AI Chat — Unified LLM Gateway

AceDataCloud exposes the following documented chat surfaces:

First-time setup

1. Install the official Skill with clawhub install @acedatacloud/ai-chat. Check that the ClawHub publisher is @acedatacloud.
2. Sign in at AceDataCloud → Applications, open General application, and check AI Chat access, balance, and current pricing. Copy its API key, or select Manage Keys → Create for a separate key. Copy the key value without Bearer ; a platform-... management token is not a service API key.
3. If you enable Allowed APIs restrictions, include /aichat2/conversations for the first example; other chat routes need their own access. Advanced workflows below may call additional routes.
4. On the OpenClaw Gateway host, put ACEDATACLOUD_API_TOKEN=your_api_key_here in ~/.openclaw/.env, then restart the Gateway and start a new session. For a direct terminal test, run export ACEDATACLOUD_API_TOKEN="your_api_key_here" in that shell. Never put the key in a prompt or commit an .env file.
5. Run the Quick Start below. If this workflow returns a task ID, query that same task until it finishes; do not submit the same billable request again just to check progress.

Illustrated key setup, first run, and troubleshooting · Token details

Quick Start

Recommended: stateful conversations

curl -X POST https://api.acedata.cloud/aichat2/conversations \
  -H "Authorization: Bearer $ACEDATACLOUD_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4.1","question":"Summarize the latest release notes.","stateful":true}'

OpenAI-compatible SDK

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ACEDATACLOUD_API_TOKEN"],
    base_url="https://api.acedata.cloud/openai"
)

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)
print(response.choices[0].message.content)

Dify: OpenAI-API-compatible provider

Set Base URL to https://api.acedata.cloud/v1, API Key to your AceDataCloud service token, model to gpt-5.5 (or another supported chat model), and model type to LLM. Select conversation type Chat and API type Chat Completions. Dify appends /chat/completions itself; do not use /openai/v1 or include the full endpoint in Base URL. Test the connection before selecting the model in a Workflow / Chatflow LLM node.

Currently Documented Model Families

The OpenAPI specs expose a broad, fast-moving model catalog. Representative current models include:

To select gpt-5.6-sol-fast, send that exact public model ID on Chat Completions, Responses, Messages, or either conversation endpoint above. It is listed in the public model/pricing catalog with vision capability, but has no public model introduction. Check account eligibility and current pricing before requesting it; catalog visibility does not guarantee access. Its pricing rules use a higher tier when recorded prompt_tokens exceeds 272,000 (not at exactly 272,000). Do not silently substitute gpt-5.6-sol after an access or parameter error; protocol-specific features remain model-dependent.

gpt-6.1-sol supports low, medium, high, xhigh, and max reasoning effort; do not send none or minimal. During its first week, access requires verified ACE T1+ holdings or an access grant. Check the platform access response for the opening time.

claude-opus-5-5 and claude-sonnet-5-5 are available only through the dedicated Claude Messages API; do not send it to /openai/chat/completions or /aichat2/conversations.

/aichat2/conversations also accepts model_group values chatgpt, claude, gemini, grok, kimi, glm, and deepseek.

Image-generating models are not chat models. Names ending in -image (e.g. gemini-*-image, gpt-4o-image) are image-generation models and do not work on any chat surface listed above. Generate images through the dedicated endpoints instead — Gemini via POST /v1beta/models/{model}:generateContent, or /nano-banana/images.

OpenAI-Compatible Chat

POST /openai/chat/completions
{
  "model": "gpt-4.1",
  "messages": [{"role": "user", "content": "Write a haiku about observability."}],
  "stream": true,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "parameters": {"type": "object", "properties": {"location": {"type": "string"}}}
      }
    }
  ]
}

Common parameters:

OpenAI Embeddings

Use POST /openai/embeddings (public alias /v1/embeddings) with the same AceDataCloud Bearer token for semantic search or RAG, not chat generation. Choose text-embedding-3-small or text-embedding-3-large; model and input are required.

POST /openai/embeddings
{
  "model": "text-embedding-3-small",
  "input": "Hello!"
}

text-embedding-ada-002 is retired; do not send new requests with that model. Rebuild existing indexes with the selected supported model before querying them; do not mix vectors from different models in one index or silently switch models for an existing index.

Native Claude Messages

Use POST /v1/messages (public alias /claude/messages) for native Claude calls, including Messages-only models. Authenticate with the same AceDataCloud Bearer token. Required fields are model, messages, and max_tokens; use top-level system for system instructions.

curl -X POST https://api.acedata.cloud/v1/messages \
  -H "Authorization: Bearer $ACEDATACLOUD_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model":"claude-sonnet-5-5",
    "messages":[{"role":"user","content":"Review this implementation plan."}],
    "max_tokens":8192,
    "thinking":{"type":"adaptive"},
    "output_config":{"effort":"high"}
  }'

• Send thinking, output_config, effort, sampling parameters, and extension fields unchanged. The platform does not enforce a frozen per-model enum or rewrite thinking modes; the selected model decides which values and combinations it accepts and its parameter errors are returned to the caller. Pass-through is not a promise of feature support across models or protocols.
• Common thinking.type values are adaptive, enabled, and disabled; newer modes such as between_tools, fixed budget_tokens, and whether thinking can be disabled depend on the model. Common effort values include low, medium, high, xhigh, and max, but are not a closed enum.
• thinking.display="summarized" returns a readable summary, not raw reasoning. omitted hides the thinking text while retaining its opaque signature. Beta updates requests short progress updates between tool calls with hidden reasoning; send anthropic-beta: thinking-display-updates-2026-08-18 and thinking.display="updates". The value and beta header are forwarded, not rewritten to summarized; actual support and output depend on the selected model.
• Thinking shares the max_tokens budget with final text. Display settings do not disable thinking or reduce its token billing. Increase the budget or lower supported effort if the final answer is empty or truncated.
• In multi-turn/tool-use conversations, return complete assistant thinking blocks and signatures unchanged. Never invent or edit a signature. Cross-protocol handling of redacted_thinking depends on the model service; surface errors instead of dropping blocks or silently changing the request.

To estimate input size, send the native input to POST /v1/messages/count_tokens (alias /claude/messages/count_tokens) with the same model, messages, and applicable system/tools/thinking fields. Read input_tokens; this endpoint generates no model output and consumes no quota. Its thinking configuration is extensible too; do not substitute a locally hard-coded model validator.

Stateful / Agentic Conversations

POST /aichat2/conversations generalizes the legacy conversation API with multimodal user content, CRUD-style actions, and server-side conversation state.

POST /aichat2/conversations
{
  "action": "chat",
  "model": "claude-sonnet-4-6",
  "message": [
    {"type": "text", "text": "Describe this image."},
    {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
  ],
  "stateful": true,
  "allowed_skills": ["google-search"],
  "allowed_mcp_servers": ["mcp-google-search"]
}

Useful parameters:

Set max_turns deliberately for the expected tool chain. Each iteration is a model call, not a history-retention setting; max_turns: 1 requests a single answer without tool calls. Omitting it allows up to 500 iterations, not a guarantee that all will run.

Scheduled Agent Tasks

Use POST /aichat2/scheduled-tasks on https://api.acedata.cloud with the same service Bearer token and aichat API access. All operations use POST with an action; this is not a platform-management-token endpoint.

1. Confirm the prompt, model, accounts, permitted tools, schedule, IANA timezone and spending scope before creating or enabling a task. Runs are billable and unattended: finish third-party authorization interactively first and remove ambiguities that would require a follow-up question. Use the unattended confirmation guard for supported write helpers; scheduling is not blanket permission to publish or send.
2. Choose schedule.type: cron requires a five-field cron expression and tz; interval requires interval_seconds and tz; once requires a Unix-seconds at and tz. Use an IANA zone such as UTC, not UTC+8. The saved timezone governs Cron wall-clock time even if the device timezone changes.
3. Put the conversation prompt/model in template. Optional template.max_turns is 1–500, default 500, and caps iterations per run, not schedule frequency. Each model call is billed by actual usage; a run can end earlier due to timeout, authorization or other execution conditions. A run lasts at most 15 minutes. Set an explicit Unix-seconds ends_at when needed; otherwise tasks stop three months after creation.
4. After creation, retain the returned task id. List with action="retrieve_batch"; inspect executions with action="retrieve_runs" and that id, or action="retrieve_runs_batch" for all tasks (optional status, offset, limit). Read each run's status, error_code and conversation_id; retrieve the conversation through /aichat2/conversations for the full result. Task state="enabled" (shown as “running” in the list) is not evidence of a successful run.
5. Consecutive AI execution failures do not auto-pause the task: the next scheduled run still attempts execution. Auto-pause occurs after five consecutive handoff failures to the execution service, not five failed AI runs. Insufficient balance fails a run; a request for user input/consent fails with awaiting_user_input. Diagnose the run history, fix balance/authorization/prompt issues, and explicitly pause recurring failures to prevent further attempts.
6. Pause with action="update", the task id and state="disabled"; resume with state="enabled" only after confirmation. Delete with action="delete" and id only after confirming permanent removal.

Creation payload (preview locally and obtain confirmation before sending):

POST /aichat2/scheduled-tasks
{
  "action": "create",
  "name": "Daily AI news summary",
  "schedule": {"type": "cron", "cron": "0 9 * * *", "tz": "UTC"},
  "template": {
    "model": "gpt-4o-mini",
    "question": "Today is {{date}}. Search and summarize the three most important AI news items. Do not publish or send messages.",
    "max_turns": 5
  }
}

POST /aichat2/scheduled-tasks
{"action": "retrieve_runs", "id": "TASK_ID"}

POST /aichat2/scheduled-tasks
{"action": "update", "id": "TASK_ID", "state": "disabled"}

Gotchas

• OpenAI-compatible routes under /openai/* retain public aliases /v1/chat/completions, /v1/responses, and /v1/embeddings. For clients that append /chat/completions, use Base URL https://api.acedata.cloud/v1 (not /openai/v1); the SDK example above keeps the existing /openai base.
• POST /aichat2/conversations is the recommended stateful endpoint; POST /aichat/conversations remains for legacy clients.
• message on /aichat2/conversations can be multimodal (text, image_url, file_url); plain question still works for simple text prompts.
• action on /aichat2/conversations is not chat-only — it also supports retrieve, retrieve_batch, update, and delete.
• Free-tier model variants such as gpt-5.5:free are documented on /openai/chat/completions.