coder/docs/ai-coder/ai-gateway/setup.md

Setup

AI Gateway runs inside the Coder control plane (coderd), requiring no separate compute to deploy or scale. Once enabled, coderd runs the aibridged in-memory and brokers traffic to your configured AI providers on behalf of authenticated users.

Required:

1. The AI Governance Add-On license.
2. Feature must be enabled using the server flag
3. One or more providers API key(s) must be configured

Note

AI Gateway environment variables and CLI flags have migrated to the new CODER_AI_GATEWAY_* and --ai-gateway-* naming scheme. The earlier CODER_AIBRIDGE_* and --aibridge-* names continue to work as aliases.

Activation

You will need to enable AI Gateway explicitly:

export CODER_AI_GATEWAY_ENABLED=true
coder server
# or
coder server --ai-gateway-enabled=true

Configure Providers

AI Gateway proxies requests to upstream LLM APIs. Configure at least one provider before exposing AI Gateway to end users.

OpenAI

Set the following when routing OpenAI-compatible traffic through AI Gateway:

• CODER_AI_GATEWAY_OPENAI_KEY or --ai-gateway-openai-key
• CODER_AI_GATEWAY_OPENAI_BASE_URL or --ai-gateway-openai-base-url

The default base URL (https://api.openai.com/v1/) works for the native OpenAI service. Point the base URL at your preferred OpenAI-compatible endpoint (for example, a hosted proxy or LiteLLM deployment) when needed.

If you'd like to create an OpenAI key with minimal privileges, this is the minimum required set:

Anthropic

Set the following when routing Anthropic-compatible traffic through AI Gateway:

• CODER_AI_GATEWAY_ANTHROPIC_KEY or --ai-gateway-anthropic-key
• CODER_AI_GATEWAY_ANTHROPIC_BASE_URL or --ai-gateway-anthropic-base-url

The default base URL (https://api.anthropic.com/) targets Anthropic's public API. Override it for Anthropic-compatible brokers.

Anthropic does not allow API keys to have restricted permissions at the time of writing (Nov 2025).

Amazon Bedrock

Set the following when routing Amazon Bedrock traffic through AI Gateway:

Required:

• CODER_AI_GATEWAY_BEDROCK_REGION or --ai-gateway-bedrock-region. Alternatively, set CODER_AI_GATEWAY_BEDROCK_BASE_URL or --ai-gateway-bedrock-base-url to a full URL (e.g., when routing through a proxy between AI Gateway and AWS Bedrock or using a non-standard endpoint that doesn't follow the https://bedrock-runtime.<region>.amazonaws.com format). If both are set, CODER_AI_GATEWAY_BEDROCK_BASE_URL takes precedence.
• CODER_AI_GATEWAY_BEDROCK_MODEL or --ai-gateway-bedrock-model
• CODER_AI_GATEWAY_BEDROCK_SMALL_FAST_MODEL or --ai-gateway-bedrock-small-fastmodel

Note

These Bedrock settings configure AI Gateway only. To configure Bedrock as an Agents provider, see Configuring AWS Bedrock.

Optional:

• CODER_AI_GATEWAY_BEDROCK_ACCESS_KEY or --ai-gateway-bedrock-access-key
• CODER_AI_GATEWAY_BEDROCK_ACCESS_KEY_SECRET or --ai-gateway-bedrock-access-key-secret

Authentication

AI Gateway supports two credential configuration paths:

AWS SDK default credential chain (recommended)

When no credentials are set in AI Gateway config, the AWS SDK resolves them automatically from the environment. This includes IAM Roles (instance profiles, IRSA, ECS task roles), shared config files, environment variables, SSO, and more.

IAM Roles are the recommended approach when AI Gateway runs on AWS infrastructure. Attach an IAM Role with Bedrock permissions to the compute running AI Gateway (EC2 instance, EKS pod via IRSA, or ECS task), no credentials need to be configured in AI Gateway itself.

The IAM Role must have permission to invoke the Bedrock models configured for AI Gateway (bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream). See Amazon Bedrock identity-based policy examples for policy examples, and AWS IAM role creation for general guidance on attaching roles to AWS services.

This aligns with AWS best practices for using temporary credentials instead of long-lived access keys.

Static credentials

For deployments when explicit credentials are preferred, provide an access key and secret for an IAM User:

1. Choose a region where you want to use Bedrock.

2. Generate API keys in the AWS Bedrock console (replace us-east-1 in the URL with your chosen region):

   • Choose an expiry period for the key.
   • Click Generate.
   • This creates an IAM user with strictly-scoped permissions for Bedrock access.

3. Create an access key for the IAM user:

   • After generating the API key, click "You can directly modify permissions for the IAM user associated".
   • In the IAM user page, navigate to the Security credentials tab.
   • Under Access keys, click Create access key.
   • Select "Application running outside AWS" as the use case.
   • Click Next.
   • Add a description like "Coder AI Gateway token".
   • Click Create access key.
   • Save both the access key ID and secret access key securely.

4. Configure your Coder deployment with the credentials:

   export CODER_AI_GATEWAY_BEDROCK_REGION=us-east-1
   export CODER_AI_GATEWAY_BEDROCK_ACCESS_KEY=<your-access-key-id>
   export CODER_AI_GATEWAY_BEDROCK_ACCESS_KEY_SECRET=<your-secret-access-key>
   coder server
   

GitHub Copilot

GitHub Copilot offers three plans: Individual, Business, and Enterprise, each with its own API endpoint. Configure one or more copilot providers using the indexed provider format depending on which plans your organization uses. Copilot providers use OAuth app installations for authentication rather than static API keys.

# GitHub Copilot (Individual)
export CODER_AI_GATEWAY_PROVIDER_0_TYPE=copilot
export CODER_AI_GATEWAY_PROVIDER_0_NAME=copilot

# GitHub Copilot Business
export CODER_AI_GATEWAY_PROVIDER_1_TYPE=copilot
export CODER_AI_GATEWAY_PROVIDER_1_NAME=copilot-business
export CODER_AI_GATEWAY_PROVIDER_1_BASE_URL=https://api.business.githubcopilot.com

# GitHub Copilot Enterprise
export CODER_AI_GATEWAY_PROVIDER_2_TYPE=copilot
export CODER_AI_GATEWAY_PROVIDER_2_NAME=copilot-enterprise
export CODER_AI_GATEWAY_PROVIDER_2_BASE_URL=https://api.enterprise.githubcopilot.com

The default base URL targets the individual Copilot API (api.individual.githubcopilot.com). Override CODER_AI_GATEWAY_PROVIDER_<N>_BASE_URL for Business or Enterprise tiers as shown above.

For client-side setup (proxy, certificates, IDE configuration), see GitHub Copilot client configuration.

ChatGPT

Configure a ChatGPT provider by creating an openai-typed instance with the ChatGPT Codex base URL:

export CODER_AI_GATEWAY_PROVIDER_0_TYPE=openai
export CODER_AI_GATEWAY_PROVIDER_0_NAME=chatgpt
export CODER_AI_GATEWAY_PROVIDER_0_BASE_URL=https://chatgpt.com/backend-api/codex

Note

See the Supported APIs section below for precise endpoint coverage and interception behavior.

Multiple instances of the same provider

You can configure multiple instances of the same provider type, for example, to route different teams to separate API keys, use different base URLs per region, or connect to both a direct API and a proxy simultaneously. Use indexed environment variables following the pattern CODER_AI_GATEWAY_PROVIDER_<N>_<KEY>:

# Anthropic routed through a corporate proxy
export CODER_AI_GATEWAY_PROVIDER_0_TYPE=anthropic
export CODER_AI_GATEWAY_PROVIDER_0_NAME=anthropic-corp
export CODER_AI_GATEWAY_PROVIDER_0_KEY=sk-ant-corp-xxx
export CODER_AI_GATEWAY_PROVIDER_0_BASE_URL=https://llm-proxy.internal.example.com/anthropic

# Anthropic direct (for teams that need direct access)
export CODER_AI_GATEWAY_PROVIDER_1_TYPE=anthropic
export CODER_AI_GATEWAY_PROVIDER_1_NAME=anthropic-direct
export CODER_AI_GATEWAY_PROVIDER_1_KEY=sk-ant-direct-yyy

# Azure-hosted OpenAI deployment
export CODER_AI_GATEWAY_PROVIDER_2_TYPE=openai
export CODER_AI_GATEWAY_PROVIDER_2_NAME=azure-openai
export CODER_AI_GATEWAY_PROVIDER_2_KEY=azure-key-zzz
export CODER_AI_GATEWAY_PROVIDER_2_BASE_URL=https://my-deployment.openai.azure.com/

# Anthropic via AWS Bedrock
export CODER_AI_GATEWAY_PROVIDER_3_TYPE=anthropic
export CODER_AI_GATEWAY_PROVIDER_3_NAME=anthropic-bedrock
export CODER_AI_GATEWAY_PROVIDER_3_BEDROCK_REGION=us-west-2
export CODER_AI_GATEWAY_PROVIDER_3_BEDROCK_ACCESS_KEY=AKIAIOSFODNN7EXAMPLE
export CODER_AI_GATEWAY_PROVIDER_3_BEDROCK_ACCESS_KEY_SECRET=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

coder server

Each provider instance gets a unique route based on its NAME. Clients send requests to /api/v2/aibridge/<NAME>/ to target a specific instance:

Instance name	Route
anthropic-corp	/api/v2/aibridge/anthropic-corp/v1/messages
anthropic-direct	/api/v2/aibridge/anthropic-direct/v1/messages
azure-openai	/api/v2/aibridge/azure-openai/v1/chat/completions
anthropic-bedrock	/api/v2/aibridge/anthropic-bedrock/v1/messages

Supported keys per provider:

Key	Required	Description
TYPE	Yes	Provider type: openai, anthropic, or copilot
NAME	No	Unique instance name for routing. Defaults to TYPE
KEY	No	API key for upstream authentication (alias: KEYS)
BASE_URL	No	Base URL of the upstream API

For anthropic providers using AWS Bedrock, the following keys are also available: BEDROCK_BASE_URL, BEDROCK_REGION, BEDROCK_ACCESS_KEY (alias: BEDROCK_ACCESS_KEYS), BEDROCK_ACCESS_KEY_SECRET (alias: BEDROCK_ACCESS_KEY_SECRETS), BEDROCK_MODEL, BEDROCK_SMALL_FAST_MODEL.

Note

Indices must be contiguous and start at 0. Each instance must have a unique NAME. If two instances of the same TYPE omit NAME, they will both default to the type name and fail with a duplicate name error.

The legacy single-provider environment variables (CODER_AI_GATEWAY_OPENAI_KEY, CODER_AI_GATEWAY_ANTHROPIC_KEY, etc.) continue to work. However, setting both a legacy variable and an indexed provider with the same default name (e.g. CODER_AI_GATEWAY_OPENAI_KEY and an indexed provider named openai) will produce a startup error. Remove one or the other to resolve the conflict.

API Dumps

AI Gateway can dump provider request and response pairs to disk for debugging. Configure the dump directory with --ai-gateway-dump-dir or CODER_AI_GATEWAY_DUMP_DIR:

coder server --ai-gateway-dump-dir=/var/lib/coder/ai-gateway-dumps

Or in YAML:

ai_gateway:
  api_dump_dir: /var/lib/coder/ai-gateway-dumps

This top-level setting replaces the previous per-provider DUMP_DIR field. For each provider, AI Gateway writes dumps under <base>/<provider_name>, where <base> is the configured dump directory and <provider_name> is the provider instance name used in the route. For example, a provider named anthropic-corp with /var/lib/coder/ai-gateway-dumps configured writes to /var/lib/coder/ai-gateway-dumps/anthropic-corp.

Sensitive headers are redacted before dumps are written. Leave the value empty to disable dumping.

Warning

API dumps are intended for short diagnostic sessions only. Dump files contain raw request and response data, which may include proprietary or sensitive information such as prompts, completions, and tool inputs. Protect the target directory and disable dumping when diagnostics are complete.

Data Retention

AI Gateway records prompts, token usage, tool invocations, and model reasoning for auditing and monitoring purposes. By default, this data is retained for 60 days.

Configure retention using --ai-gateway-retention or CODER_AI_GATEWAY_RETENTION:

coder server --ai-gateway-retention=90d

Or in YAML:

ai_gateway:
  retention: 90d

Set to 0 to retain data indefinitely.

For duration formats, how retention works, and best practices, see the Data Retention documentation.

Structured Logging

AI Gateway can emit structured logs for every interception record, making it straightforward to export data to external SIEM or observability platforms.

Enable with --ai-gateway-structured-logging or CODER_AI_GATEWAY_STRUCTURED_LOGGING:

coder server --ai-gateway-structured-logging=true

Or in YAML:

ai_gateway:
  structured_logging: true

These logs are written to the same output stream as all other coderd logs, using the format configured by --log-human (default, writes to stderr) or --log-json. For machine ingestion, set --log-json to a file path or /dev/stderr so that records are emitted as JSON.

Filter for AI Gateway records in your logging pipeline by matching on the "interception log" message. Each log line includes a record_type field that indicates the kind of event captured:

record_type	Description	Key fields
interception_start	A new intercepted request begins.	interception_id, initiator_id, provider, model, client, started_at
interception_end	An intercepted request completes.	interception_id, ended_at
token_usage	Token consumption for a response.	interception_id, input_tokens, output_tokens, created_at
prompt_usage	The last user prompt in a request.	interception_id, prompt, created_at
tool_usage	A tool/function call made by the model.	interception_id, tool, input, server_url, injected, created_at
model_thought	Model reasoning or thinking content.	interception_id, content, created_at