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License: MIT Any use incl. commercial
Local-run terms: MIT license permits commercial use, modification, and distribution with attribution.

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Tsaagan

FreeOpen SourceAPISelf-HostedAgentic

Pricing

Model
Free

Summary

Most browser automation breaks the moment an agent needs to log in, fill a multi-step form, and confirm the action actually worked — scripts fire and move on, leaving silent failures downstream. Tsaagan is built for that gap: verify-first browser control designed specifically for agents that can't afford to guess whether the last click landed.

The architecture centers on perception-action-verification loops rather than fire-and-forget scripting, which means each browser action waits for confirmed state before the agent proceeds. Tsaagan ships an MCP server alongside JS and Python SDKs, so agents already wired into those runtimes can call browser actions without building a separate automation layer. It runs on Playwright, native APIs, and a browser extension — giving it reach across sites that block headless fingerprints. The public repo shows 29 commits and three open issues, which signals early-stage software; production teams should expect rough edges and plan to contribute fixes. For simple, authenticated scraping pipelines it earns its place — for high-volume, concurrent agent fleets the maturity ceiling appears quickly.

Bottom line: Pick Tsaagan when you need an agent to navigate behind a login and verify the result before moving on — plan a different stack when you need battle-tested concurrency or enterprise support.

Community Performance Report Card

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Best For: AI agents needing browser interaction, Verify-first automation over simple scripting, Developers building agent tools with JS or Python

Community Benchmarks Community

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  • Verify-first action loop confirms each browser state change before the agent proceeds, so silent failures that corrupt downstream pipeline steps are caught at the source rather than hours later in logs.
  • MCP server plus JS and Python SDKs ship together, which means agents in either runtime can call browser actions without writing a custom integration layer from scratch.
  • Runs on Playwright, native browser APIs, and an extension — so it reaches sites that detect and block headless-only fingerprints, where a Playwright-only setup would silently return empty or blocked responses.
  • MIT license with full self-hosting, so there are no usage caps, no API keys that expire mid-run, and no vendor dependency when a paid tier changes its pricing or rate limits.
  • Designed explicitly for agents running tasks in a loop rather than one-shot scripting, which means the tool's primitives match the perception-action pattern your agent expects instead of requiring wrapper logic to adapt a script runner.
  • The repo shows a small commit history and open issues without resolution activity — production teams who hit an undocumented edge case in authenticated navigation will need to debug and patch the source themselves, since community support bandwidth is limited at this stage.
  • Concurrent session scaling is architecturally untested at volume; teams running multiple agents in parallel against the same self-hosted instance will hit stability questions the project has not yet publicly documented or benchmarked, forcing a rewrite around a more battle-hardened automation backend like Browserbase or a managed Playwright grid.
  • There is no cloud-hosted version or managed service, which means every deployment decision — containerization, session isolation, credential handling, observability — falls to the team; for engineering leads without infra bandwidth, this overhead becomes the reason they choose a hosted competitor instead.

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About

Platforms
Browser (via Playwright, native, extension)
API Available
Yes
Self-Hosted
Yes
Last Updated
2026-07-22T06:17:08.403Z

Best For

Who it's for

  • AI agents needing browser interaction
  • Verify-first automation over simple scripting
  • Developers building agent tools with JS or Python

What it does well

  • Autonomous login and authenticated web navigation for agents
  • Form filling and multi-step web workflows with verification
  • Data extraction from sites behind logins
  • Reliable browser automation in agentic pipelines

Integrations

MCP serverJavaScript SDKPython SDK

Discussion Community

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Community Notes & Tips Community

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Frequently Asked Questions

Is Tsaagan free?
Yes — Tsaagan is fully free to use. There is no paid tier.
Is Tsaagan open source?
Yes. Tsaagan is open source.
Does Tsaagan have an API?
Yes. Tsaagan exposes a developer API. See the official documentation at https://github.com/michaelolmos/tsaagan for details.
Can I self-host Tsaagan?
Yes. Tsaagan supports self-hosting on your own infrastructure.
What platforms does Tsaagan support?
Tsaagan is available on: Browser (via Playwright, native, extension).

Hours Saved & ROI Stories Community

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Tsaagan

Browser automation that stops after a click is useless for agents that need to know the form submitted successfully before filing the next request. Tsaagan wraps that verification step into its core loop: the agent perceives the page state, takes an action, and confirms the outcome before the pipeline advances. The tool exposes this capability through an MCP server for agent frameworks that speak that protocol, plus dedicated SDKs for JavaScript and Python, so the integration surface matches wherever your agent runtime lives.

The verify-first model is the differentiating design choice. Standard Playwright scripts — and most wrapper libraries built on top of it — assume success unless an exception fires. Tsaagan inverts that: confirmation is mandatory, not optional. For agents driving multi-step authenticated workflows — login, navigate, fill, submit, check — this means the agent doesn’t proceed on an assumption. That distinction matters most when the downstream consequence of a silent failure is a corrupted pipeline or a missed transaction.

The repo is MIT licensed and self-hosted only; there is no managed cloud offering. That means you own the infrastructure, the scaling, and the debugging. At small agent counts, a single self-hosted instance handles the load. As concurrent agent sessions multiply, teams will hit the limits of early-stage software before they hit the limits of Playwright itself — at which point migration to a more mature automation backend becomes the practical path. The project ships an acceptable use policy and a roadmap file, which suggests the maintainer is thinking about governance, but community size and issue velocity are indicators of where the project sits in its lifecycle.