Weaver Alpha is Mancer’s experimental system for roleplay and narrative writing. The manufacturer describes a combination of smaller models intended to produce expansive prose, catalogued as a LLaMA 2-based 13B system. It takes written scene instructions and context and generates text. Its defining role is creative, verbose continuation, rather than a promise of the coherent long-term memory or broad capabilities of another model.
Give the current setting, viewpoint, character motivations and the event that should happen next. Identify which facts are established and which details the model may invent. For roleplay, state who controls each character and whether the response should contain narration, dialogue or both. A useful brief is: “Continue this scene from the narrator’s viewpoint, preserve the character’s stated goal and stop after the new complication is revealed.”
For an atmospheric passage, describe the intended mood and the details that should carry it: surroundings, pace, voice and the action at the centre of the scene. Request the length and stopping point you need. A verbose style can supply texture, but a scene still benefits from a clear dramatic purpose instead of an instruction to elaborate without direction.
The maker requires Alpaca prompt formatting for its documented completion workflow. Its published simplified format distinguishes the system instruction, user instruction and assistant response. Keep those roles separate and preserve the model’s own format when preparing a continuation. A surrounding chat workflow may handle that formatting, but the displayed model name alone does not determine an application’s prompt construction.
The manufacturer’s suggested starting settings are temperature 0.9, repetition penalty 1.1, top-p 0.58 and top-k 50. These are producer recommendations for the experimental profile, not settings inferred for every application. They provide a starting point for the prose style; the actual result still needs to be reviewed for repetition, voice and adherence to the scene.
Weaver’s maker explicitly cautions about coherence and memory. Keep the recent scene state and essential character facts in the input when the story continues. A concise continuity note can identify what has happened, what the characters know and what must not change. If a generated passage contradicts that note, give the specific mismatch and request a revision tied to the established events.
The producer profile lists an 8,000-token context and a maximum output of 6,000 tokens. Source material, instructions, conversation and completion need to fit within the applicable budget; the output limit is not additional space beyond the context. The maker also states that this profile does not support multi-swipe generation. Treat a generated continuation as a single result to inspect, rather than assuming simultaneous alternatives from one request.
The result can be a scene continuation, descriptive passage or character exchange. Review whether it preserves viewpoint, character agency, chronology and the requested ending. For an edit, identify the exact passage to change and the meaning to retain. Keep successful story facts in the next prompt instead of expecting a long unseen history to remain available. Weaver Alpha’s practical value is its creative prose profile, with its experimental identity and documented continuity limits kept visible in how the task is prepared.
", "aboutText": "Weaver Alpha is Mancer’s experimental system for roleplay and narrative writing. The manufacturer describes a combination of smaller models intended to produce expansive prose, catalogued as a LLaMA 2-based 13B system. It takes written scene instructions and context and generates text. Its defining role is creative, verbose continuation, rather than a promise of the coherent long-term memory or broad capabilities of another model.\n\n### Prepare a narrative scene\n\nGive the current setting, viewpoint, character motivations and the event that should happen next. Identify which facts are established and which details the model may invent. For roleplay, state who controls each character and whether the response should contain narration, dialogue or both. A useful brief is: “Continue this scene from the narrator’s viewpoint, preserve the character’s stated goal and stop after the new complication is revealed.”\n\nFor an atmospheric passage, describe the intended mood and the details that should carry it: surroundings, pace, voice and the action at the centre of the scene. Request the length and stopping point you need. A verbose style can supply texture, but a scene still benefits from a clear dramatic purpose instead of an instruction to elaborate without direction.\n\n### Prompt and producer controls\n\nThe maker requires Alpaca prompt formatting for its documented completion workflow. Its published simplified format distinguishes the system instruction, user instruction and assistant response. Keep those roles separate and preserve the model’s own format when preparing a continuation. A surrounding chat workflow may handle that formatting, but the displayed model name alone does not determine an application’s prompt construction.\n\nThe manufacturer’s suggested starting settings are temperature 0.9, repetition penalty 1.1, top-p 0.58 and top-k 50. These are producer recommendations for the experimental profile, not settings inferred for every application. They provide a starting point for the prose style; the actual result still needs to be reviewed for repetition, voice and adherence to the scene.\n\n### Continuity and limits\n\nWeaver’s maker explicitly cautions about coherence and memory. Keep the recent scene state and essential character facts in the input when the story continues. A concise continuity note can identify what has happened, what the characters know and what must not change. If a generated passage contradicts that note, give the specific mismatch and request a revision tied to the established events.\n\nThe producer profile lists an 8,000-token context and a maximum output of 6,000 tokens. Source material, instructions, conversation and completion need to fit within the applicable budget; the output limit is not additional space beyond the context. The maker also states that this profile does not support multi-swipe generation. Treat a generated continuation as a single result to inspect, rather than assuming simultaneous alternatives from one request.\n\n### A useful writing artifact\n\nThe result can be a scene continuation, descriptive passage or character exchange. Review whether it preserves viewpoint, character agency, chronology and the requested ending. For an edit, identify the exact passage to change and the meaning to retain. Keep successful story facts in the next prompt instead of expecting a long unseen history to remain available. Weaver Alpha’s practical value is its creative prose profile, with its experimental identity and documented continuity limits kept visible in how the task is prepared.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Reveal a scene complication\n\nContinue in close third person through cartographer Nera. She has reached an abandoned observatory to recover her stolen atlas; her companion believes it burned. Write 200 words of atmospheric prose with one brief exchange. Preserve those facts, then stop when Nera finds a freshly drawn map of their route inside the supposedly empty dome.An atmospheric scene continuation around 200 words, ending on the discovery rather than resolving it.Check viewpoint, the companion's belief and the requested stopping event remain consistent.mancer/weaverWeaver Alpha is Mancer’s experimental text-generation system for roleplay and narrative prose. Its verbose creative style works best with an explicit scene, character brief and recent continuity notes; the producer profile lists an 8,000-token context and a 6,000-token output limit.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Weaver Alpha is Mancer’s experimental system for roleplay and narrative writing. The manufacturer describes a combination of smaller models intended to produce expansive prose, catalogued as a LLaMA 2-based 13B system. It takes written scene instructions and context and generates text. Its defining role is creative, verbose continuation, rather than a promise of the coherent long-term memory or broad capabilities of another model.
Give the current setting, viewpoint, character motivations and the event that should happen next. Identify which facts are established and which details the model may invent. For roleplay, state who controls each character and whether the response should contain narration, dialogue or both. A useful brief is: “Continue this scene from the narrator’s viewpoint, preserve the character’s stated goal and stop after the new complication is revealed.”
For an atmospheric passage, describe the intended mood and the details that should carry it: surroundings, pace, voice and the action at the centre of the scene. Request the length and stopping point you need. A verbose style can supply texture, but a scene still benefits from a clear dramatic purpose instead of an instruction to elaborate without direction.
The maker requires Alpaca prompt formatting for its documented completion workflow. Its published simplified format distinguishes the system instruction, user instruction and assistant response. Keep those roles separate and preserve the model’s own format when preparing a continuation. A surrounding chat workflow may handle that formatting, but the displayed model name alone does not determine an application’s prompt construction.
The manufacturer’s suggested starting settings are temperature 0.9, repetition penalty 1.1, top-p 0.58 and top-k 50. These are producer recommendations for the experimental profile, not settings inferred for every application. They provide a starting point for the prose style; the actual result still needs to be reviewed for repetition, voice and adherence to the scene.
Weaver’s maker explicitly cautions about coherence and memory. Keep the recent scene state and essential character facts in the input when the story continues. A concise continuity note can identify what has happened, what the characters know and what must not change. If a generated passage contradicts that note, give the specific mismatch and request a revision tied to the established events.
The producer profile lists an 8,000-token context and a maximum output of 6,000 tokens. Source material, instructions, conversation and completion need to fit within the applicable budget; the output limit is not additional space beyond the context. The maker also states that this profile does not support multi-swipe generation. Treat a generated continuation as a single result to inspect, rather than assuming simultaneous alternatives from one request.
The result can be a scene continuation, descriptive passage or character exchange. Review whether it preserves viewpoint, character agency, chronology and the requested ending. For an edit, identify the exact passage to change and the meaning to retain. Keep successful story facts in the next prompt instead of expecting a long unseen history to remain available. Weaver Alpha’s practical value is its creative prose profile, with its experimental identity and documented continuity limits kept visible in how the task is prepared.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mancer/weaver | 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 $0.0008 / 1K tokens Output · Output tokens $0.0015 / 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 $0.0008 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "mancer/weaver", "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='mancer/weaver',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='mancer/weaver',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 mancer/weaver
Use the method paths shown above.
mancer/weaverWeaver Alpha is Mancer’s experimental text-generation system for roleplay and narrative prose. Its verbose creative style works best with an explicit scene, character brief and recent continuity notes; the producer profile lists an 8,000-token context and a 6,000-token output limit.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Weaver Alpha is Mancer’s experimental system for roleplay and narrative writing. The manufacturer describes a combination of smaller models intended to produce expansive prose, catalogued as a LLaMA 2-based 13B system. It takes written scene instructions and context and generates text. Its defining role is creative, verbose continuation, rather than a promise of the coherent long-term memory or broad capabilities of another model.
Give the current setting, viewpoint, character motivations and the event that should happen next. Identify which facts are established and which details the model may invent. For roleplay, state who controls each character and whether the response should contain narration, dialogue or both. A useful brief is: “Continue this scene from the narrator’s viewpoint, preserve the character’s stated goal and stop after the new complication is revealed.”
For an atmospheric passage, describe the intended mood and the details that should carry it: surroundings, pace, voice and the action at the centre of the scene. Request the length and stopping point you need. A verbose style can supply texture, but a scene still benefits from a clear dramatic purpose instead of an instruction to elaborate without direction.
The maker requires Alpaca prompt formatting for its documented completion workflow. Its published simplified format distinguishes the system instruction, user instruction and assistant response. Keep those roles separate and preserve the model’s own format when preparing a continuation. A surrounding chat workflow may handle that formatting, but the displayed model name alone does not determine an application’s prompt construction.
The manufacturer’s suggested starting settings are temperature 0.9, repetition penalty 1.1, top-p 0.58 and top-k 50. These are producer recommendations for the experimental profile, not settings inferred for every application. They provide a starting point for the prose style; the actual result still needs to be reviewed for repetition, voice and adherence to the scene.
Weaver’s maker explicitly cautions about coherence and memory. Keep the recent scene state and essential character facts in the input when the story continues. A concise continuity note can identify what has happened, what the characters know and what must not change. If a generated passage contradicts that note, give the specific mismatch and request a revision tied to the established events.
The producer profile lists an 8,000-token context and a maximum output of 6,000 tokens. Source material, instructions, conversation and completion need to fit within the applicable budget; the output limit is not additional space beyond the context. The maker also states that this profile does not support multi-swipe generation. Treat a generated continuation as a single result to inspect, rather than assuming simultaneous alternatives from one request.
The result can be a scene continuation, descriptive passage or character exchange. Review whether it preserves viewpoint, character agency, chronology and the requested ending. For an edit, identify the exact passage to change and the meaning to retain. Keep successful story facts in the next prompt instead of expecting a long unseen history to remain available. Weaver Alpha’s practical value is its creative prose profile, with its experimental identity and documented continuity limits kept visible in how the task is prepared.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mancer/weaver | 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 $0.0008 / 1K tokens Output · Output tokens $0.0015 / 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 $0.0008 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "mancer/weaver",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'mancer/weaver', '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': 'mancer/weaver', '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 mancer/weaver
Use the method paths shown above.
Technical example in English
Reveal a scene complication\n\nContinue in close third person through cartographer Nera. She has reached an abandoned observatory to recover her stolen atlas; her companion believes it burned. Write 200 words of atmospheric prose with one brief exchange. Preserve those facts, then stop when Nera finds a freshly drawn map of their route inside the supposedly empty dome.An atmospheric scene continuation around 200 words, ending on the discovery rather than resolving it.Check viewpoint, the companion's belief and the requested stopping event remain consistent.