Agentbase LLM

GreenNode AI Platform — API Keys & LLM Models

Manage API keys and browse LLM models on the GreenNode AI Platform (MAAS). API keys created here are OpenAI-compatible and can be used with the LLM endpoint to power AI agents.

LLM Endpoint (OpenAI-compatible): https://maas-llm-aiplatform-hcm.api.vngcloud.vn/v1

Use bash .claude/skills/agentbase/scripts/aip.sh help for full command reference.

Authentication

Read the shared auth setup reference at /agentbase skill's references/auth-setup.md for full IAM credential configuration. In brief: run bash .claude/skills/agentbase/scripts/check_credentials.sh iam to verify credentials are configured, then use TOKEN=$(bash .claude/skills/agentbase/scripts/get_token.sh) to obtain a token. NEVER read .greennode.json or .env directly — always use the helper scripts. On 401: re-run with --force. If check_credentials.sh iam returns MISSING, STOP — you MUST read the "If Credentials Are Not Found" section in /agentbase skill's references/auth-setup.md and follow it exactly. Do NOT skip this or provide your own credential setup instructions.

Interaction Guidelines

• Never assume API response structure — always inspect the actual response first before extracting or filtering data. Do not guess field names.
• Guide first, act only when asked — if the user asks "how to" manage API keys or browse models, respond with instructions and guidance only. Do NOT execute API calls or create resources unless they explicitly ask you to do it for them.
• Confirm before executing (HARD GATE) — before performing any mutating action (create, delete, enable/disable model), present a clear summary of what will be done (including all parameters and values) and ask the user to confirm. Read-only operations (list, get, metadata, rate-limit) proceed directly without confirmation. Do NOT auto-execute. Only proceed when the user responds with an explicit confirmation keyword: yes, confirm, ok, approve, proceed, go ahead, do it, ship it, lgtm, or equivalent affirmative. If the user responds with ANYTHING ELSE (parameter changes, questions, corrections, additional info, or ambiguous text), treat it as adjustment input — update the plan and re-present the full summary for confirmation again. NEVER interpret a non-confirmation response as approval. For destructive operations (delete API key), additionally warn that the action is irreversible.
• Never auto-decide parameters — when an action requires parameters (e.g., API key name, model UUID), always ask the user for each required value. You may recommend sensible defaults or examples, but never auto-select or impose values without the user's explicit agreement.
• Present options, let user choose — when there are multiple choices (e.g., existing API keys, available models), list the available options and let the user pick. Do not make the choice for them.
• Dry-run support: When user requests --dry-run or preview, show the exact command and API request that would be sent (method, URL, payload) and explain the expected outcome WITHOUT executing. The scripts do not support a --dry-run flag — construct the preview manually. Let user review before proceeding.
• Always read full API response body — when calling platform APIs, capture and read the full JSON response (not just status codes). This avoids misidentifying field names or data structures, ensures correct field extraction, and enables better error handling and debugging.

API Key Management

API keys grant access to LLM models through the OpenAI-compatible endpoint. Once you have a key, you can use it like an OpenAI API key.

api-keys list

List all API keys for the current account.

bash .claude/skills/agentbase/scripts/aip.sh api-keys list [--name NAME] [--page N] [--size N]

Note: AI Platform uses 1-indexed pagination (page=1 is first page).

Response contains a paginated list with fields: listData (array of keys), page, pageSize, totalPage, totalItem. Each key has: id, name, key, status, isDefault, models, createdAt.

api-keys create [name]

Create a new API key. The script sends the request, saves the key to .env, and returns immediately. You must poll api-keys get <name> until status is ACTIVE.

• Name constraints: 5-50 chars, pattern ^[a-z0-9\-]{5,50}$ (lowercase letters, digits, hyphens only)

bash .claude/skills/agentbase/scripts/aip.sh api-keys create --name my-agent-key [--default]

If creation fails with a quota error (e.g. 400/409 indicating quota exhausted), do NOT auto-retry. Instead:

1. List all existing API keys using aip.sh api-keys list.
2. Present the list to the user and explain that their API key quota is full.
3. Suggest the user delete an unused key to free up quota. Ask which key they want to delete.
4. After the user confirms deletion, delete the key. Note: deletion is async — you must poll aip.sh api-keys list --name <name> every 5-10 seconds until no key with that exact name remains in listData (the --name filter matches substrings, so verify the exact name is gone, not just that the list is non-empty). Wait for deletion to complete before proceeding. If it doesn't complete within 2-3 minutes, inform the user that the operation is taking longer than expected.
5. Retry creating the new key once. If creation fails again, inform the user that the issue is not quota-related and ask them to check their account status or billing. Do NOT loop — max 1 retry after a deletion.

After creating a key, remind the user:

Your API key can be used as an OpenAI-compatible key:

• Base URL: https://maas-llm-aiplatform-hcm.api.vngcloud.vn/v1
• API Key: saved to .env as LLM_API_KEY. Verify with bash .claude/skills/agentbase/scripts/check_credentials.sh llm

api-keys get [name]

Get details of a specific API key. The key field is redacted in stdout by design — the plaintext key is never printed.

bash .claude/skills/agentbase/scripts/aip.sh api-keys get my-agent-key

To load an existing key into .env (e.g. when reusing a key instead of creating one), add --save-env. The key is written to .env as LLM_API_KEY silently — it never appears in output:

bash .claude/skills/agentbase/scripts/aip.sh api-keys get my-agent-key --save-env
# then verify: bash .claude/skills/agentbase/scripts/check_credentials.sh llm

api-keys update [name]

Update an API key (currently supports setting default status).

bash .claude/skills/agentbase/scripts/aip.sh api-keys update my-agent-key --default true

api-keys delete [name]

Delete an API key. Confirm with the user before proceeding. The script sends the DELETE request and returns immediately. You must confirm deletion by polling api-keys list --name <name> until no entry with that exact name remains in listData.

bash .claude/skills/agentbase/scripts/aip.sh api-keys delete my-agent-key

Note: a key can only be deleted once it is ACTIVE — deleting a CREATING key fails, and a per-account lock rejects concurrent deletes (400 "User is already deleting API keys.").

Confirm deletion to the user only after the key no longer appears in api-keys list. Do NOT confirm immediately after the script returns — deletion is async.

LLM Model Management

Browse, inspect, and enable/disable available LLM models. These models are accessible via the OpenAI-compatible endpoint using an API key from above.

models list

List available models with optional filters.

# List all models
bash .claude/skills/agentbase/scripts/aip.sh models list

# Filter by provider and type
bash .claude/skills/agentbase/scripts/aip.sh models list --providers openai --types chat --status ENABLED

Tip: Use bash .claude/skills/agentbase/scripts/aip.sh models metadata to discover valid filter values for --providers, --types, and --status.

Response contains: listData (array of models), page, pageSize, totalPage, totalItem. Key fields in each model: uuid, name, code, path, description, modelStatus, isFree, provider, types.

models get [modelUuid]

Get detailed information about a specific model.

bash .claude/skills/agentbase/scripts/aip.sh models get MODEL_UUID

models metadata

Get available filter options (providers, types, use cases) for model listing.

bash .claude/skills/agentbase/scripts/aip.sh models metadata

This is useful to discover what providers, model types, and use cases are available before filtering the model list.

models enable [modelUuid]

Enable an LLM model for your account.

bash .claude/skills/agentbase/scripts/aip.sh models enable MODEL_UUID

After enabling, verify by fetching the model detail and checking isEnabled == true. The enable operation is synchronous — verify immediately after the call returns.

Billing errors: If the enable request fails with a billing-related error (e.g. 400/402/403 indicating unpaid balance, insufficient credits, or billing not activated), do NOT retry. Instead:

1. Inform the user that the operation failed due to a billing issue.
2. Explain that they need to check and resolve their billing status before enabling the model.
3. Direct them to the GreenNode AI Platform console at https://aiplatform.console.vngcloud.vn/models to review their account and billing status.
4. Once billing is resolved, they can retry enabling the model.

models disable [modelUuid]

Disable an LLM model for your account.

bash .claude/skills/agentbase/scripts/aip.sh models disable MODEL_UUID

models rate-limit [modelUuid]

Check rate limit configuration for a specific model.

bash .claude/skills/agentbase/scripts/aip.sh models rate-limit MODEL_UUID

Using with OpenAI SDK

Once you have an API key, you can use GreenNode LLM models with any OpenAI-compatible client:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_GREENNODE_API_KEY",
    base_url="https://maas-llm-aiplatform-hcm.api.vngcloud.vn/v1",
)

response = client.chat.completions.create(
    model="MODEL_PATH",  # use the `path` field from model detail (not `code`). If `path` is missing, use `code`.
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_GREENNODE_API_KEY",
  baseURL: "https://maas-llm-aiplatform-hcm.api.vngcloud.vn/v1",
});

const response = await client.chat.completions.create({
  model: "MODEL_PATH",  // use the `path` field from model detail (not `code`). If `path` is missing, use `code`.
  messages: [{ role: "user", content: "Hello!" }],
});

Troubleshooting

Instructions

1. Parse the user's request to determine the resource type (api-keys or models) and operation.
2. If unclear, ask the user what they need:
   • api-keys: "I want to create/manage API keys for accessing LLM models"
   • models: "I want to see what LLM models are available and their details"
3. When the user needs an API key, always let the user decide:
   • First, list existing API keys using aip.sh api-keys list.
   • Present the existing keys to the user (if any) and explicitly ask them to choose one of the following options:
     • Use an existing key — let the user pick which one from the list
     • Create a new key — proceed to create a new API key
   • Do NOT auto-select or auto-use any existing key. The user must explicitly choose.
   • If the user is unsure which key to use, list all keys with their names and status, and recommend the one marked isDefault: true. If the user says "use any" or "just pick one", use the default key.
   • Once the user picks an existing key, load it into .env by running aip.sh api-keys get <name> --save-env. Reusing a key without this step leaves LLM_API_KEY unset — the key is only auto-saved on create, so an existing key must be captured explicitly. Confirm with check_credentials.sh llm.
4. When creating a new API key fails with a quota error:
   • Do NOT retry the creation. Explain that the API key quota is full.
   • List all existing API keys and present them to the user.
   • Guide the user to delete an unused key to free up quota. Ask which key they want to delete.
   • After deletion completes, retry creating the new key.
5. For api-keys create, ask for the key name if not provided. Validate it matches ^[a-z0-9\-]{5,50}$.
6. api-keys create is async: the script POSTs to the v2 endpoint, which returns 200 with the plaintext key in the response (initial status: CREATING). The script saves the key to .env immediately and returns. You must then poll the status by calling aip.sh api-keys get <name> every 5-10 seconds until the status field is ACTIVE. The key is already in .env from the create step — do NOT re-run --save-env here, it is redundant. The plaintext key is redacted from all output by design — never expect to see it; confirm it landed with check_credentials.sh llm.
7. api-keys delete is async: the script sends the DELETE request and returns immediately. You must then poll by calling aip.sh api-keys list --name <name> every 5-10 seconds until no key with that exact name remains in listData (the --name filter is a substring match — confirm the exact name is absent). Only confirm deletion to the user after the key is gone. Note: a key must be ACTIVE to be deleted, and concurrent deletes are rejected with 400 "User is already deleting API keys.".
8. After creating an API key, show the OpenAI-compatible usage info: the base URL and that the key is stored in .env as LLM_API_KEY (verify with check_credentials.sh llm). Never print the plaintext key — it is redacted from all command output by design.
9. When listing or showing model details, highlight the model path field — this is what must be passed as the model parameter when calling the LLM API (not code; if path is missing, fall back to code). Do NOT show pricing/billing info (inputPrice, outputPrice) to the user — pricing may be subject to negotiated contracts; refer users to the billing dashboard instead.
10. When the user wants to set up LLM access for an agent, guide them through: (1) browse models to pick one, (2) list existing API keys and reuse one, or create a new key if needed, (3) use the key with the OpenAI SDK pointing to the GreenNode endpoint.
11. When the user needs to pick a model, list available models filtered by status=ENABLED and sorted by most recent first, then let the user choose. Do not auto-select or recommend a specific model unless the user explicitly asks for a recommendation.
12. When enabling a model via aip.sh models enable, if the request fails with a billing-related error (400/402/403 with messages about billing, credits, or payment), do NOT retry. Inform the user that the operation failed due to a billing issue and that they need to check and resolve their billing status at https://aiplatform.console.vngcloud.vn/models before retrying. Do not attempt workarounds — billing issues must be resolved by the user.