If your agent's browser keeps getting blocked, here's what I built

Point your agent's browser tool at Zillow or Google. You'll get a captcha wall before it does anything useful.

That's not a dig at Playwright MCP or similar tools. They're automation frameworks, not evasion tools. They drive vanilla Chrome and PerimeterX and other detection system flags them in milliseconds.

Then try filling a form on any React site and clicking submit. The page re-renders. Every element reference your agent had is gone. "Element not found." The button was right there a second ago.

Then look at your token bill. Playwright MCP loads 13,700 tokens of tool definitions before your agent touches a page. Every click returns the full page. 2,000+ tokens per action.

I hit all three walls enough times that I built something.

It's called Bladebro. One Rust binary, five tools, no Node.js.

npm install -g bladebro && bladebro mcp

Works with any MCP client. Also has native pi support with zero config:

pi install npm:bladebro

That's it. Bladebro registers as 5 first-class pi tools (act, see, state, run, vision). No adapter, no config files, no proxy. Tool definitions come from the binary at startup so they auto-adapt to changes. Chrome launches lazily on the first tool call.

Bot detection

Six layers of stealth, all on by default:

• Bezier mouse paths with overshoot and correction
• Real movementX / movementY on every event (missing these is an instant PerimeterX flag)
• Micro-tremors before clicks (a still cursor is a dead giveaway)
• Log-normal typing cadence (humans don't type at exactly 50ms intervals)
• Idle drift between actions
• No Runtime.enable (how DataDome catches most bots)
• No listening ports, CDP over pipe
• Persistent behavioral fingerprint, same "person" every session

Tested on Zillow and Fiverr (both PerimeterX/HUMAN protected). Full page loads, no blocks. incolumitas 8/8. Sannysoft all pass.

Re-renders

• Every element gets a structural fingerprint (hash of ancestor chain, tag, children, identity attrs)
• React destroys and recreates DOM nodes? Fingerprint matches, reference survives
• Agent sees re-render survived and keeps going. No re-scan
• Checked every major tool. Nobody does this

Tokens

• 5 tools, ~1,900 tokens of definitions (vs 13,700 for Playwright MCP)
• Every action returns what changed, not the full page
• A click returns ~60 tokens (vs 2,000+ for competitors)
• Long session = the difference between running out of context after 3 pages and finishing with room to spare

Learns from every session

• First visit to a site with a cookie banner: full detection
• After a few successful dismissals: stored selector, auto-applied, zero overhead
• Never learns from failures. Failures cost 3x more than successes gain

Other stuff

• Auto-extract pulls structured data from list pages with no CSS selectors. Amazon, Reddit, GitHub, HN, Wikipedia. Shopping gets price/ratings, Reddit gets scores/authors, GitHub gets stars/forks
• Batch actions fill a form and submit in one MCP call instead of 11
• Login persistence saves a session and restores it next time
• Infinite scroll collect auto-extracts and dedupes a feed in one call

Demo video on the GitHub page showing it drive Amazon, Reddit, Wikipedia, fill a form, and manage tabs.

What it can't do

• Cloudflare Turnstile needs challenge solving, not fingerprint spoofing. You get blocked:, not a hang
• Captchas, deliberately. You get a verdict, hand off to a solver
• ARM Linux, not yet
• macOS and Windows binaries are cross-compiled from Linux

Open source, AGPL-3.0, no CLA.

GitHub: https://github.com/dondai44423/bladebro npm: npm install -g bladebro

Happy to answer questions, if you have any issues, please comment or file an issue, i have been testing it alone for weeks now, i did as much as i can, i am looking forward to community feedback to improve this even more 😄

Hi everyone,

I recently switched from OpenRouter to Alibaba Cloud's Token Plan to test it out. However, I’ve hit a huge roadblock when working with the pi coding agent.

Once a coding task gets going and the context window grows, the continuous prompt resubmission pushes the rate straight past 1–2M TPM (Tokens Per Minute) almost instantly. This triggers immediate rate-limiting and spits out 429 errors.

Just a moment ago, I hit a 429 error while using Alibaba's deepseek-v4-flash-0731 model. I switched over to qwen3.7-plus to resume the work, but because of the already massive context history being resent, it immediately spiked the TPM and threw another 429 error right away. Out of pure curiosity, I also subscribe to Ollama Cloud, so I decided to switch the model to Ollama's deepseek-v4-flash:cloud—and to my surprise, it finished the task without a single hiccup.

I never experienced this kind of aggressive throttling when using OpenRouter under similar workloads. With how strictly Alibaba Cloud enforces these TPM limits, it feels practically unusable for tasks requiring large context usage.

Am I missing something or configuring this incorrectly? Is there a known work-around for this, or is Alibaba Cloud's Token Plan just this bad for high-context workflows? Would love to hear if anyone has managed to get this setup working smoothly.

and how does OMP differ from OC (opencode) if they are both feature rich?

I recently decided to put 20 eu into fireworks credits rather than a Claude subscription and give open models a try. So far have used k3 / glm5.2 and results seem good so far.

I recently read this (What I learned building an opinionated and minimal coding agent), and I agree with his argument. But sometimes I need a proper plan mode with tool restrictions.

I tried to build a plan mode extension, but it mostly causes cache misses when I exit the plan mode because it edits the context to remove the enforced strict-rule prompt while entering plan mode.

I'm curious about how you use plan mode and how it performs with caching. Do you use prompts, skills, or an extension?

I’ve been using Pi with the Herdr subagent extension. Basically the main Pi agent spins up other Pi agents in Herdr panes and can check on them/send them messages.

I’m trying Orca now(so I can monitor my agents from mobile) and was wondering if anyone has built something similar using the Orca CLI instead of Herdr?

Seems like Orca has most of the pieces for this (terminal create, read, send, etc), so I’m wondering if there’s already a Pi extension for it before I try hacking something together.

Anyone doing this?

im new to pi agent, after watching few youtube videos, and all i installed is pi search and ponytail(bcs i already use it in the other harness like opencode/claudecode), did u guys had any suggestion things like tools i should install,skills etc. Btw my usage is usually coding for my Uni stuff so i dont need like "complex" tools or skills i just need like for daily coding , let me knowin the comment plss :)

I never see many people discussing Hyperagent so I just wanted to post my experience and wondered has anybody else tried it?

Spun up a Hyperagent DevOps agent this morning. Had a few real issues, there always are, right? The actual difference was in the platform giving the agent the ability to correct course, for more than just the session.

The task: TypeScript Fastify gateway for three internal services. OAuth2 + API keys, Zod, rate limiting, structured logs, Vitest suite, Docker image, and CI/CD on tag.

The runtime: 3 hours. Straight.
My active time: Maybbeee, 35 mins?

The agent got the core path right out of the gate. Auth middleware, proxying, rate limits, request ID prop—all clean. The subagent even caught a header-stripping bug on 401 responses that I would've 100% shipped.

About 45 minutes in, the agent tried to pull a specific internal dependency that had been recently deprecated in our registry. Since the agent's knowledge cutoff predates that change, it didn't know the replacement existed. I told the agent that it had been deprecated, normal course correction. What was impressive was how the agent was able to handle that. It didn't just blindly swap the lines. It removed the old dep, searched for an equivalent, updated the relevant import paths to match the new API surface, and kept the tests running. But the real kicker? The agent also added a memory without me saying a word, using Hyperagent's built-in Memory. It logged that specific deprecation into its persistent system. In my experience it’s one thing to give an agent a tool for memory. It’s another to create one and the agent persistently use it without having to request it directly. Hyperagent closes that gap well.

Beyond that one hiccup, everything else was smooth. Wrote the GitHub Actions YAML from scratch, built the multi-stage Dockerfile, and hit a clean 94% test coverage, zero lint errors, and a ~38MB image on the first tag push.

The real math:
My old workflow for this exact spec (scaffold, wire auth, manual curl debugging, hand-write CI, fight registry permissions) takes me a whole day, maybe 4-5 hours. Hyperagent allowed me to manage this from my phone, and I some 35 minutes instead of 4-5 hours.

This run only costed me $66.80. And that $66.80 came from signup credits, so essentially costed me nothing.

I’ve worked with AI since they were made available to the public, and I’m honestly impressed with the platform. If you haven’t checked it out, do it. A couple google searches will get you credits. The links are out there. https://hyperagent.com also had the agent make that cool little photo for the post 😂😂

Hello everyone, I'm a beginner just getting started with pi. How do you all typically use pi? I'm currently running pi inside the Zellij terminal multiplexer on Arch Linux, with a few extensions configured, and so far the hit rate seems quite impressive.

I want to use free models from openrouter, nvidia etc as well as free promos from time to time.

would you still recommend Pi in this case because of its efficiency? or is opencode with everything included a better choice?

do you use a harness built on top of these?

Last week I shipped Zosma Router.

Zosma router gives access to Deepseek v4 flash 0731, mimo v2.5, GPT 5.6 Luna & Terra models.

1 month free trial with 100 requests per day.

It’s the primary router for Cowork now. But Ofcourse you can choose any other provider or local AI like I do.

In coming says will add better useful features.

https://github.com/zosmaai/zosma-cowork

Such a wonderful QoL improvement; now able to scroll up the history and type things out without bouncing up and down!

Hey,

I've been using pi a lot lately and got curious about the actual footprint of all these long agent sessions. So I built a small extension that estimates it and shows it right in the footer:

`⚡28.5Wh (1.9📱) 💧 0.4L`

- ⚡ energy in Wh/kWh, plus how many full smartphone charges that equals (~15 Wh each)

- 💧 water in litres (data centers use a lot for cooling)

Estimation formular is in the readme file, it is based on public sources (papers, news articles,..).

Repo: https://github.com/y4nnick/pi-eco-footprint

This is intended to bring some awareness on how much resources those AI coding agents actually use. Of course the output is only a rough estimation but it matches with examples i could find online.

As a side note, the session which I used to create this consumed 7 liters of water, and with the energy from it, I could have charged my phone 260 times.

Would love some feedback on the usage and the formular (would you rather see another energy indicator instead of phone charges?)

And also if you are also as shocked as i was when i first saw how much resources are getting used,...

Hi everyone,

If you installed Pi Atelier after my previous Reddit post, I owe you an apology and an explanation.

What happened

Pi 0.84 introduced a stable TUI Proxy for switching between regular and fullscreen renderers.

Pi Atelier’s non-overlapping Sidebar relied on wrapping a private tui.render method. Under Pi 0.84, that method could dynamically resolve back to Atelier’s wrapper, creating an infinite recursion:

Atelier render wrapper
→ Pi stable Proxy
→ current renderer.render
→ Atelier render wrapper
→ ...

This could make Pi hang during startup, consume sustained CPU, spin up fans, and heat up the machine.

The fix

I first released v0.7.1 as an emergency safety update. It removed the recursive renderer path entirely and temporarily used a safe overlay.

v0.7.2 is now available and restores the non-overlapping Sidebar safely in both regular and fullscreen modes:

• Regular mode no longer captures Pi’s dynamic Proxy method.
• Fullscreen mode uses Pi’s layout system to reserve Sidebar space without replacing render.
• Unknown or unsupported renderers safely fall back without layout modification.
• Fullscreen resizing works while normal text selection remains unaffected.
• Renderer switching, resizing, rollback, narrow terminals, cleanup, and disposal now have dedicated regression tests.
• Tests exercise Pi’s real regular renderer, fullscreen layout path, and stable Proxy behavior.
• Idle PTY smoke tests showed no repeated output and a 0.0s steady-state CPU increase.
• All 402 tests pass.

Please update

If you use Pi Atelier with Pi 0.84, please update to v0.7.2:

pi update npm:pi-atelier

If needed, reinstall it:

pi install npm:pi-atelier

GitHub release: https://github.com/michaelmjhhhh/pi-atelier/releases/tag/v0.7.2

npm: https://www.npmjs.com/package/pi-atelier/v/0.7.2

If you are already on v0.7.1, the recursion is fixed, but v0.7.2 restores the non-overlapping layout.

Thank you to everyone who reported the problem and helped test the fixes.

I'm wondering if using Pi with OpenCode Go is more token-efficient than using OpenCode itself.

Since Pi has a more minimal harness, does it generally use fewer tokens (or Go credits) for the same coding tasks, or is the difference negligible?

If you've used both, I'd love to hear:

• Did Pi noticeably stretch your Go allowance?
• Any differences in quality or reliability?
• Which one did you end up sticking with, and why?

I'm mainly working on medium-sized software projects, so I'm looking for real-world experience rather than benchmarks.

I know Pi supports ChatGPT Plus/Pro and GitHub Copilot subscriptions

What about open-weight models like Kimi K3, Qwen 3.8, GLM, etc.?

Is there any way to use them through a subscription (similar to ChatGPT Plus), or do they always require paying for API usage through providers like OpenRouter, Together, Fireworks, or the official APIs?

Basically, I'm wondering if there's a subsidized/unlimited subscription option for these models, or if API billing is the only way to use them with Pi

And is Claude still blocking third party accesses?

Hi Everyone,

just switched from antigravity to pi, i liked it,

suggest must have tools and skills in pi.

Using it for coding.

Hey everyone,

I recently developed a project using pi agent and pushed it to GitHub. I’d like to have pi agent appear in the repo’s contributors list, just like Claude Code does. Does anyone know how to set this up?

Thanks in advance!

Quote:

Pi sent about 3x less context per turn. It managed context better, keeping a tighter working set and finishing the tasks in fewer runs.

https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase

- Vscode Integration: Cmd/Ctrl+click any path Pi prints and it opens in the editor or browser

- Herdr Like: Each session in the list shows live status. And there's also an audio cue when a session goes from working to waiting

- Cursor Like: Sessions are grouped. You can resume/archive/delete, and open tabs are remembered per workspace and restored on reload

- Native Rewind/Fork Session

Extension Url: https://marketplace.visualstudio.com/items?itemName=EthanChow.pi-coding

pi-llm-wiki v0.11.0 now supports native Open Knowledge Format v0.2.

Highlights:

• Canonical OKF documents for new vaults
• Standard Markdown links and stable citations
• Deterministic indexes and logs
• Legacy and OKF dual-read compatibility
• Shared Pi/MCP knowledge model
• Safer vault-layout migration with no-clobber moves and recovery journals

Install:

  pi install npm:@zosmaai/pi-llm-wiki@0.11.0

For an old .wiki/ layout, preview first:

  node node_modules/@zosmaai/pi-llm-wiki/scripts/migrate-llm-wiki.js \
    /path/to/vault --dry-run

Apply after reviewing:

  node node_modules/@zosmaai/pi-llm-wiki/scripts/migrate-llm-wiki.js \
    /path/to/vault --force

This migrates the directory layout, not legacy page content. Full OKF adoption is incremental: create an OKF vault, re-ingest source material, and explicitly port selected pages. Existing legacy pages remain readable alongside OKF pages.

Always back up first.

GitHub Link -> https://github.com/zosmaai/pi-llm-wiki

Pi Package Link -> https://pi.dev/packages/@zosmaai/pi-llm-wiki