Genspark Review 2026

Manus

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General AI agent that completes real tasks end to end

Manus is a general-purpose autonomous AI agent that goes beyond chat: give it an objective and it plans, browses the web, writes and runs code, manipulates files and delivers a finished artifact — a research report, a spreadsheet, a slide deck or a deployed web page. Each task runs inside its own cloud sandbox with a live view of what the agent is doing, so you can watch the browser tabs, terminal commands and files it creates in real time and step in when needed. Tasks continue running even after you close the tab, and Manus notifies you when the deliverable is ready. The platform is credit-based: simple tasks cost a few credits, long autonomous research runs cost more. Teams use Manus for competitive research, lead list building, data cleanup, market analysis and prototype generation — work that used to take an analyst a full day.

Perplexity AI

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The AI-powered answer engine, now with a $200/month Max tier

Perplexity combines a search engine with an AI chatbot, delivering concise, cited answers pulled from across the web rather than requiring you to click through multiple results. Its free tier is genuinely capable, not a crippled demo: unlimited standard searches, all six Focus modes (Web, Academic, Reddit, YouTube, News, Wolfram Alpha), voice search, Collections and Spaces with custom AI instructions, all included with no subscription. Pro, at $20/month (or $200/year, equivalent to $16.67/month), unlocks unlimited Pro Search, a much higher Deep Research allowance (commonly cited around 20 runs a day), the Labs report-builder, larger file uploads, AI image generation, and the ability to choose which frontier model answers a given question. Students and educators get Pro for $10/month through SheerID verification — half the standard price. A significant 2026 addition is Max at $200/month, aimed specifically at power users: it includes Perplexity Computer, which orchestrates 19 different AI models as specialized sub-agents to break down and execute complex projects, plus Model Council, which runs a query simultaneously across three frontier models (commonly GPT-5.4, Claude Opus 4.8 and Gemini 3.1 Pro) and synthesizes where they agree or diverge — genuinely useful for high-stakes decisions worth stress-testing from multiple angles. The Comet browser, once a premium feature, became free for all users worldwide as of October 2025. For most researchers and everyday users, the free tier or Pro at $20/month covers real needs; Max is a specific, expensive tool for power users who want multi-model orchestration and synthesis on demand.

AgentGPT

Browser-based autonomous agents — free hosted demo or self-hosted

AgentGPT was among the earliest browser-accessible autonomous agent tools, letting you set a goal in plain language and watch an AI agent break it down into sub-tasks and attempt to complete them with minimal ongoing supervision — no local installation required for the hosted demo version. It's built on the same autonomous-agent philosophy as tools like AutoGPT, but with a more accessible, browser-first entry point that made it a popular way for non-developers to first experience agentic AI. Its pricing structure is genuinely two-tiered in a way worth understanding clearly: the hosted demo offers limited free usage with no setup, while a separate self-hosted, open-source version follows a bring-your-own-infrastructure cost model — you pay only for VPS hosting (roughly $5-20/month) plus your own LLM API key usage, with no platform subscription fee at all for the self-hosted path. Managed/paid tiers on the hosted platform run around $40/month for higher usage limits and premium features. The honest limitation shared across early autonomous-agent tools like AgentGPT: they tend to consume more API tokens than more targeted platforms, since the autonomous, exploratory approach generates more LLM calls per completed task than a narrowly-scoped agent would. It remains a reasonable low-commitment way to experiment with autonomous agent behavior — set a goal, watch it work — before committing to a more structured, business-operations-focused platform like Lindy or a developer framework like CrewAI for production use.

Gamma

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70 million users and $100M ARR — the AI presentation category leader

Gamma has become the clear category leader in AI presentation generation, reaching 70 million users and $100 million in annual recurring revenue by January 2026 with a lean team of roughly 50 employees — a genuinely remarkable growth trajectory funded by just $19-20M in total Series A funding from Accel and Script Capital. It turns a text prompt into a polished, web-first presentation faster than any competitor tested in independent comparisons, with real-time collaboration and visually striking default output that reviewers consistently rate as the most impressive at first glance. A major March 2026 update — described by the company as its biggest launch ever — added Gamma Imagine (standalone graphic, logo and infographic generation from text prompts), Connectors (creating presentations directly inside Claude, ChatGPT, Make, Zapier and Atlassian without switching tabs), Smart Charts (interactive, auto-styled data visualizations), AI-native templates for remixing an existing deck for a new audience in one prompt, and a beta AI Agent that applies natural-language editing instructions like "make this shorter" or "remove all orange" across an entire deck in one action. Pricing starts free (with a genuinely useful tier, though credit-limited) and Plus runs $10/month (individual) or $8/seat/month with 1,000 monthly credits on Team plans — credits are consumed per card and image generated, so usage scales with how AI-heavy your deck-building actually is. Its clearest weakness relative to competitors is PowerPoint export fidelity, where Beautiful.ai and some newer challengers score higher; for startup pitches, web-first storytelling, and teams that live in Gamma's own format rather than needing pixel-perfect PowerPoint compatibility, it remains the category's clear leader in early-to-mid 2026.

Lindy

Prebuilt 'AI employees' for everyday business operations

Lindy's pitch is the fastest path from zero to a working AI agent handling real business operations: prebuilt "AI employees" for common tasks — email triage, meeting scheduling, CRM updates, customer support — that you can stand up in minutes via a drag-and-drop builder, rather than assembling an agent from scratch. It includes 4,000+ integrations and voice-agent capabilities alongside its core workflow automation, positioning it as more turnkey than Relevance AI's build-your-own-workforce approach. Its credit-based pricing meters every action, with cost scaling by complexity: a simple step costs roughly 1 credit, while email parsing or multi-step workflows burn 5-10 or more credits per run — worth understanding before assuming a flat monthly rate covers unlimited usage. Independent comparisons consistently place Lindy as offering better value than Zapier per dollar of task automation, largely because its pricing tiers include a meaningfully larger effective task allowance per plan level. With a free tier (limited agents and tasks) and paid plans starting around $50/month, Lindy sits above Relevance AI's $19/month entry point but below CrewAI's technical, developer-oriented positioning. It's the strongest fit specifically for non-technical teams wanting fast, ready-made automation of everyday operational tasks — email, scheduling, CRM hygiene — without the setup overhead of a code-first framework or the build-your-own-agent-team complexity of Relevance AI.

CrewAI

Open-source multi-agent orchestration, free framework plus a managed cloud

CrewAI is both a free, MIT-licensed open-source Python framework for orchestrating multiple collaborating AI agents and a managed hosted platform (CrewAI AMP) for teams that don't want to run it themselves — a dual identity that's core to understanding its actual cost. The open-source framework, with over 50,000 GitHub stars and a reported 2 billion agent executions in the trailing 12 months as of 2026, lets developers build "crews" of specialized agents (a researcher, a writer, a reviewer) that divide and coordinate complex multi-step tasks, and it's used by a majority of Fortune 500 companies according to the company's own disclosures. CrewAI's public cloud pricing has genuinely changed multiple times and is worth getting straight: a $25/month Professional tier (100 executions/month) existed from its October 2025 AMP launch through spring 2026, then was removed entirely. As of mid-2026, the public cloud pricing is two tiers only — Basic, free with 50 workflow executions/month and full core platform access (not a crippled trial), and custom-quoted Enterprise for organizations needing compliance certifications and dedicated support (estimated $60,000-$120,000 annually in third-party analysis). If you're reading an older article citing a $99/month or $25/month self-serve mid-tier, that pricing has been discontinued. The cost that catches most teams off guard isn't the platform fee at all — it's LLM API usage. Because CrewAI counts one "execution" as a full crew run regardless of how many agents are involved or how many tokens they consume internally, a crew with 10 agents making extensive LLM calls costs the same one execution as a single-agent crew, but the underlying API bill can be dramatically higher. Production costs for token-hungry multi-agent workflows commonly run $1.50-12/hour depending on task complexity — budget for that separately from whatever CrewAI's own platform tier costs.

Devin

Autonomous AI software engineer — now 96% cheaper than its original $500/month

Devin, built by Cognition AI, was introduced in March 2024 as "the world's first AI software engineer" — an agent that works more like a remote contractor than an in-editor assistant: hand it a well-scoped engineering ticket, and it plans the approach, works inside its own sandboxed cloud environment with shell, browser and editor access, writes and tests code, and opens a pull request for human review, all with minimal step-by-step supervision. That's the core distinction from tools like Cursor or Claude Code, which sit inside your editor and wait for you to drive each interactive step. The headline change worth knowing before evaluating Devin in 2026: Cognition slashed pricing by 96% in late 2025. The original $500/month plan — which became genuinely famous as a talking point in every competitor's marketing — still exists as the Team tier (250 Agent Compute Units included, additional ACUs at $2.00 each), but a new self-serve Core tier now starts at just $20/month plus pay-as-you-go ACUs at $2.25 each. One ACU represents roughly 15 minutes of active autonomous work, so an hour of Devin running on Core costs around $9 — meaningfully more accessible than the original enterprise-only positioning suggested. Cognition's own July 2026 benchmarks (vendor-reported, not independently verified) put Devin's current SWE-1.7 model at 77.8% on SWE-bench Multilingual and 81.5% on Terminal-Bench 2.1, and the company raised $1 billion at a $25 billion valuation in May 2026 with annualized revenue near $492 million — rapid growth that followed the price restructure. Devin fits well-scoped, repetitive engineering work best: large migrations, framework upgrades, CI failure triage, documentation for legacy code — tasks with enough time and ticket clarity to justify autonomous, unsupervised execution. For interactive, moment-to-moment coding, reviewers consistently note Cursor or Claude Code remain faster and cheaper.

Relevance AI

Build a coordinated 'AI workforce' of role-based agents

Relevance AI's core concept is an "AI workforce": rather than one general-purpose assistant handling every request, you build narrowly-scoped agents with distinct roles — a research agent, an SDR agent, an ops agent — that work together as a coordinated team, each using only the tools and information assigned to its specific job. That multi-agent orchestration approach makes it particularly strong for go-to-market teams building sales and revenue-operations workflows where several specialized tasks need to hand off to each other. A significant pricing restructure took effect in September 2025 and remains the current model: costs split into Actions (what an agent actually does — API calls, tool use) and Vendor Credits (the underlying LLM model costs), with no markup on Vendor Credits and the option on paid plans to bring your own API keys to bypass them entirely. The free tier includes 200 Actions/month plus $2 in bonus vendor credits — useful for initial testing, but genuinely difficult to forecast real monthly spend from, since usage compounds quickly once agents run continuously or handle complex, multi-step tasks. Paid plans run from roughly $19/month for individuals up to about $199/month for teams, positioning it as more affordable at the entry level than Lindy (~$50/month) but with real usage-based cost uncertainty that Lindy's flatter-feeling tiers avoid. It's best suited to teams that want a genuine multi-agent "workforce" model for sales or ops specifically, and are comfortable actively monitoring Actions and Vendor Credit consumption rather than a fully predictable flat bill.

Manus

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General AI agent that completes real tasks end to end

Manus is a general-purpose autonomous AI agent that goes beyond chat: give it an objective and it plans, browses the web, writes and runs code, manipulates files and delivers a finished artifact — a research report, a spreadsheet, a slide deck or a deployed web page. Each task runs inside its own cloud sandbox with a live view of what the agent is doing, so you can watch the browser tabs, terminal commands and files it creates in real time and step in when needed. Tasks continue running even after you close the tab, and Manus notifies you when the deliverable is ready. The platform is credit-based: simple tasks cost a few credits, long autonomous research runs cost more. Teams use Manus for competitive research, lead list building, data cleanup, market analysis and prototype generation — work that used to take an analyst a full day.