Palmyra X5 is WRITER’s general-purpose enterprise model for large-context tasks and agent workflows. The manufacturer specifies a 1M-token context and up to 8192 output tokens, with adaptive reasoning. Text and images can be combined in a chat request, and the model generates a textual answer or proposed function calls for the application.
Its architecture combines linear and softmax attention with a mixture-of-experts design. The large context can hold the instructions, retrieved documents and results from earlier tool interactions together. Prepare that working set around a defined objective and leave room for the final answer rather than treating the input capacity and output limit as the same quantity.
The producer organizes its use cases around multi-step enterprise work. Support documentation can classify a request, assess urgency and prepare an update for review. Content lifecycle work can identify material that needs revision and propose a replacement. Financial or fund-reporting tasks can combine the supplied research and records into an analysis, while the surrounding application retrieves the actual data and executes any action.
Large-data applications include feedback analysis, research summaries and comparison of lengthy reports. State the common criteria and the fields or conclusions the result should contain. A useful report distinguishes the source’s explicit information from an inference and keeps the supporting passage or record tied to each important finding.
For image analysis, provide one or more images with the written question in the same conversation. Identify the visible detail to inspect and supply any separate document context needed for interpretation. The producer’s image guide applies specifically to X5; image generation and later X6 agent-product features are different capabilities.
Function calling connects the model to the application’s functions through their definitions and argument schemas. The application executes a requested call, appends the result and continues the conversation. If several calls are returned, preserve the association between each call and its result before asking for the final response. A streamed call must be assembled from its chunks before execution.
Request a final artifact appropriate to the task: an executive summary, a structured set of findings, a revision draft or a proposed next action with evidence. Keep Palmyra X5 separate from X4 and X6 and from platform-specific context variants. The model’s answer and adaptive reasoning support the workflow; the application’s tools and observations establish the actual outcome.
", "aboutText": "Palmyra X5 is WRITER’s general-purpose enterprise model for large-context tasks and agent workflows. The manufacturer specifies a 1M-token context and up to 8192 output tokens, with adaptive reasoning. Text and images can be combined in a chat request, and the model generates a textual answer or proposed function calls for the application.\n\nIts architecture combines linear and softmax attention with a mixture-of-experts design. The large context can hold the instructions, retrieved documents and results from earlier tool interactions together. Prepare that working set around a defined objective and leave room for the final answer rather than treating the input capacity and output limit as the same quantity.\n\nThe producer organizes its use cases around multi-step enterprise work. Support documentation can classify a request, assess urgency and prepare an update for review. Content lifecycle work can identify material that needs revision and propose a replacement. Financial or fund-reporting tasks can combine the supplied research and records into an analysis, while the surrounding application retrieves the actual data and executes any action.\n\nLarge-data applications include feedback analysis, research summaries and comparison of lengthy reports. State the common criteria and the fields or conclusions the result should contain. A useful report distinguishes the source’s explicit information from an inference and keeps the supporting passage or record tied to each important finding.\n\nFor image analysis, provide one or more images with the written question in the same conversation. Identify the visible detail to inspect and supply any separate document context needed for interpretation. The producer’s image guide applies specifically to X5; image generation and later X6 agent-product features are different capabilities.\n\nFunction calling connects the model to the application’s functions through their definitions and argument schemas. The application executes a requested call, appends the result and continues the conversation. If several calls are returned, preserve the association between each call and its result before asking for the final response. A streamed call must be assembled from its chunks before execution.\n\nRequest a final artifact appropriate to the task: an executive summary, a structured set of findings, a revision draft or a proposed next action with evidence. Keep Palmyra X5 separate from X4 and X6 and from platform-specific context variants. The model’s answer and adaptive reasoning support the workflow; the application’s tools and observations establish the actual outcome.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Revise an outdated support article\n\nRevise this support paragraph using the approved change note. Existing: Reports are emailed every day to all workspace members. Change note C1: Reports are emailed weekly to workspace administrators only; users can still download reports manually. Return a replacement paragraph and a change table mapping each edited claim to C1. Avoid adding scheduling controls.A replacement support paragraph and a concise old-claim/new-claim/source table.Check that frequency, recipient group and manual download behavior all match C1, with no invented interface controls.writer/palmyra-x5WRITER’s Palmyra X5 is an enterprise model for long-context document work and multi-step agents. It combines a 1M context, adaptive reasoning, image-aware chat and function calling, with up to 8192 output tokens in the producer’s current specification.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Palmyra X5 is WRITER’s general-purpose enterprise model for large-context tasks and agent workflows. The manufacturer specifies a 1M-token context and up to 8192 output tokens, with adaptive reasoning. Text and images can be combined in a chat request, and the model generates a textual answer or proposed function calls for the application.
Its architecture combines linear and softmax attention with a mixture-of-experts design. The large context can hold the instructions, retrieved documents and results from earlier tool interactions together. Prepare that working set around a defined objective and leave room for the final answer rather than treating the input capacity and output limit as the same quantity.
The producer organizes its use cases around multi-step enterprise work. Support documentation can classify a request, assess urgency and prepare an update for review. Content lifecycle work can identify material that needs revision and propose a replacement. Financial or fund-reporting tasks can combine the supplied research and records into an analysis, while the surrounding application retrieves the actual data and executes any action.
Large-data applications include feedback analysis, research summaries and comparison of lengthy reports. State the common criteria and the fields or conclusions the result should contain. A useful report distinguishes the source’s explicit information from an inference and keeps the supporting passage or record tied to each important finding.
For image analysis, provide one or more images with the written question in the same conversation. Identify the visible detail to inspect and supply any separate document context needed for interpretation. The producer’s image guide applies specifically to X5; image generation and later X6 agent-product features are different capabilities.
Function calling connects the model to the application’s functions through their definitions and argument schemas. The application executes a requested call, appends the result and continues the conversation. If several calls are returned, preserve the association between each call and its result before asking for the final response. A streamed call must be assembled from its chunks before execution.
Request a final artifact appropriate to the task: an executive summary, a structured set of findings, a revision draft or a proposed next action with evidence. Keep Palmyra X5 separate from X4 and X6 and from platform-specific context variants. The model’s answer and adaptive reasoning support the workflow; the application’s tools and observations establish the actual outcome.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits writer/palmyra-x5 | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0012 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0012 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "writer/palmyra-x5", "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='writer/palmyra-x5',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='writer/palmyra-x5',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 writer/palmyra-x5
Use the method paths shown above.
writer/palmyra-x5WRITER’s Palmyra X5 is an enterprise model for long-context document work and multi-step agents. It combines a 1M context, adaptive reasoning, image-aware chat and function calling, with up to 8192 output tokens in the producer’s current specification.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Palmyra X5 is WRITER’s general-purpose enterprise model for large-context tasks and agent workflows. The manufacturer specifies a 1M-token context and up to 8192 output tokens, with adaptive reasoning. Text and images can be combined in a chat request, and the model generates a textual answer or proposed function calls for the application.
Its architecture combines linear and softmax attention with a mixture-of-experts design. The large context can hold the instructions, retrieved documents and results from earlier tool interactions together. Prepare that working set around a defined objective and leave room for the final answer rather than treating the input capacity and output limit as the same quantity.
The producer organizes its use cases around multi-step enterprise work. Support documentation can classify a request, assess urgency and prepare an update for review. Content lifecycle work can identify material that needs revision and propose a replacement. Financial or fund-reporting tasks can combine the supplied research and records into an analysis, while the surrounding application retrieves the actual data and executes any action.
Large-data applications include feedback analysis, research summaries and comparison of lengthy reports. State the common criteria and the fields or conclusions the result should contain. A useful report distinguishes the source’s explicit information from an inference and keeps the supporting passage or record tied to each important finding.
For image analysis, provide one or more images with the written question in the same conversation. Identify the visible detail to inspect and supply any separate document context needed for interpretation. The producer’s image guide applies specifically to X5; image generation and later X6 agent-product features are different capabilities.
Function calling connects the model to the application’s functions through their definitions and argument schemas. The application executes a requested call, appends the result and continues the conversation. If several calls are returned, preserve the association between each call and its result before asking for the final response. A streamed call must be assembled from its chunks before execution.
Request a final artifact appropriate to the task: an executive summary, a structured set of findings, a revision draft or a proposed next action with evidence. Keep Palmyra X5 separate from X4 and X6 and from platform-specific context variants. The model’s answer and adaptive reasoning support the workflow; the application’s tools and observations establish the actual outcome.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits writer/palmyra-x5 | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0012 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0012 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "writer/palmyra-x5",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'writer/palmyra-x5', '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': 'writer/palmyra-x5', '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 writer/palmyra-x5
Use the method paths shown above.
Technical example in English
Revise an outdated support article\n\nRevise this support paragraph using the approved change note. Existing: Reports are emailed every day to all workspace members. Change note C1: Reports are emailed weekly to workspace administrators only; users can still download reports manually. Return a replacement paragraph and a change table mapping each edited claim to C1. Avoid adding scheduling controls.A replacement support paragraph and a concise old-claim/new-claim/source table.Check that frequency, recipient group and manual download behavior all match C1, with no invented interface controls.