falaw

Agent-friendly Python facade over fal.ai for generating and managing AI media (images, video, audio).

from falaw import generate_image, list_models, journal

r = generate_image("a tiger eye, macro, 35mm", quality="fast")
r.first.download(to="./tiger.png")

[m.id for m in list_models(category="image_to_video")]
journal.note("schnell at quality='fast' defaults to 1024x1024")

Why

fal-client already gives you 100+ models behind a uniform call. What agents (and humans) still struggle with is which model to use, what parameters it takes, and what to do with the URL it returns. falaw adds:

• Task-level verbs (generate_image, text_to_speech, ...) with smart model selection by quality tier.
• A queryable model registry --- no more grepping docs for IDs.
• Result / Asset objects that download, name, and organize outputs.
• A journal so each session leaves notes for the next one.
• A Claude skill, plus stub bridges for MCP and HTTP services --- all derived from the same tool registry.

Install

pip install 'falaw[fal]'   # to actually call fal.ai (pulls in fal-client)
export FAL_KEY="your-fal-api-key"

pip install falaw alone installs no fal.ai client. That is enough to build, price, hash, serialize and cache Plans (and to read cache hits), which is all a caller needs if it never makes a paid call. The first real network call raises falaw.FalClientNotInstalled (an ImportError) naming pip install 'falaw[fal]'. Anything that executes plans declares falaw[fal] as its requirement.

From the browser

The planning half of this package ships to npm as falaw-client (the ts/ directory): the catalogue with its prices, the cost rules, CallPlan and plan_hash are generated from and pinned to this package, and execution goes through a server relay because fal.ai forbids browser-held keys.

import { planGenerateImage, makePlan, totalCostUsd, execute, queueTransport } from 'falaw-client';
const plan = makePlan([planGenerateImage({ prompt: 'a tiger eye', quality: 'fast' })]);
totalCostUsd(plan);  // before any network
await execute(plan, { transport: queueTransport({ proxyUrl: '/api/fal/proxy', key }) });

See ts/README.md. After changing a dataclass, the catalogue, a cost rule, parse_response or the canonical byte-form, run python -m falaw export-schema, then cd ts && npm run codegen, and commit both — tests/test_schema_export.py and ts/src/codegen.test.ts fail until you do.

Core surface

Structured progress events

call_fal and cached_call_fal emit ProgressEvents at every lifecycle transition (queued, progress, log, done, error, cache_hit). Subscribe per-call (on_event=) or globally (falaw.subscribe(...)); the legacy on_log=print is still honored for backward compatibility.

from falaw import subscribe, generate_image

subscribe(lambda ev: print(f"[{ev.kind}] {ev.application} {ev.elapsed_s:.2f}s"))
generate_image("a tiger eye", quality="fast")

Cost estimation

ModelRecord.cost_estimate: CostEstimate | None carries a structured {kind, amount, currency} price (kinds: per_call | per_image | per_second | per_token | per_megapixel). estimate_scene_cost(scene) sums per-call costs and returns a CostRollup with per-line breakdown. Models without a populated cost_estimate appear in the rollup's skipped list so audits surface drift.

Pricing a direct Anthropic / OpenAI call

falaw prices calls made straight to the model vendor too, from one dated table (falaw/data/first_party_llm_prices.json), so an app does not keep its own copy:

from falaw import llm_call_cost_usd

usd, known = llm_call_cost_usd("anthropic", "claude-opus-4-7", response.usage)

usage is the vendor's own (a dict or the SDK object; OpenAI's Responses and Chat Completions shapes both work, cached tokens included). A model the table does not list is priced at the provider's dearest listed rates with known=False: never a free call, but not a ceiling either (an older model can cost more), so a caller that must never under-count refuses when known is false.

Re-pricing a saved plan

A CallPlan's estimated_cost_usd is frozen at plan time — right for a quote, wrong for a gate, because rate tables move (0.0.46 re-quoted every premium LLM call tenfold upward). Every plan_* therefore records how it priced the call in CallPlan.cost_basis: which pricer, which rate table and version, and the quantity hints (duration_s, token bounds) that price the call but deliberately never enter arguments. reprice_plan(plan) reads that back and re-quotes at today's rates — pure data, no network, no cache peek:

from falaw import plan_from_dict, reprice_plan

out = reprice_plan(plan_from_dict(saved_row))
out.plan  # same calls, same plan_hash, today's costs
out.known_delta_usd  # how much the priced part moved
out.changed  # per-call diffs: index, status, old, new, basis_changed
out.unpriced  # calls today's rates cannot price — refuse the gate

Per-call status is unchanged, changed, unknown (a basis today's tables cannot price) or no_basis (a plan saved before this existed). The last two clear the call's cost to None and light up Plan.has_unknown_costs: a stale figure re-presented as a current quote is the failure this fixes, so an unknown basis is reported, never trusted. cost_basis is descriptive only — it is omitted from the serialized dict when unset, and never enters plan_hash or the cache key, so existing plans and cassettes are unmoved.

A basis is only re-quoted by a pricer reading the same table it names; a mismatch is unknown, never a re-quote. Two tables are two sets of books, and re-pricing a caller's reconciled $0.50 row at falaw's published $0.01 is a 50x under-quote wearing the clothes of a price drop. So a call you quoted with your own llm_rates= needs your own Pricer to re-quote it: reprice_plan(plan, pricers={**DFLT_PRICERS, "llm_rates": mine}), where mine.table is the identity the basis carries.

Fan-out: partial results, bounded concurrency, per-call isolation

A Plan is a fan-out — 200 panels is 200 CallPlans in one Plan — so execute_plan's list[Artifact] has no room to say "call 7 failed, here are the other 199". execute_plan_isolated does:

from falaw import execute_plan_isolated

report = execute_plan_isolated(plan, concurrency=8)

for outcome in report.outcomes:  # always one per call, in plan order
    if outcome.ok:
        save(outcome.artifact)
    elif outcome.status == "failed":
        retry_later(outcome.call, outcome.error)  # retry this call verbatim
    else:
        replan(outcome.call, outcome.reason)  # blocked: its input does not exist

report.estimated_spend_usd, report.has_unknown_costs, report.cache_hit_savings_usd

Three states, not two. Failed means the call raised and can be retried as it is. Blocked means it never ran — a "<from N>" placeholder whose producer did not succeed — so it has to be re-planned, not retried. A caller that cannot tell them apart retries a call whose input does not exist.

concurrency bounds how many calls are in flight; independent calls run in parallel, and a call is never started beside the producer it consumes. It defaults to 1 (sequential) everywhere, here and in render_scene, because every call is a paid request: parallelism is opt-in. Weigh the vendor's rate limit and memory — materializing a media result peaks at roughly twice the asset's size, and concurrency multiplies that.

execute_plan keeps the plain list[Artifact] contract and re-raises the first failure unwrapped, so falaw's typed error hierarchy still reaches the caller. It is execute_plan_isolated(...).artifacts_or_raise() — one engine, two policies.

render_scene(..., concurrency=4) runs shots and beats in parallel through the same kind of thread pool (fal calls are HTTP-bound). Use iter_render_scene(...) to yield (kind, result) pairs as each unit completes — handy for live UI updates.

Three cache modes, because a re-run is not a cache bypass

execute_plan reads the cache and writes to it. Those are separate decisions, and it takes two flags rather than one so a caller can ask for either half.

refresh=True is what belongs behind a force / "re-verify this" switch. use_cache=False re-runs and discards, so the next consumer of the same plan pays for the result the forced run already bought — a guaranteed double charge, noise on an LLM plan and a real charge for nothing on an image or video one. Reach for use_cache=False only when you genuinely want no cache interaction at all.

use_cache=False, refresh=True raises: there is no key to write under when the cache is off.

Testing code that uses falaw

falaw content-addresses every media result, so execute_plan reads the bytes behind each result URL. A suite that stubs the fal response but not the asset transport is resolving its made-up URLs for real — and it passes while doing so, because a failed fetch degrades to a URL-only artifact with a UserWarning rather than raising.

falaw ships the fake, so nobody has to write one. Three autouse fixtures, each re-exportable into a conftest.py in one line:

# conftest.py
from falaw.testing import (  # noqa: F401
    fake_assets,  # url -> bytes, served from memory
    isolated_falaw_cache,  # cache / content store / url-index under tmp_path
    no_outbound_network,  # refuses AND records any non-loopback connection
)

def test_two_urls_one_content_address(fake_assets):
    shared = fake_assets.serve("http://cdn/a.png", b"identical bytes")
    fake_assets.serve("http://cdn/b.png", shared)  # pin explicit bytes
    fake_assets.fail("http://cdn/gone.png")  # 404, as an expired asset does
    ...
    assert fake_assets.fetched == [...]  # assert on the record

fake_assets installs itself through falaw.content.using_url_fetcher, the public transport seam, so it covers execute_plan, execute_plan_isolated, materialize_asset and content_ref_for_url at once — including calls your own public API makes, which have no asset_fetcher= argument to pass. An explicitly passed fetcher= still wins. file:// URLs are not faked (they are not the network); pin one explicitly if you want it served from memory.

using_url_fetcher is not test-only: it is also how you give falaw an authenticated, retrying, or mirrored transport in production, in one place instead of at every call site.

Two rules that are easy to get wrong:

• Don't reach for FALAW_FETCH_ARTIFACT_BYTES=0 to make a suite offline. It works by turning content addressing off — the suite goes hermetic and stops testing the feature.
• Assert on the record, not on an exception. falaw absorbs a failed fetch by design, so a guard that only raises is invisible. That is why both the fake and the network guard record every attempt, and why no_outbound_network fails the test at teardown.

Marker live_api opts a test back into the real transport and no network guard. A suite with different marker names builds its own fixtures from make_fake_assets_fixture / make_no_outbound_network_fixture.

Architecture

Single source of truth: a ToolSpec dataclass per tool. From it we derive every external surface:

falaw.registry  ──► bridges/skill.py    ──►  .claude/skills/falaw/SKILL.md
                ──► bridges/mcp.py      ──►  MCP server          (planned)
                ──► bridges/service.py  ──►  qh HTTP service     (planned)
                ──► (UI)                                          (planned)

Adding a new surface is a new bridge module, never a re-implementation of the operations.

Self-improvement loop

Every session can read and write the agent journal at ~/.config/falaw/journal/. The Claude skill instructs Claude to:

1. Read recent entries before novel work.
2. Write a note / issue / improvement when something surprises it.

call_fal auto-journals failures with the application id and arguments, so the next session recognizes the trap.

Layout

falaw/
  base.py            ToolSpec, ModelRecord
  core.py            call_fal: subscribe + auto-journal
  registry.py        register_tool, list/get/pick model
  reprice.py         reprice_plan: re-quote a saved Plan at today's rates
  results.py         Asset, Result, parse_response
  session.py         Session
  journal.py         file-backed journal
  operations/
    images.py        generate_image
    audio.py         text_to_speech
  bridges/
    skill.py         render Claude SKILL.md from registry
    mcp.py           (stub)
    service.py       (stub)
  data/
    models.json      seed catalog
    skills/falaw/    generated skill files (shipped with package)
misc/
  docs/              aggregated fal.ai docs (3MB md, llms.txt, llms-full.txt)
  regenerate_skill.py
tests/

Regenerate the skill after adding a tool

python misc/regenerate_skill.py

Writes falaw/data/skills/falaw/SKILL.md and .claude/skills/falaw/SKILL.md.

Status

v0 --- functional core, real Claude skill, stubs for MCP and HTTP service. The bridges share the same registry, so filling in the stubs is additive.

Roadmap

See misc/docs/roadmap.md for the ordered work and the standing constraints. In short, four tracks:

1. Content addressing --- landed (falaw#14). Artifact.asset_id is the SHA-256 of the artifact's bytes and the per-call cache key holds upstream content hashes, not fal CDN URLs. fal states that every upload gets a unique URL and that expired files are permanently deleted, so URL-keying meant a byte-identical regeneration missed the cache and a stored response decayed into a dead link. Bytes now route through lacing.ArtifactStore.put_blob_stream. Still open: the plan-time cache peek keys on unresolved arguments (D2), which lands with track 2.
2. Backend-parametric Plan --- CallPlan.backend + registry dispatch in execute_plan, so a second execution backend is still priced, cached and dry-runnable.
3. Licence-and-terms ledger --- per (model, backend), queried at plan time, unknown means refuse.
4. Cost data from the vendor --- read fal's pricing API instead of hand-maintaining data/models.json (19 of 40 records currently carry no structured price at all).