arize-ai-provider-integration
Skill2 starsChanged 5 months agoApache-2.0View on GitHub
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
- Reads credentials
2 other projects use a version of this, 2 of them edited.
Adopted by
Most recently taken up by
- github/awesome-copilotedited, first seen Oct 25, 2026
- Arize-ai/arize-skillsedited, first seen Oct 7, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 2
- github/awesome-copilot★ 41k99% same
- Arize-ai/arize-skills★ 7249% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository168 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .cursor/rules/CO-STAR-UI-CREATOR/AGENT.mdcCursor rule
- .cursor/rules/CO-STAR-UI-CREATOR/CO-STAR-CREATOR.mdCursor rule
- .cursor/rules/CO-STAR-UI-CREATOR/CO-STAR-PROMPT.mdCursor rule
- .cursor/rules/CO-STAR-UI-CREATOR/README.mdCursor rule
- .cursor/rules/CO-STAR-UI-CREATOR/ui.mdCursor rule
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The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 3 are distinctive, and 5 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 27
If an ax command fails, troubleshoot based on the error:
- Very specific: line 32
Security: Never read .env files or search the filesystem for credentials.
- Distinctive: line 93
Use this to inspect an integration's full configuration or to confirm its ID after creation.
- Distinctive: line 99
Before creating, always list integrations first — the user may already have a suitable one:
- Distinctive: line 105
If no suitable integration exists, create one.
- Very specific: line 244
Add --space SPACE when using a name instead of ID.
- Very specific: line 279
arize-evaluator: Create LLM-as-judge evaluators that use an AI integration → use arize-evaluator
- Very specific: line 280
arize-experiment: Run experiments that use evaluators backed by an AI integration → use arize-experiment
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Arize AI Integration Skill
SPACE— Most--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list. Note:ai-integrations createdoes not accept--space— AI integrations are account-scoped. Use--spaceonly withlist,get,update, anddelete.
Concepts
- AI Integration = stored LLM provider credentials registered in Arize; used by evaluators to call a judge model and by other Arize features that need to invoke an LLM on your behalf
- Provider = the LLM service backing the integration (e.g.,
openAI,anthropic,awsBedrock) - Integration ID = a base64-encoded global identifier for an integration (e.g.,
TGxtSW50ZWdyYXRpb246MTI6YUJjRA==); required for evaluator creation and other downstream operations - Scoping = visibility rules controlling which spaces or users can use an integration
- Auth type = how Arize authenticates with the provider:
default(provider API key),proxy_with_headers(proxy via custom headers), orbearer_token(bearer token auth)
Prerequisites
Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront.
If an ax command fails, troubleshoot based on the error:
command not foundor version error → see references/ax-setup.md401 Unauthorized/ missing API key → runax profiles showto inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys- Space unknown → run
ax spaces listto pick by name, or ask the user - LLM provider call fails (missing OPENAI_API_KEY / ANTHROPIC_API_KEY) → run
ax ai-integrations list --space SPACEto check for platform-managed credentials. If none exist, ask the user to provide the key or create an integration via the arize-ai-provider-integration skill - Security: Never read
.envfiles or search the filesystem for credentials. Useax profilesfor Arize credentials andax ai-integrationsfor LLM provider keys. If credentials are not available through these channels, ask the user.
List AI Integrations
List all integrations accessible in a space:
ax ai-integrations list --space SPACEFilter by name (case-insensitive substring match):
ax ai-integrations list --space SPACE --name "openai"Paginate large result sets:
# Get first page
ax ai-integrations list --space SPACE --limit 20 -o json
# Get next page using cursor from previous response
ax ai-integrations list --space SPACE --limit 20 --cursor CURSOR_TOKEN -o jsonKey flags:
| Flag | Description |
--space | Space name or ID to filter integrations |
--name | Case-insensitive substring filter on integration name |
--limit | Max results (1–100, default 15) |
--cursor | Pagination token from a previous response |
-o, --output | Output format: table (default) or json |
Response fields:
| Field | Description |
id | Base64 integration ID — copy this for downstream commands |
name | Human-readable name |
provider | LLM provider enum (see Supported Providers below) |
has_api_key | true if credentials are stored |
model_names | Allowed model list, or null if all models are enabled |
enable_default_models | Whether default models for this provider are allowed |
function_calling_enabled | Whether tool/function calling is enabled |
auth_type | Authentication method: default, proxy_with_headers, or bearer_token |
Get a Specific Integration
ax ai-integrations get NAME_OR_ID
ax ai-integrations get NAME_OR_ID -o json
ax ai-integrations get NAME_OR_ID --space SPACE # required when using name instead of IDUse this to inspect an integration's full configuration or to confirm its ID after creation.
Create an AI Integration
Before creating, always list integrations first — the user may already have a suitable one:
ax ai-integrations list --space SPACEIf no suitable integration exists, create one. The required flags depend on the provider.
OpenAI
ax ai-integrations create \
--name "My OpenAI Integration" \
--provider openAI \
--api-key $OPENAI_API_KEYAnthropic
ax ai-integrations create \
--name "My Anthropic Integration" \
--provider anthropic \
--api-key $ANTHROPIC_API_KEYAzure OpenAI
ax ai-integrations create \
--name "My Azure OpenAI Integration" \
--provider azureOpenAI \
--api-key $AZURE_OPENAI_API_KEY \
--base-url "https://my-resource.openai.azure.com/"AWS Bedrock
AWS Bedrock uses IAM role-based auth. Provide the ARN of the role Arize should assume via --provider-metadata:
ax ai-integrations create \
--name "My Bedrock Integration" \
--provider awsBedrock \
--provider-metadata '{"role_arn": "arn:aws:iam::123456789012:role/ArizeBedrockRole"}'Vertex AI
Vertex AI uses GCP service account credentials. Provide the GCP project and region via --provider-metadata:
ax ai-integrations create \
--name "My Vertex AI Integration" \
--provider vertexAI \
--provider-metadata '{"project_id": "my-gcp-project", "location": "us-central1"}'Gemini
ax ai-integrations create \
--name "My Gemini Integration" \
--provider gemini \
--api-key $GEMINI_API_KEYNVIDIA NIM
ax ai-integrations create \
--name "My NVIDIA NIM Integration" \
--provider nvidiaNim \
--api-key $NVIDIA_API_KEY \
--base-url "https://integrate.api.nvidia.com/v1"Custom (OpenAI-compatible endpoint)
ax ai-integrations create \
--name "My Custom Integration" \
--provider custom \
--base-url "https://my-llm-proxy.example.com/v1" \
--api-key $CUSTOM_LLM_API_KEYSupported Providers
| Provider | Required extra flags |
openAI | --api-key <key> |
anthropic | --api-key <key> |
azureOpenAI | --api-key <key>, --base-url <azure-endpoint> |
awsBedrock | --provider-metadata '{"role_arn": "<arn>"}' |
vertexAI | --provider-metadata '{"project_id": "<gcp-project>", "location": "<region>"}' |
gemini | --api-key <key> |
nvidiaNim | --api-key <key>, --base-url <nim-endpoint> |
custom | --base-url <endpoint> |
Optional flags for any provider
| Flag | Description |
--model-name | Allowed model name (repeat for multiple, e.g. --model-name gpt-4o --model-name gpt-4o-mini); omit to allow all models |
--enable-default-models | Enable the provider's default model list |
--function-calling-enabled | Enable tool/function calling support |
--auth-type | Authentication type: default, proxy_with_headers, or bearer_token |
--headers | Custom headers as JSON object or file path (for proxy auth) |
--provider-metadata | Provider-specific metadata as JSON object or file path |
After creation
Capture the returned integration ID (e.g., TGxtSW50ZWdyYXRpb246MTI6YUJjRA==) — it is needed for evaluator creation and other downstream commands. If you missed it, retrieve it:
ax ai-integrations list --space SPACE -o json
# or by name/ID directly:
ax ai-integrations get NAME_OR_IDUpdate an AI Integration
update is a partial update — only the flags you provide are changed. Omitted fields stay as-is.
# Rename
ax ai-integrations update NAME_OR_ID --name "New Name"
# Rotate the API key
ax ai-integrations update NAME_OR_ID --api-key $OPENAI_API_KEY
# Change the model list (replaces all existing model names)
ax ai-integrations update NAME_OR_ID --model-name gpt-4o --model-name gpt-4o-mini
# Update base URL (for Azure, custom, or NIM)
ax ai-integrations update NAME_OR_ID --base-url "https://new-endpoint.example.com/v1"
# Restrict visibility to specific spaces (full replace — lists all spaces that should have access)
ax ai-integrations update NAME_OR_ID \
--scopings '[{"space_id": "SPACE_GLOBAL_ID", "scoping_type": "include"}]'Add --space SPACE when using a name instead of ID. Any flag accepted by create can be passed to update.
--scopings flag: Controls which spaces can use this integration. Accepts a JSON array of scoping rules. Replaces all existing scopings on update. Use ax spaces list -o json to find space global IDs.
Delete an AI Integration
Warning: Deletion is permanent. Evaluators that reference this integration will no longer be able to run.
ax ai-integrations delete NAME_OR_ID --force
ax ai-integrations delete NAME_OR_ID --space SPACE --force # required when using name instead of IDOmit --force to get a confirmation prompt instead of deleting immediately.
Troubleshooting
| Problem | Solution |
ax: command not found | See references/ax-setup.md |
401 Unauthorized | API key may not have access to this space. Verify key and space ID at https://app.arize.com/admin > API Keys |
No profile found | Run ax profiles show --expand; set ARIZE_API_KEY env var or write ~/.arize/config.toml |
Integration not found | Verify with ax ai-integrations list --space SPACE |
has_api_key: false after create | Credentials were not saved — re-run update with the correct --api-key or --provider-metadata |
| Evaluator runs fail with LLM errors | Check integration credentials with ax ai-integrations get INT_ID; rotate the API key if needed |
provider mismatch | Cannot change provider after creation — delete and recreate with the correct provider |
Related Skills
- arize-evaluator: Create LLM-as-judge evaluators that use an AI integration → use
arize-evaluator - arize-experiment: Run experiments that use evaluators backed by an AI integration → use
arize-experiment
Save Credentials for Future Use
See references/ax-profiles.md § Save Credentials for Future Use.
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