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26 production projects and 5 rule sets built with Python.
Production projects
- Desktop & Web AI Agent Workspace AI-agent MindsHub (formerly MindsDB) Splitting the client, API and agent into separately versioned git submodules lets each ship on its own cadence (a signed desktop app, a PyPI server package, an agent package), while one Makefile and one docker-compose.yml still bring the whole stack up together.
- Browser Agent Classic Jev Ultrafast Asking Jev for the operation and speculative per-operation targets in one request turns each browser step into a single constrained choice, while code validates the observed node and a small LLM writes only typed text.
- Identity Management Platform Classic Kanidm Kanidm ships every identity protocol from one Rust server with bundled SQLite and built-in replication, renders its UI with Axum, Askama and htmx, and maintains its own WebAuthn, JWT and LDAP crates to keep security-critical code under its control.
- Local AI Voice Cloning & Speech Studio Hybrid VoiceStudio Heavy model work stays in a Python FastAPI backend with a pluggable, hardware-aware engine registry. Desktop, web, MCP and remote-worker clients all use the same local API, so voice models run on the user's own GPU without a cloud dependency.
- LLM Knowledge Base & RAG Platform Classic WeKnora Retrieval, agents and the API sit in one Go service, and parsing is delegated to a Python gRPC service. Storage backends are pluggable, so the same codebase runs as a lite SQLite binary or a full Kubernetes deployment on existing Postgres, Elasticsearch or vector databases.
- Columnar Real-Time Analytical Database (OLAP) Classic ClickHouse A single CMake super-project that vendors ~150 native dependencies (Arrow, gRPC, AWS/Azure/GCS SDKs, NuRaft, Delta Lake kernel) under contrib/ lets ClickHouse ship a largely self-contained, dependency-free binary and Docker image.
- High-Throughput LLM Inference & Serving Engine AI-agent vLLM vLLM solves GPU memory fragmentation in large language model serving, delivering state-of-the-art throughput on commodity and enterprise hardware.
- LLM Application Framework & Agent Orchestrator AI-agent LangChain The current langchain package is a thin agent-building layer on top of LangGraph, with the older chain-based API kept separately as langchain-classic, and every model provider ships as its own versioned package so integrations can release independently of the core library.
- High-Performance Python Web API Framework Classic FastAPI FastAPI keeps its own dependency surface to Starlette and Pydantic and pushes servers, CLI, templating, forms and settings into an optional 'standard' extra, so a bare install stays minimal while typical apps opt into one bundle instead of assembling it dependency by dependency.
- Multi-Threaded In-Memory Key-Value Store Classic Dragonfly A fiber-based, shared-nothing execution model (one thread and one keyspace shard per core) lets Dragonfly scale vertically across cores, which single-threaded Redis cannot do without external sharding, while staying protocol-compatible so existing clients need no changes.
- Open-Source Error Tracking & APM Hybrid GlitchTip Lightweight Sentry-compatible application performance monitoring and error tracking platform. Built on Python Django and PostgreSQL with async Celery task queues for high-volume exception ingestion and Redis message buffering.
- AI Copilot & Assistant SDK AI-agent CopilotKit A shared AG-UI protocol plus a PlatformAdapter-based channel architecture let one agent implementation power web, mobile and chat-platform surfaces from a single Nx/pnpm monorepo instead of building a separate integration per surface.
- Self-Hosted AI Interface & Agent Hub AI-agent Open WebUI A SvelteKit-plus-FastAPI split lets one Docker image serve the UI and API together, while SQLAlchemy over SQLite or PostgreSQL and a swappable vector-store layer let self-hosters scale from a laptop to a multi-worker Redis-backed deployment without changing code.
- Photo & Video Management Platform Hybrid Immich Machine-learning inference is split into its own FastAPI/Python service with pluggable CPU/GPU backends so heavy models stay off the request-serving NestJS process, while BullMQ and Redis move thumbnailing and job dispatch out of the request/response path.
- AI Memory & Context Engine AI-agent Mem0 Keeping the core library provider-agnostic through optional dependency groups, while the hosted server bundles a fixed set of vector stores, LLM providers, auth, and rate limiting behind its own FastAPI app, lets self-hosters install a minimal footprint without forking the managed-service code path.
- Identity Provider & Access Management Classic Authentik Replacing Redis with three purpose-built PostgreSQL-backed packages for channels, cache, and task queueing shrinks the required external dependencies to just PostgreSQL, while protocol proxying is pushed into separate Go outposts instead of the main Django process.
- Business Intelligence Platform Classic Metabase Shipping the backend as one Clojure uberjar behind a bare JVM Docker image keeps self-hosting to a single artifact and runtime, while splitting SQL-dialect transform logic into a separate Python package lets that feature reuse an existing SQL-transpilation library instead of reimplementing it in Clojure.
- Developer Data Platform Classic Crowd.dev Decomposing ingestion, enrichment, and merging into many independent Temporal-orchestrated worker services under services/apps lets crowd.dev scale each pipeline stage separately, while shared @crowd/* libraries keep database, queue, search, and cache access consistent across all of them.
- AI Application Framework Hybrid Taipy Taipy enables Python data scientists to build full-stack web applications and data DAG pipelines using pure Python bindings.
- No-Code Database Platform Hybrid Baserow A Django/PostgreSQL core with Nuxt/Vue frontend layers lets Baserow add premium and enterprise features as separate installable packages on top of one open-source base, while Channels and Celery keep realtime sync and background work inside the same deployable unit.
- Error Tracking Platform Classic Sentry Pairing Django with Kafka and ClickHouse allows Sentry to ingest billions of events daily while providing sub-second analytical search across production traces.
- Data Pipeline Engine Hybrid Mage AI A Tornado Python server handles orchestration over a SQLAlchemy and Alembic-modeled Postgres database, while the pipeline editor ships as an independent Next.js frontend, and Spark or dbt support is layered in through opt-in Docker images and bundled adapters.
- Product Analytics Platform Hybrid PostHog Pairing Apache Kafka with ClickHouse columnar storage enables PostHog to ingest billions of monthly events while maintaining sub-second query response times.
- LLM App Builder AI-agent Dify.ai Agent execution and vector retrieval are split into separate, independently scalable services (a Go sandbox runtime, a FastAPI agent orchestrator, and pluggable vector-store providers) so untrusted tool code and swappable retrieval backends stay out of the core Flask API process.
Often used with Python
AGENTS.md rules and agent skills with Python
django-postgres-redis/AGENTS.mdDjango + PostgreSQL + Redis + CeleryProduction guidelines for Django 6 enterprise applications, PostgreSQL connection pooling, Redis caching, and Celery async workers. intermediatefastapi-jev-decision-service/AGENTS.mdFastAPI + Jev Decision Service (Python)Python rules for a Jev decision service: FastAPI, the async typesafe-sdk client, a typed question registry, confidence thresholds, decision logs and replay tests. intermediatefastapi-python-postgres/AGENTS.mdFastAPI + Python + PostgreSQL (Async SQLAlchemy & Pydantic v2)Production guidelines for FastAPI, Pydantic v2 validation, Async SQLAlchemy 2.0 (asyncpg), Alembic migrations, and PostgreSQL/Redis. intermediatejev-browser-agent/AGENTS.mdJev Browser Agent (Playwright + TypeSafe System One)Rules for a browser agent where Jev picks the action and target from an indexed element table, Noul checks goal and stuck, and a small LLM types only text. advancedlanggraph-python-fastapi/AGENTS.mdLangGraph + Python + FastAPI (Agentic AI Workflows)Production architecture for autonomous multi-agent systems, LangGraph cyclic state graphs, Pydantic v2 structured tool calling, and Chroma/Qdrant vector stores. advanced
Questions about Python
How many production projects use Python?
26 verified production projects in the STACK IT FAST directory use Python, each with a full architecture breakdown.
What is Python most often used with?
Across projects in the directory, Python is most commonly paired with TypeScript, Docker, PostgreSQL.
Is there an AGENTS.md rule or Claude Code skill for Python?
Yes — Django + PostgreSQL + Redis + Celery, FastAPI + Jev Decision Service (Python), FastAPI + Python + PostgreSQL (Async SQLAlchemy & Pydantic v2), Jev Browser Agent (Playwright + TypeSafe System One), LangGraph + Python + FastAPI (Agentic AI Workflows) each include a full AGENTS.md, CLAUDE.md, Cursor .mdc rule set and an installable SKILL.md covering Python.