Argus is a retrieval platform for AI agents. Its core purpose is to unify multiple search providers under a single API, providing AI agents, RAG pipelines, and operations teams with reliable search, content scraping, and local evidence retention capabilities, avoiding the need to stitch together multiple search APIs yourself.
In terms of main capabilities, Argus supports 14 search providers, using free-first tiered routing, with providers whose budgets are exhausted automatically skipped. It can start with zero keys: after installing via pip install argus-search, DuckDuckGo and Yahoo are available with no account required. Self-hosted SearXNG can aggregate over 70 engines, including Google, Bing, Yahoo, Startpage, Ecosia, Qwant, and others, running through a single Docker container. Content extraction uses a 12-step process, returning full page text with quality gating rather than just links. It also provides dead link recovery (with the Wayback Machine and archive fallback), site important content capture, local document plus research package building, multi-turn sessions (passing context via session_id), scoring attribution, a usage dashboard, four search modes (discovery, research, recovery, grounding), and four integration paths (HTTP API, CLI, MCP server, Python SDK).
In terms of usage, the text gives two main modes. Mode one is local CLI zero configuration: DuckDuckGo handles search directly, with no account, key, or container required, suitable for any machine with Python 3.11+. For MCP clients (Claude Code, Codex, OpenCode, Cursor, VS Code), there are corresponding configuration commands, and the client must be restarted after configuration. Mode two is the full-stack server: via SearXNG (disabled by default, requires setting ARGUS_SEARXNG_ENABLED=true) plus local JS-rendered content extraction, it can run with 512MB of memory. Integration methods also include the HTTP API (all endpoints are prefixed with /api, with OpenAPI documentation located at http://localhost:8000/docs）、CLI（所有命令支持 --json) and the Python SDK. The MCP adapter is a stateless execution layer that delegates to the authenticated HTTP authoritative service.
Applicable scenarios include: AI agent builders, RAG pipelines, and operations teams needing reliable search and local evidence; specific workflows include recovering dead articles, capturing important site pages, and building document plus research packages.
In terms of limitations, the project status is beta, and the retrieval workflows and corpus model are production-oriented but still maturing. The text explicitly states the production checkpoint time as September 7, 2026, when DuckDuckGo was intermittently failing and was shut down after the fetch policy blocked it; paid providers have not yet been documented because their non-secret credential versions are tied to account scope, and have not yet been integrated; browser capabilities and recovery metadata evidence still need improvement. Production readiness status is ready=true, degraded. Runtime data is written to a writable user data directory (resolved by platformdirs, or overridden via ARGUS_DATA_ROOT), rather than the repository checkout directory. Remote HTTP calls require sending ARGUS_API_KEY. The quotas, prices, and current availability of paid providers are not given as specific values in the materials, and it is explicitly stated that these are package-level provider tiers and claimed quotas, not proof of current production availability.
