fabro/docs/public/integrations/bedrock.mdx
Ryan Neal d5b2220ed3
feat(llm): Amazon Bedrock provider — Converse codec, SigV4 + API-key auth (#459)
Adds **Amazon Bedrock** as an opt-in built-in provider, over Bedrock's
unified **Converse / ConverseStream** API. One codec serves every
Converse-capable family — Claude, Amazon Nova, Meta Llama, Mistral,
DeepSeek, Moonshot Kimi, Z.AI GLM, MiniMax, NVIDIA Nemotron, and OpenAI
gpt-oss — because AWS translates the envelope to each model's native
dialect server-side. Auth is either **AWS SigV4** (the default
credential chain — env / profile / IMDS / IRSA / SSO, resolved per
request so sessions refresh) or a **Bedrock API key**
(`AWS_BEARER_TOKEN_BEDROCK`, bearer). Disabled by default (the Ollama /
OpenRouter opt-in pattern).

This is the redo of #459's original Claude-only `InvokeModel` adapter,
rebuilt on the gateway-refactor seams (#481–#497). @depopry's SigV4
signer, AWS event-stream frame decoder, `BedrockAuth`, the `aws_sigv4`
credential grammar, `AdapterKind::Bedrock`, region-from-base_url, and
the lean-deps decision are preserved and authored by him on the first
two commits; the per-family `BedrockCodec` trait he wrote turned out to
be the crate-wide `Codec` seam in miniature, so the refactor promoted
exactly that shape. The original Claude-only description is preserved in
a comment below.

## What's here

- **`AdapterKind::Bedrock` × `CodecKind::BedrockConverse`** on the
route, plus the `aws_sigv4` credential source (no static secret — the
adapter signs at request time; `fabro-auth` stays AWS-free).
*(@depopry)*
- **SigV4 signer + AWS event-stream `FrameDecoder`** on the lean AWS
stack (no `aws-sdk-bedrockruntime`; transport stays on `fabro-http`).
Re-targeted at Converse's direct-JSON stream frames; the signer resolves
credentials per request. *(@depopry)*
- **`bedrock_converse` codec** — Converse envelope (`system[]`, typed
content blocks, `inferenceConfig`, `toolConfig`), prompt caching via
`cachePoint`, thinking-signature round-trip through `reasoningContent`,
usage mapped onto the disjoint `TokenCounts` buckets,
`provider_options.bedrock` passthrough. Plus the adapter shell and an
event-stream byte loop beside the transport's shared SSE loop.
- **Catalog**: `bedrock.toml` (Claude incl. Fable 5, Nova 2, Llama 4,
Mistral, DeepSeek, Kimi, GLM, MiniMax, Nemotron, gpt-oss — cross-region
inference-profile ids, per-model `billing_policy` so Claude bills
Anthropic-style) and a companion **`bedrock-openai`** provider for
GPT-5.5/5.4 over the `bedrock-mantle` Responses endpoint (pure config
over the existing `openai_responses` codec, zero new code).
- Secrets registry (`AWS_BEARER_TOKEN_BEDROCK`), gitleaks rules for both
Bedrock key formats, the `docs/integrations/bedrock` guide, and live e2e
tests.

## Live verification (confirmed end-to-end against a real AWS account)

Verified on a real Bedrock account (us-east-2, SigV4 + bearer):

- **SigV4 + Converse** — multiple families (Claude, Nova, DeepSeek, …)
via the full settings → catalog → route → adapter → codec path.
- **ConverseStream** — streaming deltas through the workflow engine.
- **Multi-turn tool use** — agent loop with tool calls round-tripping
(no-arg tools included).
- **Multi-model routing** — Claude + DeepSeek pinned in one run through
the single Converse codec.
- **mantle Responses** — `openai.gpt-5.5` answered via the
`bedrock-openai` provider (bearer auth).

The exercise caught and fixed several issues that unit tests (static
creds, mocked transports) could not — see the follow-up commits below.

## Follow-up fixes from live testing (commits on top of the foundation)

1. **Worker AWS env** — the workflow worker scrubs its env to an
allowlist, so SigV4 (which re-resolves from the ambient chain per
request) couldn't work through `fabro run`. The AWS credential-chain
inputs now cross into the worker.
2. **Vault bearer key** — Bedrock was the only key-based provider
missing a `vault:` credential ref, so `fabro secret set
AWS_BEARER_TOKEN_BEDROCK` silently didn't feed it. Now resolves env →
vault → SigV4.
3. **Converse tool-encoding hardening** — a no-arg tool call's
`toolUse.input` is now a `{}` object (Bedrock rejects null), and every
tool `inputSchema` gets a top-level `type: "object"` (strict families
like DeepSeek reject a typeless schema Claude tolerates).
4. **Nova output cap** — `amazon.nova-2-lite` max_output 65536 → 65535
(Bedrock's per-request limit).

Earlier fixes already folded into the foundation commits: the
`aws-config` sleep-impl (default chain panicked) and AWS error-body
decoding (top-level `message`/`Message`/`__type` → proper messages
instead of "Unknown error").

## Manual testing & setup

See `docs/integrations/bedrock` — now documents the non-obvious account
setup that live testing surfaced: the per-Region Anthropic use-case
approval, `aws-marketplace:Subscribe` for third-party models, the Fable
5 / Mythos-class data-sharing opt-in, and the bearer-vs-SigV4 precedence
override for running Converse + mantle side by side.

## Open decision / discussion

- **Model-id naming** — Bedrock rows use dotted ids mirroring Bedrock's
native inference-profile ids (`us.anthropic.claude-sonnet-4-6`,
`openai.gpt-5.5`), which also makes them the wire `api_id`. Third scheme
alongside bare ids and OpenRouter's `vendor/model` slashes. No collision
risk (enforced at catalog build). Open to a uniform scheme if preferred.
- **`BEDROCK_API_KEY` alias** — see the comment thread; the AWS console
hands some users `export BEDROCK_API_KEY=` while the SDK-standard var is
`AWS_BEARER_TOKEN_BEDROCK`. Question of whether to accept both.

## Deferred (named follow-ups)

- **`qwen.qwen3-coder-next`** — omitted pending a verified Bedrock
model/inference-profile id (its fabro id isn't a valid Bedrock
identifier; needs an explicit `api_id`). Re-add once confirmed via `aws
bedrock list-inference-profiles`.
- **Claude Mythos 5** — Anthropic-Messages-only on `bedrock-mantle`
(limited preview).
- **Converse structured output** (`response_format` rejected with a
clear error).
- **`reasoning_effort` on Converse rows** via
`additionalModelRequestFields` (the `bedrock-openai` GPT rows already
accept effort levels).
- **CountTokens** route (`count_input_tokens` returns `None`).

## Verification

`cargo nextest run --workspace`: green except the pre-existing
environment-dependent fabro-workflow failures (identical on main).
clippy `-D warnings` + pinned-nightly fmt clean. Codec unit tests +
adapter httpmock tests + frame-decoder/signer locks.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Scott Werner <scott@sublayer.com>
Co-authored-by: Scott Werner <stwerner@vt.edu>
2026-06-16 11:46:49 -04:00

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---
title: "Amazon Bedrock"
description: "Route Fabro models through Amazon Bedrock with SigV4 or API-key auth"
---
[Amazon Bedrock](https://aws.amazon.com/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.
## Prerequisites
- An AWS account with [Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) granted for the models you want (see [Model access and approvals](#model-access-and-approvals))
- Either a [Bedrock API key](https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys.html) or working AWS credentials (environment keys, profile, IMDS, IRSA, SSO)
## 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](https://docs.aws.amazon.com/bedrock/latest/userguide/data-retention.html) (`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."*
## Enable the provider
Add the provider override to `~/.fabro/settings.toml`:
```toml title="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).
## 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.
```bash
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:
```toml
[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).
<Warning>
**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:
```toml
[llm.providers.bedrock.auth]
credentials = ["aws_sigv4"]
```
</Warning>
## 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 ID | Notes |
| --- | --- |
| `us.anthropic.claude-sonnet-4-6` | Provider default; Anthropic cache billing |
| `us.anthropic.claude-opus-4-8` | Anthropic cache billing |
| `us.anthropic.claude-haiku-4-5` | Provider small default |
| `us.anthropic.claude-fable-5` | Frontier; 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](#model-access-and-approvals)) |
| `openai.gpt-oss-120b`, `openai.gpt-oss-20b` | OpenAI open-weights |
| `amazon.nova-2-lite` | Vision |
| `meta.llama4-maverick` | Vision |
| `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.
## 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.
```toml title="settings.toml"
[llm.providers.bedrock-openai]
enabled = true
# regional: change to https://bedrock-mantle.<region>.api.aws/openai/v1
```
```bash
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.
## Use Bedrock models
```bash
fabro model list --provider bedrock
fabro model test --model us.anthropic.claude-sonnet-4-6
fabro run workflow.fabro --model deepseek.v3-2
```
## 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`.
## 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:
```json
{
"model": "us.anthropic.claude-sonnet-4-6",
"provider_options": {
"bedrock": {
"additionalModelRequestFields": { "top_k": 200 },
"guardrailConfig": { "guardrailIdentifier": "gr-abc", "guardrailVersion": "1" },
"serviceTier": { "type": "flex" }
}
}
}
```
## 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](#configure-credentials). 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](#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.
## Further reading
<Columns cols={2}>
<Card title="Models" icon="microchip" href="/core-concepts/models">
How Fabro routes model IDs, providers, and fallbacks.
</Card>
<Card title="Settings Configuration" icon="gear" href="/reference/user-configuration">
Full reference for `[llm.providers.<id>]` and `[llm.models.<id>]`.
</Card>
</Columns>