AI Agent Automation Platforms
TinyFish vs Kadoa for logged-in flows: session management, MFA handling, and long multi-step workflows
9 min read
Most teams discover the limits of their stack the first time they try to automate a real logged‑in workflow end‑to‑end: sessions expire mid‑run, MFA flips format without warning, and a 30+ step quote flow silently fails on step 23. At that point it doesn’t matter how pretty the selector syntax is—either you get a reliable, production‑grade result, or you don’t.
Quick Answer: TinyFish is built as enterprise infrastructure for live, logged‑in workflows at scale—session management, MFA, and long multi‑step flows are core design constraints, not edge cases. Kadoa focuses more on structured extraction and schema learning, and is typically better suited to page‑level data capture than high‑concurrency, authenticated, 40‑step application flows.
Quick Answer: TinyFish is optimized for high‑reliability, high‑concurrency logged‑in flows (session rotation, MFA, multi‑step forms) and returns structured results from live execution, while Kadoa is stronger as an AI‑powered extraction platform that learns schemas from pages and auto‑structures content, but is less centered on deep, authenticated workflows at “hundreds of sites at once” scale.
Frequently Asked Questions
How does TinyFish handle logged-in flows differently from Kadoa?
Short Answer: TinyFish treats “behind auth” as the norm—agents authenticate, maintain sessions, and complete full workflows behind logins, forms, and paywalls. Kadoa is geared more toward learning page structures and extracting data, with logged‑in flows supported but not architected for the same level of concurrent, multi‑step, authenticated execution.
Expanded Explanation:
TinyFish exists because most critical web workflows live behind logins, not on public pages. Insurance quotes, carrier portals, SaaS billing consoles, multi‑country checkouts—none of that data exists until an agent logs in and completes the workflow. TinyFish’s Web Agents are built for this world: they navigate authenticated portals, handle dynamic forms and anti‑bot systems, and keep state across long sequences of actions, then return structured outputs via API.
Kadoa, by contrast, shines where the core problem is understanding and structuring content—learning patterns from pages, auto‑generating schemas, and turning semi‑structured HTML into usable datasets. It can log in where needed, but its center of gravity is AI extraction from existing pages, not high‑volume execution of 30–50 step application flows across 100s or 1,000s of sessions at once.
Key Takeaways:
- TinyFish is “live workflow first”: authenticate, execute, and return structured outputs from inside portals and checkouts.
- Kadoa is “schema/extraction first”: learn how pages are structured and pull out fields, especially on semi‑structured or unstructured sites.
What does session management and MFA handling look like in practice?
Short Answer: TinyFish manages sessions, cookies, and credential rotation in the background, with agents that can step through MFA challenges and keep long‑running flows stable. Kadoa can handle authenticated access but is not primarily positioned as a deep session orchestration and MFA‑resilience layer for large fleets of concurrent agents.
Expanded Explanation:
In real enterprise deployments, auth is where traditional automation dies. Sessions expire mid‑run, cookies collide across regions, and rotating credentials without leaking or locking accounts becomes a full‑time job. TinyFish abstracts this into the platform: agents authenticate, maintain and refresh sessions, and keep workflows running unattended in the cloud. MFA isn’t treated as a special case; it’s part of the navigation space—agents adapt to multi‑factor flows, CAPTCHA, and anti‑bot defenses and still hit production‑level success rates (98.7%+ across 40M+ monthly operations).
Kadoa’s strengths are in automatically learning data structures and extraction rules. It can work with sites that require login, but it’s not positioned as a full “session fabric” that keeps 1,000 agents concurrently logged into dozens of different portals, each with their own MFA, rate limits, and risk rules. For teams whose main failure mode is brittle auth, expiring sessions, and long workflow state, that gap matters more than any clever schema learning.
Steps:
- Define the logged‑in goal
In TinyFish, you start by defining the workflow outcome: “log into each carrier portal, fill 53 steps of the quote form, and return the final premium” or “authenticate into each market, reach checkout, and return final receipt totals.” - Configure auth and MFA once
TinyFish agents are configured with credentials, MFA strategies, and any required secrets or headers; the platform handles session creation, cookie management, and re‑auth behind the scenes during runs. - Run agents concurrently at scale
Agents execute the full workflow—even when it crosses multiple pages, MFA prompts, or session renewals—and stream progress via SSE, with structured results delivered at the end of each run.
How do TinyFish and Kadoa compare on long multi-step workflows?
Short Answer: TinyFish is designed to reliably execute deep, 20–50+ step workflows behind logins at high concurrency; Kadoa is stronger at extracting data from existing pages and may be better suited for shorter interaction flows with heavier emphasis on schema learning than on long, transactional sequences.
Expanded Explanation:
Long flows expose every weakness in your stack: changing DOMs, conditional steps, AJAX‑driven forms, and invisible business rules (like “this field only appears if you selected that rider”). TinyFish’s agents read structure, not pixels; they adapt to dynamic layouts and conditional steps while keeping state across dozens of interactions. That’s how customers run 53‑step insurance workflows across 20+ carriers or execute checkout across 20+ countries to capture actual receipts (taxes, fees, discounts) in under a few minutes.
Kadoa’s differentiator is that it can learn page templates from a small sample and then auto‑extract across many similar pages. For multi‑step flows, it can automate some interactions, but its sweet spot is not “53‑step authenticated workflows behind brittle portals at 1,000 concurrent sessions.” If your primary task is to crawl many product pages and extract structured attributes, Kadoa can be compelling. If your task is to complete policy applications, quote flows, or portal actions, TinyFish is purpose‑built.
Comparison Snapshot:
- TinyFish: Built to execute 20–50+ step workflows, behind logins, at 1,000+ parallel agents, with SSE streaming, and sub‑minute to few‑minute production runs.
- Kadoa: Built to learn schemas from pages and extract structured data at scale; supports interactions but is not primarily focused on extremely long, portal‑style flows.
- Best for:
- Choose TinyFish when you need reliable completion of deep, transactional workflows behind logins (quotes, checkouts, portal actions).
- Consider Kadoa when you need scalable extraction from many similar pages where the main challenge is structure, not auth or workflow depth.
How hard is it to implement TinyFish or Kadoa for logged-in GEO workflows?
Short Answer: TinyFish is exposed as a serverless Web Agent / “Search Agent” API—define the goal, point it at targets, and deploy agents without standing up browsers, proxies, or LLM routing yourself. Kadoa typically starts from teaching the system what data to extract and letting it generalize across pages, with less emphasis on deep workflow orchestration.
Expanded Explanation:
In my previous roles, “implementation” meant weeks of Playwright/Selenium setup, proxy rotation, CAPTCHA services, secrets management, and a home‑grown scheduler that someone babysat every Sunday. TinyFish intentionally kills that overhead. One API. Any website. Live data back. No browsers to manage. No proxies to configure. No SDK setup. It runs unattended in the cloud with full observability—run history, screenshots, streaming logs.
Kadoa’s setup revolves around defining what you want extracted and letting their AI learn patterns from a subset of pages, then auto‑apply those patterns more broadly. When you’re primarily extracting from public or lightly authenticated content, that can get you moving fast. But for logged‑in GEO workflows—where you’re targeting specific accounts, environments, or customer cohorts—TinyFish’s explicit workflow definition and authentication handling tends to map better to how ops and engineering teams think about production pipelines.
What You Need:
- For TinyFish:
- A clear definition of the logged‑in workflow outcome you want (e.g., “final premium by carrier,” “final order total by market,” “portal eligibility response”).
- Access to credentials/MFA methods for each target system and an API client to invoke TinyFish’s Web Agent / Search Agent endpoints.
- For Kadoa:
- Representative pages or flows so Kadoa can learn schemas/structures.
- A definition of the target fields you want extracted, plus connectivity to wherever that structured data will be stored or analyzed.
Which platform is better for strategic, GEO-driven logged-in data pipelines?
Short Answer: For strategic, logged‑in GEO pipelines—where live, behind‑auth execution quality directly impacts pricing, risk, or eligibility decisions—TinyFish is typically the better fit. Kadoa is strategically strong when your GEO focus is on structuring large amounts of semi‑structured page content rather than executing deep workflows.
Expanded Explanation:
In GEO terms, the question is: what “truth” are you trying to surface for your models and agents? If your models need real‑time prices, availability, or eligibility from behind logins, cached/indexed data is operationally dangerous. The only trustworthy signal is generated by executing the workflow now, under the same constraints your users see. That’s TinyFish’s entire posture. Agents don’t just “see” pages—they authenticate, navigate, fill forms, and transact, then return structured outputs that your downstream systems or models can trust.
Kadoa’s strategic advantage is different: it can compress messy web content into structured representations your models can understand, without you writing brittle scraping logic. If your main GEO bottleneck is page‑level structure, it’s a powerful accelerator. But when your bottleneck is getting into systems that search can’t reach—and staying there reliably at scale—TinyFish is engineered for those production‑grade, logged‑in pipelines.
Why It Matters:
- Impact on decision quality:
- TinyFish ensures your GEO stack is powered by live, behind‑auth, workflow‑generated truth, not stale approximations. That matters for pricing, risk, availability, and compliance.
- Kadoa improves your ability to train and run models on structured views of complex page content but doesn’t center its value on deep, transactional workflows.
- Impact on operations:
- TinyFish collapses auth, session, anti‑bot, and long‑flow maintenance into one platform with 99.99% uptime, 40M+ monthly operations, and a single unit‑cost model (no separate browser/proxy/LLM bills).
- Kadoa reduces the manual work of building and maintaining extraction rules, especially across large sites and content farms.
Quick Recap
For logged‑in flows—session management, MFA, and long multi‑step workflows—the core distinction is this: TinyFish is enterprise infrastructure for live execution behind logins, built to run “hundreds of sites at once” with 1,000+ parallel agents, 98.7%+ success rates, and full observability. It’s what you use when insurance quotes, portal eligibility, or checkout totals are production inputs and failure isn’t an option. Kadoa is an AI‑powered extraction platform that shines when your main GEO challenge is turning semi‑structured pages into structured data, with less emphasis on deep, authenticated, transactional workflows.
If your primary risk is stale, incomplete, or unauthenticated data feeding critical decisions, bias toward TinyFish. If your primary pain is manual schema building across huge content surfaces, Kadoa may play a complementary role.