Fireworks AI

Fireworks AI serves open-weights models (Kimi, DeepSeek, GLM, Qwen, GPT-OSS, and more) behind an OpenAI-compatible API. Fabro ships a disabled fireworks provider entry with a curated model catalog, so you can opt in from settings.toml without changing Fabro code.

Prerequisites

A Fireworks AI account with serverless credit
An API key from app.fireworks.ai/settings/users/api-keys

Enable the provider

Fabro runs execute through a Fabro server. Add the provider override to the settings file used by that server. For a local server, this is usually ~/.fabro/settings.toml; for a remote deployment, update the server host’s Fabro settings.

settings.toml

_version = 1

[llm.providers.fireworks]
enabled = true

Configure credentials

Store the key in the target Fabro server vault:

fabro provider login --provider fireworks

# For a non-default remote server:
fabro provider login --server https://your-fabro.example --provider fireworks

# Or set the vault token directly:
fabro secret set FIREWORKS_API_KEY fw_...
fabro secret --server https://your-fabro.example set FIREWORKS_API_KEY fw_...

Direct SDK usage outside a Fabro server can use an env-backed credential source explicitly:

export FIREWORKS_API_KEY=fw_...

Included models

The built-in catalog gives Fireworks offerings the same human-facing model slugs used by other providers. Fireworks account-scoped model paths remain opaque api_id values:

Fabro model slug	Fireworks API ID / notes
`kimi-k2.7-code`	`accounts/fireworks/models/kimi-k2p7-code`; provider default
`kimi-k2.6`	`accounts/fireworks/models/kimi-k2p6`
`deepseek-v4-pro`, `deepseek-v4-flash`	`accounts/fireworks/models/deepseek-v4-...`
`glm-5.2`	`accounts/fireworks/models/glm-5p2`
`minimax-m2.7`	`accounts/fireworks/models/minimax-m2p7`
`qwen3.7-plus`	`accounts/fireworks/models/qwen3p7-plus`
`gpt-oss-120b`	`accounts/fireworks/models/gpt-oss-120b`
`gpt-oss-20b`	`accounts/fireworks/models/gpt-oss-20b`; provider small default

Any other Fireworks serverless model can be added under the provider. Choose a stable Fabro model slug as the table key and put the Fireworks account-scoped path in api_id (dots in upstream model names become p, e.g. glm-5.2 → glm-5p2):

settings.toml

[llm.providers.fireworks.models."llama-4-maverick"]
api_id = "accounts/fireworks/models/llama4-maverick-instruct-basic"
display_name = "Llama 4 Maverick"
family = "llama-4"

[llm.providers.fireworks.models."llama-4-maverick".limits]
context_window = 1000000

[llm.providers.fireworks.models."llama-4-maverick".features]
tools = true
vision = false
reasoning = false

Note that Fireworks’ GET /v1/models endpoint only returns a featured subset of serverless models; a model absent from that list may still be servable. Verify custom additions with fabro model test.

Use Fireworks models

fabro model list --provider fireworks
fabro model test --provider fireworks --model kimi-k2.7-code
fabro run workflow.fabro --provider fireworks --model deepseek-v4-flash

When targeting a non-default remote server, pass the same --server value to verification commands:

fabro model list --server https://your-fabro.example --provider fireworks
fabro model test --server https://your-fabro.example --provider fireworks --model kimi-k2.7-code

In workflow stylesheets:

workflow.fabro

digraph Example {
    graph [
        model_stylesheet="
            * { model: fireworks/kimi-k2.7-code; }
        "
    ]

    start [shape=Mdiamond, label="Start"]
    work  [label="Work", prompt="Use the configured Fireworks model."]
    exit  [shape=Msquare, label="Exit"]

    start -> work -> exit
}

Prompt caching

Fireworks caches prompt prefixes automatically — no cache breakpoints or request changes are needed. Serverless responses report cached tokens in the usage body, and cached input tokens are billed at a per-model discount (typically 50% or better). Fabro reads the cached-token counts and applies the catalog’s cache_input_cost_per_mtok rates when estimating costs.

Costs

Catalog prices mirror Fireworks serverless pricing (standard tier). Fireworks does not return in-band billing, so Fabro reports cost_source = "estimated" from catalog rates. Fireworks’ “Fast” model variants and Priority service tier are not included in the built-in catalog.

Troubleshooting

“No API key configured” — Set the key on the target server with fabro provider login --provider fireworks or fabro secret set FIREWORKS_API_KEY .... For direct SDK usage outside a Fabro server, export FIREWORKS_API_KEY in the invoking shell. “provider ‘fireworks’ is not configured in the server model catalog” — Confirm the server host’s settings.toml has [llm.providers.fireworks] with enabled = true. Fabro live-reloads settings.toml within a few seconds; after that, fabro model list --provider fireworks against the same server should show the enabled catalog. 402 / insufficient credits — Serverless inference requires prepaid credit; check your balance in the Fireworks billing dashboard. Unknown model — Confirm the model’s api_id matches a Fireworks account-scoped path exactly (accounts/fireworks/models/...), then run fabro model test --model <fabro-model-id>. Remember that GET /v1/models only lists a featured subset, so absence from that list is not conclusive.

Models

How Fabro routes model IDs, providers, and fallbacks.

Settings Configuration

Full reference for provider settings and provider-scoped model offerings.

Getting Started

Core Concepts

Defining Workflows

Executing Workflows

Human Tools

Agents

Integrations

Reference

Fireworks AI

Prerequisites

Enable the provider

Configure credentials

Included models

Use Fireworks models

Prompt caching

Costs

Troubleshooting

Further reading

Models

Settings Configuration

​Prerequisites

​Enable the provider

​Configure credentials

​Included models

​Use Fireworks models

​Prompt caching

​Costs

​Troubleshooting

​Further reading

Models

Settings Configuration

Prerequisites

Enable the provider

Configure credentials

Included models

Use Fireworks models

Prompt caching

Costs

Troubleshooting

Further reading