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Amazon Bedrock hosts Anthropic, Amazon, Meta, Mistral, DeepSeek, Qwen, Moonshot, Z.AI, MiniMax, NVIDIA, and OpenAI open-weight models behind one AWS endpoint. Fabro ships a disabled bedrock provider entry with a curated model catalog over Bedrock’s unified Converse API, so you can opt in from settings.toml without changing Fabro code.

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Prerequisites

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Model access and approvals

Access is granted per Region and varies by model family — enabling the provider in Fabro is necessary but not sufficient.
  • IAM. Converse and ConverseStream have no dedicated IAM actions; they’re authorized by bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream. A Bedrock API key additionally needs bedrock:CallWithBearerToken.
  • Anthropic (Claude) models require a one-time use-case submission in the Bedrock console (Model access) before first use, and the grant is per Region — approval in us-east-1 does not cover us-east-2. An un-approved Region returns AccessDeniedException.
  • Third-party models (OpenAI gpt-oss, DeepSeek, Qwen, Moonshot, Z.AI, MiniMax, NVIDIA) auto-enable on first call, which needs aws-marketplace:Subscribe and aws-marketplace:ViewSubscriptions on the calling principal. The first call may take a moment while the subscription activates.
  • Claude Fable 5 / Mythos-class models additionally require opting into data sharing via the Data Retention API (provider_data_share, 30-day retention) before they can be invoked. With the account/project on the default retention mode, Converse rejects the request with “data retention mode ‘default’ is not available for this model.”

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Enable the provider

Add the provider override to ~/.fabro/settings.toml:
settings.toml
_version = 1

[llm.providers.bedrock]
enabled = true
base_url = "https://bedrock-runtime.us-east-1.amazonaws.com"
The SigV4 signing region is derived from base_url — change it to your Region’s endpoint (https://bedrock-runtime.<region>.amazonaws.com, FIPS and China endpoints included).

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Configure credentials

Two auth modes, tried in order: Bedrock API key (simplest): store the key and Fabro sends it as a bearer token. The key is read from either AWS_BEARER_TOKEN_BEDROCK (AWS’s canonical name, also honored by the AWS SDKs and CLI) or BEDROCK_API_KEY (Fabro’s <PROVIDER>_API_KEY convention) — use whichever you prefer. 
fabro secret set AWS_BEARER_TOKEN_BEDROCK bedrock-api-key-...
# or, equivalently
fabro secret set BEDROCK_API_KEY bedrock-api-key-...
# or for standalone local runs
export AWS_BEARER_TOKEN_BEDROCK=bedrock-api-key-...
AWS SigV4 (IAM-scoped): with no API key configured, Fabro signs each request using the AWS default credential chain — environment keys, shared profile, EC2/ECS instance roles, IRSA/web identity, SSO. Expiring session credentials refresh automatically. The catalog declares this as the aws_sigv4 credential source: 
[llm.providers.bedrock.auth]
credentials = ["env:AWS_BEARER_TOKEN_BEDROCK", "env:BEDROCK_API_KEY", "vault:AWS_BEARER_TOKEN_BEDROCK", "vault:BEDROCK_API_KEY", "aws_sigv4"]
The key resolves from the process environment first (either name), then the server vault (fabro secret set), then falls back to SigV4 — so on a server, prefer secret set. To select a non-default AWS profile for SigV4, set AWS_PROFILE (it, and the rest of the AWS credential-chain variables, are passed through to workflow workers).
Bearer-vs-SigV4 precedence. Because the bearer key is tried before SigV4, setting AWS_BEARER_TOKEN_BEDROCK makes the bedrock (Converse) provider authenticate with that key too — not just the bedrock-openai mantle provider below. If your key is valid only for mantle (it lacks bedrock:InvokeModel* on the runtime), every Converse model then fails with “Authentication failed.” To run Converse models on SigV4 while using a mantle-only bearer key for GPT-5.x, pin the Converse provider to SigV4 explicitly:
[llm.providers.bedrock.auth]
credentials = ["aws_sigv4"]

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Included models

The built-in catalog curates Converse-capable models, using cross-region inference profile ids (us./global. prefixes) where on-demand access requires them:
Fabro model IDNotes
us.anthropic.claude-sonnet-4-6Provider default; Anthropic cache billing
us.anthropic.claude-opus-4-8Anthropic cache billing
us.anthropic.claude-haiku-4-5Provider small default
us.anthropic.claude-fable-5Frontier; sampling params pinned by Bedrock (Fabro drops temperature/top_p automatically); requires the account-level provider_data_share data-sharing opt-in (see Model access)
openai.gpt-oss-120b, openai.gpt-oss-20bOpenAI open-weights
amazon.nova-2-liteVision
meta.llama4-maverickVision
mistral.mistral-large-3, mistral.devstral-2
deepseek.v3-2
moonshotai.kimi-k2.5, zai.glm-5
minimax.minimax-m2.5, nvidia.nemotron-3-super
Any other Converse-capable Bedrock model can be added as a settings model entry with provider = "bedrock" and the Bedrock model or inference-profile id as api_id. Not included on this provider: Claude Mythos 5 (Anthropic-Messages-only on bedrock-mantle, limited preview). OpenAI’s frontier models live on the companion bedrock-openai provider below.

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OpenAI frontier models (GPT-5.5 / GPT-5.4)

GPT-5.5 and GPT-5.4 on Bedrock are served only by the bedrock-mantle endpoint’s OpenAI Responses API — a different surface than Converse. Fabro ships a companion bedrock-openai provider for them: the same AWS account and AWS_BEARER_TOKEN_BEDROCK key, pointed at the mantle endpoint over the OpenAI dialect.
settings.toml
[llm.providers.bedrock-openai]
enabled = true
# regional: change to https://bedrock-mantle.<region>.api.aws/openai/v1

fabro model test --model openai.gpt-5.5
Auth on this provider is Bedrock-API-key only (mantle SigV4 uses a different signing name than the runtime endpoint). Fabro always sends store: false, so nothing is retained under mantle’s default 30-day response storage.

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Use Bedrock models

fabro model list --provider bedrock
fabro model test --model us.anthropic.claude-sonnet-4-6
fabro run workflow.fabro --model deepseek.v3-2

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Prompt caching

Claude models cache automatically when the catalog row declares prompt_cache: Fabro places Converse cachePoint blocks after the system prompt, the tool definitions, and the conversation prefix — the same placement as the direct Anthropic provider. Cache reads and writes price Anthropic-style via the per-model billing_policy.

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Converse extensions

Bedrock-specific request fields pass through verbatim via provider_options.bedrock on API/SDK requests — the keys merge into the top level of the Converse envelope: 
{
  "model": "us.anthropic.claude-sonnet-4-6",
  "provider_options": {
    "bedrock": {
      "additionalModelRequestFields": { "top_k": 200 },
      "guardrailConfig": { "guardrailIdentifier": "gr-abc", "guardrailVersion": "1" },
      "serviceTier": { "type": "flex" }
    }
  }
}

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Troubleshooting

“no AWS credentials provider found” — Neither an API key nor any AWS chain source resolved. Set AWS_BEARER_TOKEN_BEDROCK (or BEDROCK_API_KEY), or configure standard AWS credentials. AccessDeniedException / 403 — The IAM principal lacks bedrock:InvokeModel* for the model, or model access has not been granted in the Bedrock console for your Region (Claude needs the per-Region use-case approval; third-party models need aws-marketplace:Subscribe). “Authentication failed: Please make sure your API Key is valid.” — A Bedrock API key was sent but rejected by the runtime. Common cause: a mantle-scoped key used against Converse — see the bearer-vs-SigV4 warning above. Verify the key is valid for bedrock-runtime in this Region, or pin Converse to aws_sigv4. “data retention mode ‘default’ is not available for this model” — Fable 5 / Mythos-class models require opting into data sharing first; see Model access and approvals. “The provided model identifier is invalid” — The wire id sent to Bedrock isn’t a recognized model or inference-profile id. Set an explicit api_id (from aws bedrock list-inference-profiles) on the model entry. ValidationException mentioning on-demand throughput — The model requires an inference-profile id; use the us./global.-prefixed id from the catalog rather than the bare model id. ValidationException mentioning maximum tokens — The requested max_tokens exceeds the model’s per-request output cap; lower the model’s max_output to the documented limit. ThrottlingException — Account-level Bedrock quota; consider cross-region inference profiles or a quota increase.

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Further reading

Models

How Fabro routes model IDs, providers, and fallbacks.

Settings Configuration

Full reference for [llm.providers.<id>] and [llm.models.<id>].