semantica/GROWTH.md

Growth & Distribution Playbook

North star: 10,000 developers who actually use Semantica in real projects, not a raw PyPI download number. Downloads are a lagging indicator of distribution, not a target to optimize directly.

GitHub stars → Website visitors → PyPI installs → Weekly active users → Production deployments → Enterprise customers

The last two matter far more than the download count.

Guardrails — do not do this

• No fake/looping CI jobs that repeatedly pip install semantica purely to inflate the graph. It's detectable, it produces zero real users, and it damages credibility with anyone doing diligence (investors, enterprise buyers, security reviewers).
• No package-splitting purely to multiply install counts — only split into semantica-* packages when there's a real architectural reason.
• No meaningless Docker pulls or notebook launches with no real content behind them.
• Every item below should get someone from "installed it" to "used it for something real." If a channel can't do that, it's not worth building.

30-day priority sprint

Ordered by leverage-to-effort ratio; do these first.

#	Initiative	Target
1	✅ GitHub Actions example + reusable setup-semantica composite action + install-matrix badge	done
2	Google Colab notebooks	10
3	Docker images (RAG, Graph, Agent, API)	4-5
4	Hugging Face Spaces demos	3-4
5	LangChain integration + example	1
6	LlamaIndex integration + example	1
7	Vector/graph DB integrations (Qdrant, Weaviate, Neo4j)	3
8	MCP server + example	1 (already have mcp/ — package as a distributable example)
9	Production-quality starter repos (FastAPI, Streamlit, Gradio)	3
10	awesome-rag / awesome-llm / awesome-knowledge-graph list submissions	3+ PRs

Push everything through: GitHub → Discord (sV34vps5hH) → X (@BuildSemantica) → GitHub Discussions → Reddit → Hacker News → relevant newsletters.

Full channel checklist

CI/CD (highest-intent distribution — installs tied to real pipelines)

• GitHub Actions example in examples/ci/github-actions.yml
• Reusable composite GitHub Action — .github/actions/setup-semantica, modeled on actions/setup-python; usable by any repo as uses: semantica-agi/semantica/.github/actions/setup-semantica@main
• "pip install" status badge in the README, backed by .github/workflows/install-matrix.yml — verifies the published package installs cleanly on Ubuntu/macOS/Windows across Python 3.9-3.12, weekly + on every release
• GitLab CI template — examples/ci/gitlab-ci.yml
• CircleCI template — examples/ci/circleci-config.yml
• Jenkins, Azure DevOps, Bitbucket Pipelines, Buildkite, Travis CI equivalents

Release pipeline hardening (already had Trusted Publishing/OIDC + SLSA attestation — this rounds it out to match top-tier OSS release practice)

• twine check gate in .github/workflows/release.yml before publish — catches a broken PyPI long-description render before it goes live instead of after (a malformed README on the live PyPI page is a silent conversion killer)
• CITATION.cff (see Academic & research below)
• OpenSSF Scorecard (see Discoverability below)
• Considered and deliberately skipped: Release Drafter / auto-generated changelogs — this repo hand-curates CHANGELOG.md with far more detail (PR numbers, contributors, phase-1 limitations) than a bot would produce. Don't introduce this without checking with maintainers first.
• Renovate / Dependabot config templates that auto-bump the semantica version in downstream repos — real recurring CI runs on real adopters
• Nightly scheduled workflow template that tests a downstream project against semantica@latest

Containers & dev environments

• Official Docker images: RAG, Graph, Agent, API, +Postgres, +Neo4j, +Qdrant
• docker-compose examples (repo already has docker-compose.dev.yml / docker-compose.yml as a base)
• .devcontainer/devcontainer.json for one-click "Reopen in Container"
• GitHub Codespaces-ready config
• Gitpod config
• "Use this template" GitHub repo button so new projects start with semantica in requirements.txt

Notebooks & hosted demos

• 10-20 Google Colab notebooks (Graph RAG, agent memory, entity resolution, semantic search, document intelligence)
• Kaggle Notebooks/Kernels
• Binder / mybinder.org config for instant repo launch
• SageMaker Studio Lab / Databricks Community Edition / Paperspace Gradient examples
• Hugging Face Spaces (Streamlit/Gradio) demos with semantica in requirements.txt
• Public hosted playground (source on GitHub, install visible)

Framework & data-store integrations

• LangChain integration — integrations/langchain/ (SemanticaRetriever, SemanticaVectorStore, SemanticaKGTool/SemanticaDecisionTool), pip install semantica[langchain], shipped in 0.6.7
• LlamaIndex integration + example
• LangGraph example
• Neo4j integration/example (docs already list it as a supported graph store — turn into a runnable example repo)
• Vector DB examples: Qdrant, Weaviate, Milvus, Pinecone, Chroma, FAISS, pgvector, OpenSearch/Elasticsearch (FAISS/Pinecone/Weaviate/Qdrant/Milvus/PgVector already supported per docs/community-projects.md — package each as a standalone example)
• LLM provider quickstarts: OpenAI, Anthropic, Gemini, Groq, Ollama, HuggingFace, DeepSeek, LiteLLM (already-supported providers per docs — each gets its own copy-paste quickstart)
• CrewAI / Agno integration examples (already documented under docs/integrations/) — promote as standalone repos, not just docs pages

Package managers & installers

• conda-forge feedstock
• Homebrew formula for the CLI
• Nix/nixpkgs packaging
• Chocolatey / Scoop (Windows)
• Document uv add semantica and poetry add semantica explicitly alongside pip install

Downstream packages & CLI

• Genuinely useful semantica-* packages only where warranted (e.g. semantica-rag, semantica-connectors) — each pulls semantica as a real dependency
• Make sure semantica init / ingest / index / query / serve CLI flows are the default onboarding path in every tutorial
• VS Code extension wrapping the CLI (scaffold + run commands from the command palette)
• JetBrains plugin equivalent

Templates & starters

• Cookiecutter templates: cookiecutter-semantic-rag, cookiecutter-ai-agent, cookiecutter-enterprise-rag
• Starter repos: FastAPI, Streamlit, Gradio, Next.js frontend + Semantica backend
• Cloud deploy templates: AWS, GCP, Azure, Modal, Railway, Render, Fly.io (repo already has deploy/azure, deploy/gcp, deploy/fly, deploy/railway, deploy/render, deploy/kubernetes, deploy/helm — link these prominently from the README/quickstart, they're already-built distribution surface)
• Terraform / Pulumi / Helm modules published to their respective registries

Discoverability & curation

• Submit to awesome-rag, awesome-llm, awesome-knowledge-graph, awesome-python
• Pitch newsletters with engaged Python/AI audiences (Python Weekly, Import AI, TLDR AI, etc.)
• PyPI trove classifiers/keywords and project.urls (Homepage/Docs/Repository/Changelog/Bug Tracker) — already complete in pyproject.toml
• Get listed on Papers With Code for any retrieval/graph-RAG benchmark work
• OpenSSF Scorecard badge + weekly workflow (.github/workflows/scorecard.yml) — a concrete trust signal security/procurement teams check before greenlighting adoption, which gates real (non-CI-bot) install growth at enterprises

Academic & research

• CITATION.cff at repo root — enables GitHub's native "Cite this repository" button, feeds Google Scholar/academic tooling; complements docs/citation.md (still needs a real Zenodo DOI to replace the XXXXXXX placeholder in both places once one is minted)
• arXiv paper if there's real architectural novelty to describe
• Zenodo DOI for citability (docs/citation.md already exists — make sure it points to a real DOI)
• Workshop/tutorial sessions at PyData/ODSC-style events with hands-on install steps
• University course material / bootcamp adoption outreach

Content

• Reproducible benchmark repos (Graph RAG vs vector RAG, retrieval@k, enterprise-scale retrieval) with pip install semantica && python benchmark.py
• 20-30 real-world example applications (RAG, enterprise document intelligence, financial entity graphs, code knowledge graphs, research discovery, agent memory)
• Blog/tutorial posts on Dev.to, Medium, personal blogs — always with runnable code, not just prose
• Contribute integrations/PRs to other projects building RAG/agents/knowledge graphs — "I implemented Semantica support" beats "please use Semantica"

Tracking

Don't just watch the raw PyPI number — use download analytics (e.g. PePy) to separate CI/bot traffic from real installs, and track the funnel above end-to-end where possible (stars → site visits → installs → weekly actives).