At a Glance:
Scira is an agentic research platform that plans multi-step queries, retrieves live web information using 28 built-in tools, and provides answers with inline citations across 17 configurable search modes including web, academic, and financial data.
Overview:
Scira is an open-source, agentic research platform designed to handle complex research tasks by breaking questions into sub-steps, planning tool usage, and executing multi-step retrieval workflows. It supports text questions, PDF uploads, and URL inputs, then returns answers with inline citations from live sources. The platform is built with Next.js and provides a web UI at scira.ai, with support for self-hosting via Docker or Node.js. It targets users who need cited, multi-source research that spans general web content, academic papers, code context, and specialized financial or social media data.
Key Decision Points:
Self-hostable under AGPL-3.0: You can deploy your own instance using Docker Compose or Node.js, as documented in the README.
Multi-model support: The platform supports a wide range of LLMs from providers including xAI, OpenAI, Anthropic, Google, DeepSeek, and others, allowing model selection.
17 search modes available: Research can be scoped to specific domains like Web, Academic, X, Stocks, Code, Reddit, GitHub, Crypto, YouTube, Spotify, and more, with some modes requiring a Pro account.
Tool-driven retrieval: The agent uses 28 tools for search, financial data, media lookup, translation, code execution within a Daytona sandbox, and file querying, not a single AI response.
Recurring research via Lookouts: The platform can schedule recurring research agents that monitor specified topics and email updates, providing ongoing awareness.
Core Features:
Agentic Planning: The system breaks complex questions into sub-tasks, selects appropriate models and tools, and executes multi-step workflows to produce an answer.
Inline Citations: Every generated answer includes clickable citations linked to the source evidence, allowing direct verification.
17 Configurable Search Modes: Users can scope research to specific data sources like X posts, academic papers, GitHub repositories, stock charts, prediction markets, or uploaded files.
28 Integrated Tools: The agent leverages tools for web search deduplication, academic paper access, currency conversion, Python code execution in a sandboxed environment, and semantic search over uploaded documents like PDFs and CSVs.
Lookouts Scheduling: Users can create recurring agents that monitor topics for changes and send updates via email.
URL and File Input: Users can provide context by pasting URLs for content extraction or uploading PDF and office documents for semantic search.
Use Cases:
Researchers can use the Academic and Web modes to gather cited information from papers and live sources while auditing claims through inline links.
Developers can use the Code, GitHub, and Code Interpreter modes to get programming context, search repositories, and execute Python in a sandbox for data analysis.
Users monitoring specific domains can schedule Lookouts to track changes in stocks, prediction markets, or general web topics and receive email alerts.
Self-hosters can deploy their own instance of the platform under the AGPL-3.0 license, bringing their own API keys for LLMs and search tools.
Open-Source Alternative Value:
Scira is available under the AGPL-3.0 license, which provides the ability to self-host the platform using Docker or Node.js and configure it with your own choice of LLM providers and API keys for search and data tools. The project's value as an open-source alternative lies in its transparent, tool-driven agent architecture that users can deploy and tailor themselves, rather than relying solely on the hosted scira.ai service. The README documents a clear path for local deployment, model selection across many providers, and extension through the platform's modular interface for models, tools, and data sources.
