Assistly vs HuggingFace Gaia 2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Assistly and HuggingFace Gaia 2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Assistly
Assistly
A live meeting assistant for Mac and Windows that reads call audio locally and shows guidance in an overlay excluded from screen shares.
Key features
- Bot-Free System Audio Capture: Works from your computer's audio rather than joining the meeting, so nothing appears in the participant list and there is nothing to integrate with the call app.
- Screen-Capture-Excluded Overlay: The assistant window is excluded from screen capture at the OS level, so it stays visible to you and invisible in shares and recordings.
- Auto-Assist Without Prompting: Detects when a question lands or when you think out loud and streams structured talking points into your thread automatically, with no hotkey and no break in eye contact.
- Multi-Speaker Language Tracking: Separates your voice from other participants and follows who said what across dozens of auto-detected languages, even when the call switches language mid-sentence.
- Two-Way MCP Context: Pulls context from Google Calendar, Notion, Linear or any MCP server during the call, and exposes your meeting history back over MCP so Claude, ChatGPT or Cursor can query it later.
- Personas from Your Material: Builds a persona from your CV, docs and notes and switches modes for a sales call, client review or interview so responses match your background and phrasing.
- Automatic Recap and Action Items: Turns the transcript into a summary with owners and deadlines the moment the call ends, auto-saved and searchable across sessions.
- Per-Client Projects: Files each session to a project based on the calendar, and scopes answers and mid-call lookups to that client's history so context never crosses between accounts.
Best for
- Live Sales Calls: Surfacing objection handling and product detail the instant a prospect asks, without breaking eye contact to search a doc.
- Client Account Reviews: Recalling what was committed to a specific client in a previous session, with the source call cited, while the review is still running.
- Non-Native Language Meetings: Following a call that switches language mid-sentence and receiving guidance in clear English.
- Customer Success Handoffs: Leaving every call with a written summary and assigned action items instead of reconstructing notes afterwards.
- Meetings Where Bots Are Unwelcome: Getting live assistance on calls with clients or legal teams who object to a recording bot joining the room.
- Querying Past Meetings from Your Editor: Asking Claude, ChatGPT or Cursor what was agreed in a past session over MCP without opening the app.
HuggingFace Gaia 2
Hugging Face
Gaia2 is an open benchmark and evaluation suite of 800 dynamic scenarios for studying and comparing generalist agent capabilities.
Key features
- Large-scale Dynamic Scenarios: A packaged corpus of 800 curated scenarios across multiple universes that exercise long-horizon, multi-step tasks requiring tool use, reasoning, and multimodal inputs.
- Capability Configurations: Supports targeted evaluations across capabilities such as execution, search, adaptability, time-awareness, and ambiguity handling to isolate strengths and weaknesses of agents.
- Multi-Phase Evaluation Pipeline: Executes three evaluation phases — standard, Agent2Agent, and noise — enabling comparisons under clean, interactive, and perturbed conditions.
- Variance and Robustness Analysis: Enforces multiple runs (e.g., 3 runs per scenario) and aggregated metrics to measure variance, stability, and robustness of agent behavior.
- ARE CLI/SDK Integration: Native integration with the ARE toolkit (are-run, are-benchmark gaia2-run) for local testing, batch evaluation, and reproducible experiment orchestration.
- Leaderboard-Ready Trace Generation: Produces submission-ready trace artifacts and automated evaluation hooks for uploading to the Hugging Face GAIA leaderboard.
- Model Provider Flexibility: Works with multiple model backends (via LiteLLM and other integrations) so researchers can plug diverse LLMs and tool stacks into the evaluation pipeline.
- Gated-but-Accessible Dataset Governance: Publicly hosted on Hugging Face with controlled access agreement to avoid data contamination and ensure fair benchmark usage.
- Comprehensive benchmark of 800 dynamic scenarios spanning 10 universes
- ARE CLI tooling: are-run, are-benchmark, and gaia2-run commands for scenario execution and evaluation
- Three evaluation phases: standard, Agent2Agent, and noise, with 3 runs per scenario for variance analysis
- Integration with Hugging Face Hub: dataset hosting, Hugging Face Spaces demo, and leaderboard submission
- Submission-ready trace generation with oracle events and ground-truth for automated evaluation
- Configurable capability splits (e.g., execution, search, adaptability, time, ambiguity) and dataset splits (validation)
- Supports multiple model providers via LiteLLM integration and Hugging Face model ecosystem
- Scenario browser UI in ARE environment and ability to load Gaia2 directly from the Hugging Face Datasets tab
- Requires Hugging Face authentication (huggingface-cli login) to access dataset and submit results
- Open-source reference implementations, demos, and documentation (blog post, paper, GitHub ARE repo)
Best for
- Benchmarking Generalist Agents: Compare LLM-based agent systems on long-horizon, tool-using tasks to measure execution, search, and adaptability capabilities against a community leaderboard.
- Researching Robustness and Variance: Run repeated scenario trials with noise and Agent2Agent phases to study stability, failure modes, and sensitivity to perturbations in agent policies.
- Tool and Pipeline Validation: Validate integrations between LLMs and external tools (code execution, web search, file handling) by executing Gaia2 scenarios that require real tool calls.
- Agent Architecture Comparison: Evaluate different agent designs (planner-actor, chain-of-thought, tool-routing) on identical scenario sets to quantify architectural trade-offs.
- Coursework and Benchmarks for Education: Use Gaia2 in practical assignments and projects (e.g., Hugging Face agents course) to teach agents engineering and evaluation best practices.
- Leaderboard-driven Iteration: Continuously improve and submit agent traces to the Hugging Face GAIA leaderboard to track progress and compare against community baselines.
- Agent-Agent Interaction Studies: Use the Agent2Agent evaluation phase to study emergent behaviors, cooperation, or adversarial interactions between autonomous agents.
- Benchmarking and comparing generalist agent architectures on multi-domain tasks
- Academic and industrial research into agent capabilities, robustness, and multi-run variance
- Developing and validating agent tool integrations (code execution, search, multi-modal inputs)
- Continuous evaluation and leaderboard submission for agent development pipelines
- Interactive exploration of scenarios via Hugging Face Spaces for demo and debugging