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Python dependency commands on this page use a PM-prepared source checkout. After a dependency change, reactivate the checkout and restart Mibyan. Mibyan supports Amazon Bedrock as a native provider. This gives you full access to the Bedrock ecosystem: IAM authentication, Guardrails, cross-region inference profiles, and all foundation models. Mibyan routes each model family through the API that serves it best: All three routes share the same AWS credential chain and region resolution — no separate configuration is needed. Requests to the Mantle endpoint are authenticated with AWS_BEARER_TOKEN_BEDROCK when set, or SigV4-signed via the standard boto3 credential chain otherwise.

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Prerequisites

  • AWS credentials — any source supported by the boto3 credential chain:
    • IAM instance role (EC2, ECS, Lambda — zero config)
    • AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY environment variables
    • AWS_PROFILE for SSO or named profiles
    • aws configure for local development
  • boto3 — install with cd ~/.mibyan/mibyan-agent && python -c "import pm; pm.sync_venv(['bedrock'], explicit=True)"
  • IAM permissions — at minimum:
    • bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream (for inference)
    • bedrock:ListFoundationModels and bedrock:ListInferenceProfiles (for model discovery)
    • bedrock:GetInferenceProfile (only if model.default is an application inference profile ARN — used to size the context window from the wrapped model)
EC2 / ECS / LambdaOn AWS compute, attach an IAM role with AmazonBedrockFullAccess and you’re done. No API keys, no .env configuration — Mibyan detects the instance role automatically.

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Quick Start

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Configuration

After running mibyan model, your ~/.mibyan/config.yaml will contain:

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Region

Set the AWS region in any of these ways (highest priority first):
  1. bedrock.region in config.yaml
  2. AWS_REGION environment variable
  3. AWS_DEFAULT_REGION environment variable
  4. Default: us-east-1

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Guardrails

To apply Amazon Bedrock Guardrails to all model invocations:
The guardrail is attached on the Converse route (guardrailConfig) and on the Claude route (InvokeModel headers via the Anthropic Bedrock SDK, so prompt caching and thinking are kept). A blocked request surfaces as a content-filter refusal rather than as model text. stream_processing_mode only applies to Converse. AWS does not apply Guardrails to the Mantle Responses endpoint used by openai.gpt-5.x models (AWS docs); use a Converse-served model when a guardrail is required.

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Model Discovery

Mibyan auto-discovers available models via the Bedrock control plane. You can customize discovery:

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Prompt caching (cachePoint)

Mibyan automatically applies prompt caching on the Bedrock Converse API path by inserting cachePoint markers after the system prompt, tool definitions, and the latest message. Because sending a cachePoint block to a model that doesn’t support it raises a ValidationException, markers are only added for models on a known-good allowlist (Anthropic Claude and Amazon Nova model IDs); unknown models default to no cache markers. Claude models normally use the AnthropicBedrock SDK path, which has its own prompt caching — the Converse cachePoint path covers Nova and the bearer-token Claude fallback. No configuration needed; cache reads/writes show up in usage accounting.

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Context-window probing

For models whose context window isn’t in Mibyan’ static table, Mibyan can probe the real limit by sending oversized requests at fixed tiers (~1.3M and ~2.2M tokens) and parsing the maximum reported in Bedrock’s length-validation error. Probed values feed the same metadata cache as the static table; stale cached entries that under-report a model’s window (e.g. entries seeded before a model’s 1M window went GA) are dropped automatically in favor of the larger known value. Application inference profiles. An ARN such as arn:aws:bedrock:us-west-2:123456789012:application-inference-profile/abcdef123456 names no model, so neither the probe nor the static table can size it. Mibyan calls bedrock:GetInferenceProfile in the ARN’s region and sizes the window from the model the profile wraps (1M for a profile wrapping Claude Sonnet 4.6). Without that permission the 128,000-token default applies and a WARNING names the profile; set model.context_length explicitly to override either way.

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Available Models

Bedrock models use inference profile IDs for on-demand invocation. The mibyan model picker shows these automatically, with recommended models at the top:
Cross-Region InferenceModels prefixed with us. use cross-region inference profiles, which provide better capacity and automatic failover across AWS regions. Models prefixed with global. route across all available regions worldwide. OpenAI openai.* model IDs are served by Bedrock Mantle in the configured region and don’t use inference-profile prefixes.

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Switching Models Mid-Session

Use the /model command during a conversation:

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Diagnostics

The doctor checks:
  • Whether AWS credentials are available (env vars, IAM role, SSO)
  • Whether boto3 is installed
  • Whether the Bedrock API is reachable (ListFoundationModels)
  • Number of available models in your region

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Gateway (Messaging Platforms)

Bedrock works with all Mibyan gateway platforms (Telegram, Discord, Slack, Feishu, etc.). Configure Bedrock as your provider, then start the gateway normally:
The gateway reads config.yaml and uses the same Bedrock provider configuration.

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Troubleshooting

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”No API key found” / “No AWS credentials”

Mibyan checks for credentials in this order:
  1. AWS_BEARER_TOKEN_BEDROCK
  2. AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY
  3. AWS_PROFILE
  4. EC2 instance metadata (IMDS)
  5. ECS container credentials
  6. Lambda execution role
If none are found, run aws configure or attach an IAM role to your compute instance.

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”Invocation of model ID … with on-demand throughput isn’t supported”

Use an inference profile ID (prefixed with us. or global.) instead of the bare foundation model ID. For example:
  • ❌ anthropic.claude-sonnet-4-6
  • ✅ us.anthropic.claude-sonnet-4-6

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”ThrottlingException”

You’ve hit the Bedrock per-model rate limit. Mibyan automatically retries with backoff. To increase limits, request a quota increase in the AWS Service Quotas console.

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One-Click AWS Deployment

For a fully automated deployment on EC2 with CloudFormation: sample-mibyan-agent-on-aws-with-bedrock — creates VPC, IAM role, EC2 instance, and configures Bedrock automatically. Deploy in any region with one click.