* Fix style - use trailing comma
* Rewrite state management
* catch up with upstream/master
* Fix style - "type" is the first item of SSE data
* Explicitly check "instructions" from response_body
* Make lambdas static
* Check if reasoning content exists
* Add `oai_resp_id` to task_result_state(also initialized at ctor), server_task_result_cmpl_partial, and server_task_result_cmpl_final
* Reject `input_file` since it is not supported by chatcmpl
* Add "fc_" prefix to non-straming function call id as coderabbit pointed out
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Co-authored-by: openingnow <>
Refactors MCP server enabling logic to remove the dependency on global settings.
This simplifies the logic by directly checking the per-chat override status, and removes the need to pass the global enabled state as a parameter.
Additionally:
- Only shows MCP servers that are enabled in settings in the selector.
- Sorts the servers by whether they are enabled for the current chat.
Remove all frontend validation logic that prevented users from selecting
models based on multimodal capabilities. This refactoring removes
restrictive UI code while maintaining full functionality
- Vision models can describe images as text
- That text remains useful for non-vision models
- Chaining vision -> non-vision is a valid workflow
- Users know their use case better than the UI
- Users can return to vision models when needed
Aleksander Grygier
Pascal
Xuan-Son Nguyen
손희준
Hendrik Erz
Adrien Gallouët
Lennart Austenfeld