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🌐 Open Politics HQ

Open Source Intelligence Platform
Talk: Open Source Political Intelligence @ CCCB Datengarten
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Open Politics HQ Platform
The modern information landscape is chaotic and difficult to navigate. Whether you need to prove a point, dive deeper into a topic, or simply understand what’s happening, the challenge is the same: making sense of vast amounts of unstructured information. This platform serves students seeking better research tools, organizations drowning in unstructured data, NGOs overwhelmed by document management, and businesses struggling with process inefficiencies.

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The Idea

A journalist knows how to identify “security framing” in news coverage. A policy analyst knows what counts as “meaningful stakeholder engagement” in legislative proposals. A bureaucrat knows whether a grant application is properly filled out. That expertise lives in their heads, maybe in spreadsheets and notes. This works great for tens of documents. At hundreds or thousands, you’re either stuck or you need to hire engineers. Meanwhile, sophisticated analysis infrastructure — the kind that lets you systematically apply analytical frameworks at scale — has only been available to well-funded institutions. HQ. Define your analytical questions in plain language. Apply them at scale. The key innovation: schemas are shareable, transparent, and improvable. Other researchers can see exactly how you defined your framework, critique it, refine it, or apply it to their own data. For example: Imagine as a journalist, you are analyzing 200 news articles. You create a schema: 
Primary source cited? → [government, activist, expert, anonymous]
Emotional intensity?  → 1-5
Which side gets final word? → string

# + your specific definitions of "emotional intesity"
# Your description becomes your method, you can measure and evolve it.
Table with annotation Run it. Get structured data showing systematic patterns. Export visualizations. The principle scales. A journalist knows “framing.” A policy analyst knows “stakeholder engagement.” An NGO worker knows what signals a policy shift. Schemas let you take that knowledge and apply it systematically across thousands of documents. The same infrastructure that analyzes legislation can sort emails, process intake forms, track regulatory changes, or monitor media coverage. This capability shouldn’t be locked behind institutional walls. We’re building it as public infrastructure — schemas, geocoding, vector search, local AI. Basic components, simple when you list them out. But that’s the point. These are basic intelligence capabilities an open society needs, like libraries or archives, and they should be equally accessible. Open source. Self-hostable. Bring your own LLM keys if you want privacy. Share your analytical frameworks publicly if you want transparency. Use it for journalism, research, advocacy, governance — anything that serves the public interest. Importantly: none of this would exist without the many people dedicated to open source. We’re assembling the hard work of countless other projects. We are standing on the shoulders of a massive collaborative ecosystem that makes this possible. We are grateful for their work and we are proud to be part of it. Additional Demo Video

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How It Works

  1. Ingest content from files, URLs, search results, RSS feeds
Example result
  1. Define schemas that describe what information to extract
  2. Run analysis using AI to apply your schema at scale
  3. Explore results through tables, visualizations, maps, or export the data
Supported formats: PDFs, web articles, text, CSV, RSS feeds
Coming soon: Images, audio, email inbox ingestion The schemas are the key innovation. They let you formalize your analytical method in natural language, making qualitative approaches reproducible and transparent. Other researchers can see exactly how you defined “populist rhetoric” or “security framing” and apply the same lens to their data. Example annotation schema

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Chat & MCP

Chat Interface Demo The Chat & MCP (Model Context Protocol) centralizes all platform tools (asset management, schema-based analysis, vector search, content ingestion) into a unified analysis access point that lets you work with structured analytical methods through conversational AI. This combination allows for various workflows or researching & synthesizing data. Editing MCP configs (upcoming on a per user-level) furthermore allows you to grant your LLM access to almost arbitray outside data and use it for your analysis.

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Infospaces & Vector Storage

Your assets, schemas and analysis results are scoped to “Information Spaces” that you can use to curate information. Each Information Space is a dedicated vector space. Use vector embeddings from local or cloud models to search through your data, cluster it (upcoming) and find duplicates. Infospace Manager
  • Webapp — hosted instance (public registration opening soon)
  • Documentation — user guides and tutorials
  • Forum — community discussions

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Getting Started

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Option 1: Use the Hosted Instance

The easiest way to start. We host the infrastructure, you bring your own LLM API keys (see supported providers).
  1. Register at open-politics.org/accounts/register
  2. Add your API keys on the home page
  3. Start uploading content and creating schemas
Your account also works on the forum for community support.

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Option 2: Self-Host with Docker

For privacy, customization, or institutional requirements. Run everything on your own infrastructure. 
git clone https://github.com/open-politics/open-politics-hq.git
cd open-politics-hq
bash prepare.sh
cp .env.example .env
# Edit .env with your configuration
docker compose up
Default admin credentials (change these): 
FIRST_SUPERUSER=app_user
FIRST_SUPERUSER_PASSWORD=app_user_password

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Deployment Flexibility

Fully Local: Run everything on your own hardware. Good for air-gapped environments or complete data control. Hybrid: Run the application locally but use managed services (AWS RDS, Upstash Redis, S3) to reduce operational burden. Kubernetes: We provide a Helm chart at https://github.com/open-politics/open-politics-hq/tree/main/.deploymentskubernetes/open-politics-hq-deployment

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Architecture

The platform is built from several independent services that work together. You can run them all locally or mix local and managed services.

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Core Components

ComponentWhat It DoesTechnology
BackendAPI, analysis jobs, MCP serverFastAPI + Python
FrontendWeb interfaceNext.js + React
WorkerBackground processing for large jobsCelery
DatabaseData storage with vector searchPostgreSQL + PGVector
Object StorageFile storage for uploadsMinIO (S3-compatible)
Cache/QueueSession management, job queuesRedis
GeocodingLocation extraction and mappingPelias
LLM (optional)Local AI inferenceOllama

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LLM Support

Connect any of these AI providers:
  • Anthropic (Claude Sonnet, etc.)
  • OpenAI (GPT-5, etc.)
  • Google (Gemini models)
  • Ollama (run models locally — Llama, OAI OSS, Qwen, etc.)
Configure API keys in the web interface or run Ollama locally for complete privacy.

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Contributing

We’re building this in the open. The codebase, analytical methods, and documentation are all public and improvable. Ways to contribute:
  • Report bugs or suggest features (GitHub Issues)
  • Improve documentation or add examples
  • Build and share analytical schemas
  • Contribute code (see backend and frontend READMEs)
  • Join community discussions on the forum

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Contact & Community

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License

AGPLv3 — see LICENSE This means you can use, modify, and distribute this software, but any modifications or services built on it must also be open source. You can get an enterprise license for private use modifications which are not publicly deployed for one year at a time under strict ethical guidelines.

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Project Origins & Story

This project started at Freie Universität Berlin’s Political Science department, born from a student’s frustration with the disconnect of theoretically available tools and the practical ones available. Our first official funding came from the European Horizon NGI Search project, and before that we were lucky to have a warm circle of friends and early supporters who believed in the idea before there was much to show. The business model is intentionally simple: we’ll try to keep a free hosted version running as much as we can shoulder, but hosting HQ is rather cheap as most of the cost comes from LLMs. We focus on helping organizations deploy and use the platform effectively — infrastructure orchestration, custom implementations, training. The platform itself always stays open source and self-hostable. Our “marketing” is the research that gets done with these tools. If people publish interesting work using HQ, that’s worth more than any advertising campaign. If you want to support this mission or just talk about what you’re building, reach out: engage@open-politics.org
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