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Lets AI agents discover and run Apify Actors as MCP tools for web scraping and automation; supports OAuth/Streamable HTTP, dynamic tool discovery, output-schema inference, and agentic payments (AGI, x402, Skyfire) so agents like Claude, ChatGPT, or VS Code can call ready-made scrapers and automations.
Indexes full text of visited web pages and local files on a self‑hosted server so you can search your personal knowledge from a web UI, terminal, CLI, or an AI assistant. Runs without mandatory telemetry, offers a browser extension for automatic capture, and supports optional semantic search via a configurable embeddings endpoint.
Provides a local-first web-intelligence layer for AI agents: search, fetch, crawl, extract, cache, find-similar and agent-style research without API keys or per-query billing, running as an MCP server, REST daemon, or SDK.
Automates end-to-end web workflows from browser screenshots by emitting pixel-grounded actions (click, type, scroll, visit, search). Vision-first multimodal agent fine-tuned from Qwen3.5-27B with critical-point safety checks; intended for sandboxed, human-supervised deployments.
Evaluates retrievers and search agents on synthetic multi-hop questions that require assembling a complete set of supporting evidence. Provides English and Russian variants (395 questions each), a fixed dense index embedded with Qwen3-Embedding-8B, and BrowseComp-Plus evaluation integrations.
An open-weight LLM checkpoint post-trained for agentic deep search: Qwen-compatible reasoning and tool-call formats optimized for web browsing, multi-source evidence aggregation, long-horizon planning and recovery from failed interactions; typically paired with the AxisAgentic harness.
An open-weight, Qwen-derived thinking model optimized for agentic deep web search and long-horizon planning. Provides Qwen-compatible reasoning and tool-call formats for English/Chinese browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions.
Provides 7,000 bilingual multi-turn, search-oriented tool-use trajectories (5,000 English, 2,000 Chinese) for supervised fine-tuning and analysis of agentic search models. Includes serialized system/user/assistant messages, embedded Qwen3 tool schemas, and conversion scripts; not a standalone benchmark.
Evaluates multimodal context learning across grounding, new information application, and knowledge acquisition using a 3,443-instance benchmark spanning science, finance, long documents, spatial reasoning, and web VQA; finds current multimodal models perform poorly (best score 0.2847) and analyzes failure modes.
Presents two LLM-based search agents (Iris-mini and Iris-pro) trained by alternating supervised fine-tuning and reinforcement learning against live web search. Key features: web-graph-derived multi-hop tasks with entity abstraction, SFT–RL climbing, inference-time context management, and state-of-the-art open-source benchmark results.
Benchmarks joint recovery of obfuscated platform messages and evidence-grounded web investigations by pairing 3,600 synthetic restoration inputs with 600 controlled web environments. Separately scores message-entry recovery and downstream web risk judgments, and provides a frozen multimodal evidence judge plus a resettable local sandbox for reproducible agent evaluation.
Co-evolves candidate solutions and web-search queries to help LLM-driven evolutionary discovery, using a retrieval gate plus bilevel inner/outer loops that refine queries, rank documents by predicted solution value, and generate evaluated candidates.