Scira

Scira answers a question by researching it. You type a question, upload a PDF or paste a URL, and the agent breaks the request into sub-tasks, searches live sources, cross-checks the evidence and returns an answer whose claims each carry an inline citation you can click and audit.

The application is built on Next.js with Tailwind CSS and shadcn/ui, and routes model calls through the Vercel AI SDK, so it runs against xAI, OpenAI, Anthropic, Google, Mistral, DeepSeek and many other providers. Retrieval comes from Exa, Firecrawl, Parallel and Tavily, with Upstash for rate limiting and Drizzle ORM over the database.

Seventeen search modes and twenty-eight tools cover most of what a research session needs.

• Agentic planning: breaks a complex question into steps, picks the models and tools for each, then runs the workflow end to end.
• Grounded retrieval: every answer ships with citations, so the evidence stays auditable rather than implied.
• Specialist modes: academic papers, X posts, Reddit threads, GitHub repositories, stock and crypto data, YouTube transcripts and more.
• Code interpreter: writes and executes Python in a sandboxed Daytona environment and generates charts from the result.
• Lookouts: schedules recurring agents that watch a topic, track changes and email you updates.

Self-hosting runs through Docker Compose or a plain Docker build, and needs API keys for whichever providers you want. The AGPL-3.0 license means bringing your own models and wiring in custom tools is expected rather than tolerated.