foley

Find (or generate) the right sound effect for a moment of narration — and weave it in.

foley is a retrieval-first façade for sound effects: one simple surface over many sound sources (your own library, service APIs, and generative-AI models), a searchable index of every sound (by keyword and meaning), an agent that picks the right sound for a narrative context, and a compositor that places it under the voice.

It's the SFX sibling of arioso (a unified façade over AI music-generation backends): same discipline — one entry function, config-driven plugin adapters, a unified vocabulary translated per-backend, zero required core deps with lazy optional-deps — but centered on search rather than generation, with generation as just one of several sources.

Status: v1. All four stages — source, index, select, weave — plus the MCP server and the licensing/provenance, evaluation, and observability layers are implemented (Epic #13 complete). The API below is live; see the roadmap for what's next. Follow along in misc/docs/.

The idea

import foley

# The headline — right sounds for a narrative context:
# decompose the passage into salient sound events → search → verify → decide
candidates = foley.find("She pushed open the heavy oak door; rain hammered outside.")

# Direct hybrid search of your library (text query or a reference clip)
hits = foley.search("distant thunder rumble", k=10)  # commercial use is the default

# Generate a sound when nothing fits (arioso-style; pluggable backends)
clip = foley.generate("a single wooden door creak", backend="stable_audio", duration=3)

# Grow the library — ingest auto-tags, captions, and embeds every file
foley.ingest("~/my_sounds/", license="user-owned")  # omit it and rights stay unknown
foley.add_from("freesound", query="ocean waves", license="cc0")

# Compose: place the sounds under the narration (find → plan → weave)
timeline = foley.plan(candidates)  # the editable sound-design plan
result = foley.weave("narration.wav", timeline)  # mastered mix + SDH captions + credits

How it works — four stages

The selection tools publish as an MCP server (via py2mcp) so the same capabilities drive the agent, a CLI, and external hosts.

For AI agents

foley is AI-first: most callers are agents (directly, and via downstream packages like braidio and nw). The headline is one call — hand it narration text and it chooses tasteful, license-clean sounds and (given the audio) weaves them in:

import foley

# Plan only — choose sounds, get an editable timeline + a rationale:
result = foley.score("She pushed open the heavy oak door; rain hammered outside.")
print(result.rationale)

# Plan + render — weave under the actual narration audio:
result = foley.score(segments, audio="narration.wav", commercial_ok=True)
result.weave.audio  # mastered mix   result.weave.captions_vtt   # SDH captions
result.weave.credits  # attribution    result.timeline             # still editable

foley.score(...) is the stable contract braidio/nw call. The same surface is available as MCP tools (foley_score, foley_guide, foley_search, foley_preview, foley_weave, …) — serve them over stdio (foley-mcp) or authenticated HTTP (foley-mcp --http --token …).

Ship the agent kit into your agent host (a foley-sound-design skill, a /foley-score slash command, and a sound-designer subagent):

foley agent-install            # → ./.claude/{skills,commands,agents}/…

The one rule is restraint — most sentences need no sound. The design is grounded in the research reports below; taste heuristics (layering, ducking, onset, licensing) live in the skill.

Design & research

foley's design is grounded in five research reports (unified, cited):

• Design · Roadmap
• Research reports — sound sources, SFX generation, recognition/ tagging, embeddings/indexing, and the context-retrieval agent
• Deep-research prompt library — 12 ready-to-run prompts covering every dimension of the project

Install

pip install foley                         # core — dol-only, light; `import foley` stays minimal
pip install "foley[audio,clap,index]"     # the usual retrieval stack (CLAP + hybrid index)
foley demo                                # no-download smoke test on the bundled fixture
foley agent-install                       # drop the agent kit into ./.claude (skill + command + subagent)

foley is zero-dep at its core and grows by capability via optional extras — each imported lazily, so a bare install stays light. What each adds:

Nothing paid runs unless you ask for it. Having ANTHROPIC_API_KEY set does not switch the SELECT rungs to Claude: pass llm="anthropic" to find() / score() or set FOLEY_LLM=anthropic (llm="local" uses FOLEY_LLM_BASE_URL). By default they run the free deterministic fakes, or your local endpoint if one is configured. with foley.offline(): (or FOLEY_OFFLINE=1) blocks every external source and LLM call foley makes, on every surface, raising foley.runtime.EgressBlocked (model-weight downloads from Hugging Face are not covered yet).

foley.check_requirements() (and the foley_capabilities MCP tool) report what's installed and what's degraded. Full docs: thorwhalen.github.io/foley.

Changelog

Per-release changes are in CHANGELOG.md.

License

MIT