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Add Portkey to LibreChat to get:
  • Unified access to 1600+ LLMs through a single API
  • Real-time observability with 40+ metrics and detailed logs
  • Enterprise governance with budget limits and RBAC
  • Security guardrails for PII detection and content filtering
For enterprise governance setup, see Enterprise Governance.

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1. Setup Portkey

1

Add Provider

Go to Model Catalog → AI Providers and add your provider (OpenAI, Anthropic, etc.) with your API credentials.
Add Provider
2

Create Config (Optional)

Go to Configs and create a config for routing, fallbacks, or other features:
{"override_params": {"model": "@openai-prod/gpt-4o"}}
3

Create Portkey API Key

Go to API Keys → Create New API Key. Optionally attach your config from Step 2.

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2. Integrate with LibreChat

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Configure Files

docker-compose.override.yml (docs)
docker-compose.override.yml
services:
  api:
    volumes:
    - type: bind
      source: ./librechat.yaml
      target: /app/librechat.yaml
.env
.env
PORTKEY_API_KEY=YOUR_PORTKEY_API_KEY
PORTKEY_GATEWAY_URL=https://api.portkey.ai/v1
librechat.yaml - Choose one option: 
version: 1.1.4
cache: true
endpoints:
  custom:
    - name: "Portkey"
      apiKey: "dummy"
      baseURL: ${PORTKEY_GATEWAY_URL}
      headers:
        x-portkey-api-key: "${PORTKEY_API_KEY}"
        x-portkey-config: "pc-libre-xxx"
      models:
        default: ["@openai-prod/gpt-4o"]
        fetch: true
      titleConvo: true
      titleModel: "current_model"
      modelDisplayLabel: "Portkey"
LibreChat requires an apiKey field—use "dummy" since auth is via Portkey headers.
For per-user cost tracking in centralized deployments, see this community guide.

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3. Enterprise Governance

Create providers per team with budget & rate limits in Model Catalog.
Use Model Catalog to provision which models are exposed to each workspace.
{"strategy": {"mode": "single"}, "targets": [{"override_params": {"model": "@openai-prod/gpt-4o"}}]}
Create configs in Configs. Update anytime without redeploying.
Create API keys with metadata for tracking and scoped permissions:
from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="engineering-team",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {"environment": "production", "department": "engineering"}
    },
    scopes=["logs.view", "configs.read"]
)
Distribute API keys and monitor in Portkey dashboard: cost tracking, model usage patterns, request volumes, error rates.

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Portkey Features

Observability

Track 40+ metrics: cost, tokens, latency. Filter by custom metadata.

1600+ LLMs

Switch providers by changing the model slug in your config.

Guardrails

PII detection, content filtering, compliance controls.

Custom Metadata

Filter logs, track usage, attribute costs by team.

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Reliability

Fallbacks

Auto-switch to backup on failure.

Load Balancing

Distribute requests by weight.

Caching

Reduce costs with response caching.

Retries

Exponential backoff on failures.

Conditional Routing

Route by metadata conditions.

Budget Limits

Control spending per team.

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Enterprise

SSO

SAML 2.0, Okta, Azure AD support.

Organization Management

Workspaces, teams, RBAC.

Audit Logs

Access control and compliance tracking.

Budget Controls

Granular spending limits.

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FAQs

Yes. Create multiple providers in Model Catalog, add them to a single config, and attach that config to your API key.
Create separate providers per team, use metadata tags in configs, or set up team-specific API keys. Monitor in the analytics dashboard.
Requests are blocked, admins notified, usage stats remain visible. Adjust limits as needed.

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Next Steps

For enterprise support, contact our enterprise team.