Quickstart

Flow AI is an OpenAI-compatible API. Point any OpenAI SDK at our base URL and use your Flow AI key — every request is routed to the cheapest model that can do the job.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.flowaiapi.com/v1",
    api_key="fa-...",          # from your dashboard
)

r = client.chat.completions.create(
    model="flow-1",            # Flow's model — routes to the cheapest capable supply for you
    messages=[{"role": "user", "content": "Summarize this ticket: ..."}],
)
print(r.choices[0].message.content)
print(r.model_dump()["_flowaiapi"])   # which model served it, price, your cost

curl https://api.flowaiapi.com/v1/chat/completions \
  -H "Authorization: Bearer fa-..." \
  -H "Content-Type: application/json" \
  -d '{"model":"auto","messages":[{"role":"user","content":"hello"}]}'

Authentication

Pass your API key as a bearer token: Authorization: Bearer fa-.... Create and manage keys (and per-key budgets) in the dashboard. A key is shown in full only once at creation — store it securely. New accounts get a 7-day free trial; after that an active membership ($4.99/mo or $45/yr) is required to make requests.

Chat completions

POST /v1/chat/completions — accepts the standard OpenAI request body (messages, max_tokens, temperature, tools, response_format, stream, …) and returns the standard OpenAI response shape plus a _flowaiapi block. Both streaming (SSE) and non-streaming are supported.

Flow AI adds two optional fields:

Routing

On each request Flow AI classifies the task with cheap, non-LLM heuristics (length, code, tool/JSON schema, multimodal parts), then picks the cheapest model whose capabilities cover the task — looked up against the live per-minute price. So a vision prompt goes to a multimodal model, a long-context prompt to a 1M-context model, and plain text to the cheapest capable one. The decision is cached by system prompt, so agent traffic pays near-zero routing overhead.

Cascade (default). Rather than guess upfront, auto serves the cheapest capable model, runs a fast judge over the output (empty / refusal / malformed-JSON checks), and escalates to a stronger model only if it fails — you pay cheap by default and only pay up when the task actually needs it. Rejected cheap attempts aren't charged. The _flowaiapi.cascade array shows each tier tried. If a provider errors, it also fails over to the next capable one (failover: true).

Response metadata

Every non-streamed response includes a _flowaiapi object so the route is fully transparent:

"_flowaiapi": {
  "seller_id": "api:deepseek-v4-flash",
  "model_used": "deepseek-v4-flash",
  "input_tokens": 412, "output_tokens": 188,
  "clearing_price_per_1k_in": 0.00014,
  "clearing_price_per_1k_out": 0.00028,
  "cost_usd": 0.0000934,
  "buyer_charge_usd": 0.0000962,
  "interval_id": 29695141,
  "routing_reason": "auto__cheapest_capable",
  "latency_ms": 885,
  "failover": false,
  "budget_alerts": []
}

You pay cost_usd × 1.025 (buyer_charge_usd) — the model's pass-through cost plus a flat 2.5% spread. The commercial price is always the ceiling.

Models

GET /v1/models lists the currently available models. You can pin one by passing its id as model or route_strategy, but auto is recommended — it tracks price changes for you. For strict, guaranteed pinning (experiments, model panels) see Panel Mode.

Panel Mode — pinned models

Routing by default, any model on demand. Flow AI's default product is smart routing — flow-1 picks the cheapest capable model for every request. But some workloads need a specific model, or a panel of genuinely different ones: judge ensembles, consensus scoring, cross-model evals, A/B model tests, reproducible research. Panel Mode is strict, opt-in pinning for exactly those cases.

Enable it per key in the dashboard (Keys → "Panel Mode"). It's off by default — leave agent keys off, since routing is what saves you money.

The contract

Pin a single model

Prefix the model id with pin: — or send the header X-FlowAI-Route: pinned with a plain model id. Accepts a registry id (gpt-4o-mini) or a provider-prefixed name (openai/gpt-4o-mini).

curl https://api.flowaiapi.com/v1/chat/completions \
  -H "Authorization: Bearer fa-..." -H "Content-Type: application/json" \
  -d '{"model":"pin:gpt-4o-mini","messages":[{"role":"user","content":"..."}]}'
# -> _flowaiapi: { "served_model": "gpt-4o-mini", "pinned": true, ... }

The pinnable catalog: GET /v1/models?pinnable=true — id, provider, family, context window, per-token price, capabilities.

Fan out a panel — POST /v1/panel

One prompt across up to 10 pinned models in a single call. Legs run concurrently (a 5-model panel takes about as long as its slowest model), each leg bills at its own model's rate, and a model that can't serve returns an error entry while the rest still answer.

curl https://api.flowaiapi.com/v1/panel \
  -H "Authorization: Bearer fa-..." -H "Content-Type: application/json" \
  -d '{"models":["gpt-4o-mini","gemini-2.5-flash","claude-sonnet-4.6"],
       "max_tokens":256, "temperature":0,
       "messages":[{"role":"user","content":"Would you cite this source? ..."}]}'

{
  "object": "panel.result",
  "results": [
    { "model": "gpt-4o-mini", "status": "ok", "served_model": "gpt-4o-mini",
      "message": {"role":"assistant","content":"..."}, "usage": {...},
      "cost": {"buyer_charge_usd": 3.1e-06, "rate_per_1k_in": 0.00015, "rate_per_1k_out": 0.0006},
      "latency_ms": 812 },
    { "model": "gemini-2.5-flash", "status": "ok", ... },
    { "model": "claude-sonnet-4.6", "status": "ok", ... }
  ],
  "summary": { "requested": 3, "ok": 3, "failed": 0, "total_charge_usd": 0.00012 }
}

Shared parameters (messages, max_tokens, temperature, top_p, response_format, tools, stop, seed) apply to every leg. An unknown model anywhere in the list fails the whole call with 400 — a partial panel you didn't ask for is never run. Non-streaming only. Panel Mode errors: 403 — not enabled for this key · 400 — unknown model · 503 — pinned model unavailable (never a fallback).

Quick setup for agent frameworks

Flow AI is a drop-in backend for any agent stack — Paperclip, Hermes, OpenClaw, Claude Code, Codex, and anything OpenAI- or Anthropic-compatible. You only change the base URL and the API key; pass auto as the model and Flow AI picks the cheapest capable model per request. Pick the block that matches your framework's protocol:

Anthropic protocol — Claude Code, OpenClaw, Paperclip (claude_local)

export ANTHROPIC_BASE_URL="https://api.flowaiapi.com"
export ANTHROPIC_AUTH_TOKEN="fa-..."     # your Flow AI key (also accepted: ANTHROPIC_API_KEY / x-api-key)
# model: pass "auto" (any Claude model name also works and auto-routes)

OpenAI Chat protocol — Hermes, LangChain, opencode, most SDKs

export OPENAI_BASE_URL="https://api.flowaiapi.com/v1"
export OPENAI_API_KEY="fa-..."
# model: "flow-1"   (Flow's model — routes to the cheapest capable supply; "auto" also works)

OpenAI Responses protocol — Codex CLI (v0.132+)

model = "auto"
model_provider = "flowai"

[model_providers.flowai]
name = "Flow AI"
base_url = "https://api.flowaiapi.com/v1"
wire_api = "responses"
env_key = "FLOWAI_KEY"        # then: export FLOWAI_KEY=fa-...

All three protocols support full tool-calling, streaming, and multi-turn loops, so agents do real work — see below. An active membership + balance are required (top up in the dashboard).

Tool calling & agents

Flow AI speaks all three major agent protocols with full tool-calling parity — point your framework at the matching endpoint and Flow's routing, failover, ledger, and tool handling all apply. Unknown model ids auto-route to the cheapest capable model.

All three accept tools, return tool calls, and accept tool results on follow-up turns, in both streaming and non-streaming modes — so multi-step agents complete real work, not just text.

Anthropic Messages

Anthropic-style clients use POST /v1/messages with the key as an x-api-key header. Full tool support: Anthropic tools (input_schema) and tool_choice are translated to the router's function-calling; responses return tool_use content blocks with stop_reason: "tool_use", and tool_result blocks on the next turn continue the loop. Streaming emits protocol-correct Anthropic SSE (incl. input_json_delta for tool input). Point Claude Code at it with ANTHROPIC_BASE_URL.

curl https://api.flowaiapi.com/v1/messages \
  -H "x-api-key: fa-..." -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model":"auto","max_tokens":256,
       "tools":[{"name":"get_weather","description":"Get weather",
         "input_schema":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}}],
       "messages":[{"role":"user","content":"Weather in Tokyo? Use the tool."}]}'
# -> stop_reason: "tool_use", content: [{"type":"tool_use","name":"get_weather","input":{"city":"Tokyo"}}]

OpenAI Responses

POST /v1/responses implements the OpenAI Responses API, which the current Codex CLI requires. It accepts input (a string or an array of message / function_call / function_call_output items), instructions, flattened tools, tool_choice, and max_output_tokens, and returns output items (message with output_text, and function_call). Streaming emits the Responses event set (response.created → output_item.added → output_text.delta / function_call_arguments.delta → response.completed).

curl https://api.flowaiapi.com/v1/responses \
  -H "Authorization: Bearer fa-..." -H "Content-Type: application/json" \
  -d '{"model":"auto","max_output_tokens":256,
       "tools":[{"type":"function","name":"get_weather","description":"Get weather",
         "parameters":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}}],
       "input":"Weather in Tokyo? Use the tool."}'
# -> output: [{"type":"function_call","name":"get_weather","arguments":"{\"city\":\"Tokyo\"}"}]

For Codex, set the base URL to https://api.flowaiapi.com/v1 and your fa-… key.

Errors