Doubao 1.5 UI-TARS 250328 is a ByteDance model for GUI-operation workflows. The maker’s computer-use application names this exact edition as one of its selectable GUI models. In that workflow, a written task guides interaction with a computer environment, while the surrounding application executes operations and makes their result visible.
Give the application a concrete task and explain what completion should look like on screen. For a presentation task, identify the material to use, the intended slides and the file to produce. For a settings task, name the setting and the desired value. Clear conditions help the workflow distinguish progress from a completed result.
State the relevant constraints with the task: which application or document is involved, which material to preserve and which final artifact to return. If the screen contains several similar controls, identify the target through its visible label and surrounding context. The next observation should show whether the action reached the intended state.
The documented computer-use service connects model decisions with mouse control, keyboard control and screen capture in a remote computer environment. The service and computer instance carry out the actual actions; the model is one component of that arrangement. The task’s useful outcome is the changed application state or saved artifact, supported by the observations collected during execution.
For a reusable workflow, keep the task, selected model identity and sequence of observations together. After an action, compare the resulting screen with the completion condition before proceeding. If the expected control or result is absent, use that observation to refine the next instruction.
The selected UI-TARS model does not itself create a computer instance, retain a logged-in application session or save a presentation file. Those are functions of the surrounding application and its configured environment. The maker’s walkthrough demonstrates this separation by creating the computer-use application, selecting a GUI model and starting a sandbox before submitting the task.
This entry preserves the 250328 edition. Later UI-TARS releases and the separate Thinking Vision model have their own identities. The exact UI-TARS profile is useful for screen-based operation decisions; its action format and input limits should follow the application’s implementation for this edition.
", "aboutText": "Doubao 1.5 UI-TARS 250328 is a ByteDance model for GUI-operation workflows. The maker’s computer-use application names this exact edition as one of its selectable GUI models. In that workflow, a written task guides interaction with a computer environment, while the surrounding application executes operations and makes their result visible.\n\n### Describe the task and its completion state\n\nGive the application a concrete task and explain what completion should look like on screen. For a presentation task, identify the material to use, the intended slides and the file to produce. For a settings task, name the setting and the desired value. Clear conditions help the workflow distinguish progress from a completed result.\n\nState the relevant constraints with the task: which application or document is involved, which material to preserve and which final artifact to return. If the screen contains several similar controls, identify the target through its visible label and surrounding context. The next observation should show whether the action reached the intended state.\n\n### Observe, decide and execute\n\nThe documented computer-use service connects model decisions with mouse control, keyboard control and screen capture in a remote computer environment. The service and computer instance carry out the actual actions; the model is one component of that arrangement. The task’s useful outcome is the changed application state or saved artifact, supported by the observations collected during execution.\n\nFor a reusable workflow, keep the task, selected model identity and sequence of observations together. After an action, compare the resulting screen with the completion condition before proceeding. If the expected control or result is absent, use that observation to refine the next instruction.\n\n### Model role and application role\n\nThe selected UI-TARS model does not itself create a computer instance, retain a logged-in application session or save a presentation file. Those are functions of the surrounding application and its configured environment. The maker’s walkthrough demonstrates this separation by creating the computer-use application, selecting a GUI model and starting a sandbox before submitting the task.\n\nThis entry preserves the 250328 edition. Later UI-TARS releases and the separate Thinking Vision model have their own identities. The exact UI-TARS profile is useful for screen-based operation decisions; its action format and input limits should follow the application’s implementation for this edition.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Choose the next GUI step from a text observation\n\nPlan one next step for this GUI task from the supplied text observation only. Goal: turn off weekly email summaries. Screen text: Notifications; Weekly summary [on]; Security alerts [on]; Save changes. Name the control to change, the subsequent save step and the observation needed to confirm completion. Do not claim to click anything.A short action plan using visible labels and a post-save verification condition.Only Weekly summary changes; Security alerts stays on and completion requires a new observed saved state.doubao-1.5-ui-tars-250328Doubao 1.5 UI-TARS 250328 is a GUI-operation model for an application that turns a task and screen observations into computer actions. The model supplies action decisions; the surrounding computer-use service executes them and returns the next observation.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Doubao 1.5 UI-TARS 250328 is a ByteDance model for GUI-operation workflows. The maker’s computer-use application names this exact edition as one of its selectable GUI models. In that workflow, a written task guides interaction with a computer environment, while the surrounding application executes operations and makes their result visible.
Give the application a concrete task and explain what completion should look like on screen. For a presentation task, identify the material to use, the intended slides and the file to produce. For a settings task, name the setting and the desired value. Clear conditions help the workflow distinguish progress from a completed result.
State the relevant constraints with the task: which application or document is involved, which material to preserve and which final artifact to return. If the screen contains several similar controls, identify the target through its visible label and surrounding context. The next observation should show whether the action reached the intended state.
The documented computer-use service connects model decisions with mouse control, keyboard control and screen capture in a remote computer environment. The service and computer instance carry out the actual actions; the model is one component of that arrangement. The task’s useful outcome is the changed application state or saved artifact, supported by the observations collected during execution.
For a reusable workflow, keep the task, selected model identity and sequence of observations together. After an action, compare the resulting screen with the completion condition before proceeding. If the expected control or result is absent, use that observation to refine the next instruction.
The selected UI-TARS model does not itself create a computer instance, retain a logged-in application session or save a presentation file. Those are functions of the surrounding application and its configured environment. The maker’s walkthrough demonstrates this separation by creating the computer-use application, selecting a GUI model and starting a sandbox before submitting the task.
This entry preserves the 250328 edition. Later UI-TARS releases and the separate Thinking Vision model have their own identities. The exact UI-TARS profile is useful for screen-based operation decisions; its action format and input limits should follow the application’s implementation for this edition.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits doubao-1.5-ui-tars-250328 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.001 / 1K tokens Output · Output tokens $0.0034 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "doubao-1.5-ui-tars-250328", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='doubao-1.5-ui-tars-250328',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='doubao-1.5-ui-tars-250328',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra doubao-1.5-ui-tars-250328
Use the method paths shown above.
doubao-1.5-ui-tars-250328Doubao 1.5 UI-TARS 250328 is a GUI-operation model for an application that turns a task and screen observations into computer actions. The model supplies action decisions; the surrounding computer-use service executes them and returns the next observation.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Doubao 1.5 UI-TARS 250328 is a ByteDance model for GUI-operation workflows. The maker’s computer-use application names this exact edition as one of its selectable GUI models. In that workflow, a written task guides interaction with a computer environment, while the surrounding application executes operations and makes their result visible.
Give the application a concrete task and explain what completion should look like on screen. For a presentation task, identify the material to use, the intended slides and the file to produce. For a settings task, name the setting and the desired value. Clear conditions help the workflow distinguish progress from a completed result.
State the relevant constraints with the task: which application or document is involved, which material to preserve and which final artifact to return. If the screen contains several similar controls, identify the target through its visible label and surrounding context. The next observation should show whether the action reached the intended state.
The documented computer-use service connects model decisions with mouse control, keyboard control and screen capture in a remote computer environment. The service and computer instance carry out the actual actions; the model is one component of that arrangement. The task’s useful outcome is the changed application state or saved artifact, supported by the observations collected during execution.
For a reusable workflow, keep the task, selected model identity and sequence of observations together. After an action, compare the resulting screen with the completion condition before proceeding. If the expected control or result is absent, use that observation to refine the next instruction.
The selected UI-TARS model does not itself create a computer instance, retain a logged-in application session or save a presentation file. Those are functions of the surrounding application and its configured environment. The maker’s walkthrough demonstrates this separation by creating the computer-use application, selecting a GUI model and starting a sandbox before submitting the task.
This entry preserves the 250328 edition. Later UI-TARS releases and the separate Thinking Vision model have their own identities. The exact UI-TARS profile is useful for screen-based operation decisions; its action format and input limits should follow the application’s implementation for this edition.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits doubao-1.5-ui-tars-250328 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.001 / 1K tokens Output · Output tokens $0.0034 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "doubao-1.5-ui-tars-250328",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'doubao-1.5-ui-tars-250328', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'doubao-1.5-ui-tars-250328', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra doubao-1.5-ui-tars-250328
Use the method paths shown above.
Technical example in English
Choose the next GUI step from a text observation\n\nPlan one next step for this GUI task from the supplied text observation only. Goal: turn off weekly email summaries. Screen text: Notifications; Weekly summary [on]; Security alerts [on]; Save changes. Name the control to change, the subsequent save step and the observation needed to confirm completion. Do not claim to click anything.A short action plan using visible labels and a post-save verification condition.Only Weekly summary changes; Security alerts stays on and completion requires a new observed saved state.