metalworks 0.0.1

Open-source marketing research and Reddit engagement library — demand reports from real conversations, usable as a Python library, CLI, MCP server, or Claude Code plugin.

metalworks

Go from a startup idea to launch, grounded in real demand.

Give metalworks one sentence about what you want to build. It reads real Reddit conversations to tell you whether people actually want it, then turns that into the things you need to launch: your positioning, the competitors to beat, a marketing site, a build plan for your coding agent, and launch copy. Every claim links back to a real comment you can click — nothing is invented.

A Python library (also a CLI, an MCP server, and a Claude Code plugin). MIT licensed and built to be embedded — every layer (LLM, search, embeddings, storage, data source) is a swappable protocol.

Status: pre-release (0.0.1). APIs are unstable below 1.0. The stable surface is the Metalworks facade, the metalworks.contract Pydantic models, and the MCP tool contracts. Everything else may change in any 0.x release. Some surfaces below are marked planned for 0.1 where they are not wired yet; this README is honest about what runs today.

Read the USAGE_POLICY before you use the Reddit side. Short version: authentic, disclosed engagement only. No fake personas, no vote manipulation, no coordinated inauthentic behavior.

Quickstart (no API key)

The headline is the offline demo: fake models on a bundled Reddit corpus, no keys, no network.

pip install "metalworks[research]"

from metalworks import Metalworks

research = Metalworks.demo().research("Is there demand for a focus supplement?",
                                     subreddits=["Supplements"])
report = research.demand   # .research() returns a Research bundle; the report is on .demand
print(report.verdict)
for cluster in report.ranked_clusters:
    print(cluster.signal, cluster.distinct_author_count, cluster.claim)

Then: a real demand report

Install a provider SDK and set its key. Google or OpenAI is simplest — one key covers both the chat model and the embeddings the pipeline needs:

pip install "metalworks[google,research]"   # or [openai,research]
export GOOGLE_API_KEY=...                    # chat + embeddings

Anthropic is chat-only (no embeddings API), so an ANTHROPIC_API_KEY must be paired with a GOOGLE_API_KEY or OPENAI_API_KEY.

Prefer Vertex AI over an API key? Set GOOGLE_GENAI_USE_VERTEXAI=true plus VERTEX_PROJECT_ID and VERTEX_LOCATION and the Google adapters authenticate via Application Default Credentials. See docs/configuration.md.

from metalworks import Metalworks

mw = Metalworks()             # provider inferred; or Metalworks(model="anthropic/claude-opus-4-6")
research = mw.research("Is there demand for a focus supplement aimed at developers?",
                       subreddits=["Nootropics", "Supplements"])
report = research.demand

Every quote in report.ranked_clusters is the exact text of a real Reddit comment, and every web finding carries its real source URL — never model prose. Anything metalworks can't back with a real quote, it drops. See why you can trust the output.

The Metalworks facade is the easy path over run_research / run_discovery and the protocols — drop down to those whenever you want more control. Submissions come from the Hugging Face open-index/arctic Parquet mirror; comments from the live Arctic Shift API. Set HF_TOKEN for windows beyond a few months. To read the submission corpus from a Supabase Storage bucket instead (no HF runtime dependency), install metalworks[supabase] and set ARCTIC_SHIFT_SOURCE=mirror, or bring your own corpus to skip Arctic Shift.

Extras

Core stays lean (pydantic, httpx, typer, rich). Everything that pulls a provider SDK or a heavy dependency lives behind an extra, so you install only what matches the keys you have. Adapters lazy-import their SDK and raise MissingExtraError with the exact pip install command when it is absent.

pip install "metalworks[google]"
pip install "metalworks[research,reddit]"
pip install "metalworks[all]"

Extra	Pulls in	For
anthropic	anthropic	Claude ChatModel adapter
openai	openai	OpenAI ChatModel + embedding adapters
google	google-genai	Gemini ChatModel (native grounding) + embeddings
litellm	litellm	Optional long-tail provider routing
reddit	redditwarp, cryptography	Reddit search, OAuth, posting, token encryption
arctic	duckdb	Read Arctic Shift Parquet shards (submissions corpus)
research	arctic + rank-bm25	The full demand-report pipeline
supabase	arctic + supabase	ArcticMirrorReader — Arctic corpus from a Supabase Storage bucket (ARCTIC_SHIFT_SOURCE=mirror)
exa	exa-py	Exa SearchProvider adapter
tavily	tavily-python	Tavily SearchProvider adapter
mcp	mcp[cli]	MCP server surface
all	everything above	Kitchen sink
dev	pytest, ruff, pyright, respx	Contributors

A bare import metalworks pulls in no provider modules; CI asserts this.

Architecture

metalworks owns small, versioned protocols and ships thin adapters over official provider SDKs. It does not route every provider through LiteLLM by default. The protocols are the seam your code and the pipeline speak through:

• ChatModel — complete_text / complete_structured, model bound at adapter construction. GroundedChatModel adds model-native web grounding with full provenance (chunks plus character-offset supports).
• SearchProvider — external web search (Exa, Tavily).
• EmbeddingProvider — embeddings with a hard index-identity guard.
• The typed repos (CorpusRepo, BriefRepo, RunRepo, AccountRepo, OpportunityRepo, InboxRepo) are the storage protocol. MemoryStores and SqliteStores ship in core; hosted backends (Postgres/PostgREST) are a custom store you implement downstream — see docs/custom-store.md.

See docs/protocols.md for signatures.

Two verticals sit on top of those protocols:

• Research (metalworks.research) — turns an idea into a clustered DemandReport of real, permalinked Reddit quotes. Entry point: run_research(deps, brief=...). Seven functions build on a finished report, each linking its output back to that report's real quotes: positioning (build_positioning_brief), competitors (run_competitor_map), surface + UX (decide_surface / build_ux_skeleton), marketing site (build_marketing_site), launch assets (build_launch_assets / plan_channels), a content/SEO plan (content_plan_from_report), and a build plan + scaffold (build_spec_from_report / scaffold).
• Reddit (metalworks.reddit) — OAuth, search, subreddit intel, inbox, posting, in-library rate limiting, and a deterministic compliance gate (heuristic_check) that runs offline on reply and post text.

Four form factors share that contract:

1. Library — from metalworks import Metalworks, or the functions and protocols underneath.
2. CLI — metalworks research|reddit|arctic|discovery run, the report commands (metalworks research position|competitor-map|surface|site|launch|content-plan, metalworks build init), metalworks quickstart, metalworks doctor, metalworks mcp serve.
3. MCP server — zero-key data tools plus key-gated pipeline tools, over stdio or SSE.
4. Claude Code plugin — /demand-report and friends, zero keys on the demo path (/plugin marketplace add Lab2A/metalworks).

Testing your own adapters and backends

The conformance suites metalworks holds itself to ship as a public module:

from metalworks.testing import FakeChatModel, FakeEmbedding, check_all_repos

def test_my_backend():
    check_all_repos(MyBackend())   # includes the >1000-row pagination case

See docs/custom-chatmodel.md and docs/custom-store.md.

Docs

Full docs: metalworks.lab2a.ai

• Installation · Quickstart · Build a startup, end to end
• Capabilities: demand research · positioning & competitors · design & site · build spec · launch · content & SEO · Reddit engagement
• Why you can trust the output · Data model
• Reference: Python SDK · CLI · MCP tools · Configuration · Using with AI agents
• Extending: overview · protocols · custom model/corpus/store

Project

• License: MIT.
• Usage policy: USAGE_POLICY.md.
• Security: SECURITY.md.
• Contributing: CONTRIBUTING.md.
• Changes: CHANGELOG.md.
• Org: Lab2A.