leanmcp/superview.sh/fireworks-inference

Fireworks inference

Inference on Fireworks is three independent choices, and nearly every confusing failure is one of them being wrong while you debug another:

   TRANSPORT                ROUTE                        MODEL
   ─────────                ─────                        ─────
   raw HTTP                 direct Fireworks             serverless
   openai SDK        ×      api.fireworks.ai        ×    accounts/fireworks/models/<slug>
   litellm                  Leanmcp gateway              dedicated deployment
                            aigateway.leanmcp.com        accounts/<acct>/deployments/<id>

This skill's job is to let you isolate them one at a time instead of guessing.

The route matrix — memorize this

Same models, same request and response shape. Only the endpoint and the key change — which is why routing should be a flag, never a code path. Note the key swap: through the gateway you send your Leanmcp key; the gateway holds the Fireworks credential.

Gateway keys at <https://app.leanmcp.com/api-keys> (top up credits first at <https://app.leanmcp.com/billing>); every request then shows up at <https://app.leanmcp.com/observability>.

Start here

Something is broken. Climb the ladder in references/verification.md — each rung isolates one layer, so the first failure names the culprit.

python scripts/fw_smoke.py --model gpt-oss-20b            # raw + sdk + litellm, gateway
python scripts/fw_smoke.py --model gpt-oss-20b --direct   # same, direct

The model returns nothing. Almost always references/reasoning-models.md (the answer is in reasoning_content, or the budget went to reasoning) or a cold deployment (references/models.md).

Picking a model. references/models.md, then python scripts/fw_models.py --serverless --tools --sort context.

Setting up a route or a framework. references/routes.md for the transport quirks, references/gateway.md for the proxy specifics.

Building a sweep. references/runner-scripts.md

• assets/run_fireworks.sh, and hammer it first

(references/load-and-concurrency.md).

Working code at full scale. references/reference-implementation.md.

Scripts

Model ids — three shapes, not interchangeable

accounts/fireworks/models/gpt-oss-20b            serverless, shared, always warm
accounts/<your-account>/deployments/<id>         dedicated, yours, scales to zero
fireworks_ai/<either of the above>               the litellm form

A bare slug is shorthand you expand yourself; nothing accepts it directly.

litellm: two things that look like the service is down

1. The model id needs the `fireworks_ai/` prefix so litellm picks the

provider. Without it litellm guesses from the string, usually wrongly.

1. litellm appends `/chat/completions` to `api_base`. Pass the base without

that suffix.

litellm.completion(
    model="fireworks_ai/accounts/fireworks/models/gpt-oss-20b",
    messages=[...],
    api_base="https://aigateway.leanmcp.com/v1/fireworks",   # no /chat/completions
    api_key=os.environ["LEANMCP_API_KEY"],                    # Leanmcp, not Fireworks
)

litellm._turn_on_debug() prints the URL it actually hits — that one line settles most "the gateway is broken" arguments. (It also prints the API key, so treat such a log as a secret.)

Things that are true and easy to miss

`content: null` is not failure. Reasoning models put chain-of-thought in reasoning_content and may return no content at all — especially when the budget ran out mid-thought (finish_reason: "length"). Always read msg.content or msg.reasoning_content, and budget 4096, not 1024. A tool call with null content is also correct: the model acted instead of speaking.

Cold deployments return 200 with an empty body. A scale-to-zero deployment does not error while spinning up — downstream code dies on the None with TypeError: expected string or bytes-like object, got 'NoneType', which reads as a code bug. Warm it first with fw_ready.py --wait.

The gateway forwards with *its* credentials. Your private deployment is invisible to it unless the gateway is wired to your Fireworks account — expect a 404, which is configuration, not a bug. Confirm the deployment is alive with a direct call before blaming the gateway.

Tool support is per model, and the flag is a hint. --serverless --tools filters the catalogue, but some models report supportsTools: false and call tools fine. Probe with fw_smoke.py --tools.

Load reveals what a single call cannot. Missing scopes: model.request, rate limits, and under-replicated deployments all pass a smoke test and fail a sweep — unevenly across cells, which silently unbalances the comparison. Hammer at your intended concurrency before launching.

Turn client-side caching off for anything you measure. A cached response is not observed, not billed, and not a measurement.

Route every speaker the same way. Agent through the gateway and the simulated user direct gives you a dashboard showing half the traffic and a cost total that understates the run.

Related

• research-observability — the gateway tells you what a run cost; a local

per-turn trace tells you what the model said and why the episode failed. Use both; the adapters there take LiteLLM/OpenAI responses directly.

• tinker-training-inference — for the Tinker platform (training and its own

inference endpoints).