Open-source browser for AI agents
Hi HN, I forked chromium and built agent-browser-protocol (ABP) after noticing that most browser-agent failures aren’t really about the model misunderstanding the page. Instead, the problem is that the model is reasoning from a stale state.ABP is designed to keep the acting agent synchronized with the browser at every step. After each action (click, type, etc), it freezes JavaScript execution and rendering, then captures the resulting state. It also compiles the notable events that occurred during that action loop, such as navigation, file pickers, permission prompts, alerts, and downloads, and sends that along with a screenshot of the frozen page state back to the agent.The result is that browser interaction starts to feel more like a multimodal chat loop. The agent takes an action, gets back a fresh visual state and a structured summary of what happened, then decides what to do next from there. That fits much better with how LLMs already work.A few common browser-use failures ABP helps eliminate: * A modal appears after the last Playwright screenshot and blocks the input the agent was about to use * Dynamic filters cause the page to reflow between steps * An autocomplete dropdown opens and covers the element the agent intended to click * alert() / confirm() interrupts the flow * Downloads are triggered, but the agent has no reliable way to know when they’ve completedAs proof, ABP with opus 4.6 as the driver scores 90.5% on the Online Mind2Web benchmark. I think modern LLMs already understand websites, they just need a better tool to interact with them. Happy to answer questions about the architecture, forking chrome or anything else in the comments below.Try it out: `claude mcp add browser -- npx -y agent-browser-protocol --mcp` (Codex/OpenCode instructions in the docs)Demo video: https://www.loom.com/share/387f6349196f417d8b4b16a5452c3369
FL score
out of 100
Verdict
high confidence
Competition
12
competitors found, emerging market, funded players
Trend
8 community mentions
An open-source browser protocol that solves AI agent reliability issues by synchronizing browser state, backed by strong benchmark performance, but faces a crowded market and high build complexity for a solo builder.
The pain
The gap
Build angle
Strengths
- Addresses a real, severe, and specific technical pain point for AI agent developers.
- Solution is validated by a high benchmark score (90.5% on Mind2Web), demonstrating effectiveness.
- Clear, defensible technical innovation with a novel approach to browser-agent synchronization.
- Target market (AI agent developers) is growing rapidly and has purchasing power.
- Open-source nature provides excellent distribution and community potential.
Risks
- High technical complexity and ongoing maintenance burden for a solo builder due to forking Chromium.
- Crowded market with several funded competitors, making differentiation and market penetration challenging.
- Monetization strategy for an open-source protocol requires careful validation beyond the initial technical offering.
- Risk of being commoditized or integrated by larger platforms if not swiftly productized.
Next steps
- Conduct in-depth interviews with current users of browser automation tools for AI agents (Playwright, Puppeteer) to confirm specific pain points and willingness to pay for a hosted, more reliable solution.
- Explore potential business models beyond pure open source (e.g., hosted API, enterprise support, premium features) and validate pricing structures.
- Gather user testimonials and case studies from early adopters to build social proof and articulate clear ROI.
- Assess the effort required to maintain and update the Chromium fork, considering future browser updates and security patches.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 25
- Solution gap
- 18
- Willingness to pay
- 20
- Buildability
- 13
The idea addresses a real, specific, and severe pain point for AI agent developers. While the technical solution is novel and highly desired, the market is crowded with funded competitors, posing a significant challenge for a solo builder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea targets a rapidly growing market with a strong value proposition, but faces significant technical complexity and competition.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly technical, niche solution with strong problem clarity, but significant complexity for a solo builder to maintain and monetize effectively.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A well-defined value proposition for a specific, reachable audience, but the open-source nature requires a clear monetization strategy to be a viable micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Strong demand and a clear solution for a critical pain point in a rapidly growing field, with a well-defined narrow wedge.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct in-depth interviews with current users of browser automation tools for AI agents (Playwright, Puppeteer) to confirm specific pain points and willingness to pay for a hosted, more reliable solution.
- Explore potential business models beyond pure open source (e.g., hosted API, enterprise support, premium features) and validate pricing structures.
- Gather user testimonials and case studies from early adopters to build social proof and articulate clear ROI.
- Assess the effort required to maintain and update the Chromium fork, considering future browser updates and security patches.
Who is already there, emerging market
An open-source framework that converts website interfaces into structured text, allowing AI agents to interact with the web more reliably and efficiently.
Pricing: Free tier available. Paid plans include: AI Agent Tasks ($0.01 init + per-step, with per-step costs varying by LLM model, e.g., Browser Use 2.0 at $0.006/step), Browser Sessions ($0.06/hour), Skill Creation ($2.00/skill), Skill Execution ($0.02/call), Proxy Data ($10/GB). Business/Scaleup plans offer discounts.
Provides serverless cloud browser infrastructure for AI agents and automation, supporting Playwright, Puppeteer, and Selenium at scale with features like stealth mode and session persistence.
Pricing: Not explicitly stated on the public page, but charges on a consumption basis.
An open-source CLI tool built in Rust that provides AI agents with direct browser control through command-line interface.
Pricing: Free and open-source.
- Steel.devVisit
An open-source headless browser API for AI agents focusing on providing infrastructure with transparency and self-hosted options.
Pricing: Free and open-source.
A platform that enables technical and non-technical users to build complex AI browser agents for reliable back-office task automation.
Pricing: Not explicitly stated on the public page.
A full Chromium-based browser with Perplexity's AI search engine built-in, offering autonomous navigation, form filling, and multi-step task completion.
Pricing: Free tier available. Agent mode requires a Plus subscription ($20/month).
OpenAI's AI-powered web browser that integrates browsing into the ChatGPT ecosystem, allowing agents to navigate and interact with websites.
Pricing: Requires a Plus subscription ($20/month) for Agent Mode.
- Do BrowserVisit
A Chrome extension that automates browser tasks using natural language commands, enabling web scraping, form filling, and multi-tab support.
Pricing: $25/month with a 7-day free trial. Free with your own Gemini API key or Claude subscription.
- BrowseragentVisit
Browser-based AI agents offering unlimited runs for a fixed cost, focusing on core automation, workflows, and AI power-ups.
Pricing: Free tier with 50 Cloud AI calls/month, 5 workflows, 10 nodes/workflow. Basic plan at $20/month with 5000 Cloud AI calls/month, 50 workflows, 50 nodes/workflow. Enterprise plan with custom pricing for unlimited features.
- MyNextBrowserVisit
Transforms existing browsers into AI assistants for tasks like summarization, research, form filling, and content creation, emphasizing privacy and local operation.
Pricing: Not explicitly stated on the public page.
- BrowserOSVisit
An open-source browser with built-in AI agents that emphasizes privacy and automation through natural language, acting as a privacy-first alternative.
Pricing: Free and open-source.
- FirecrawlVisit
A web data API for AI agents and developers that handles web scraping, search, and full browser interaction, providing clean structured data.
Pricing: Free tier with 500 credits (includes 5 hours of free browser usage). Paid plans start from $16/month.
What they charge
What people say, 8 mentions
building saas with ai agent browser control, how do i not end up hosting illegal shit by accident?
r/SaaS
Market validation: AI-powered job application automation tool
r/Entrepreneur
How does one build Browser Agents?
r/SaaS
Lessons learned after launching my first SaaS after 3 months of nights and weekends
r/Entrepreneur
Non negotiable rule for browser agents: human approval before submit, send, or payment
r/SaaS
Which opensource AI agent do you use for your business?
r/Entrepreneur
Exploring new product category: Embeddable Web Agents
r/Entrepreneur
Building an AI agentic extention for your browser!!
r/SaaS
Recent news
MyNextBrowser: Make any browser agentic and automate workflows
Product Hunt, March 14 2026
Browser Use raises $17M to help steer AI agents through the internet
SiliconANGLE, March 23 2025
AI Browser: Automate anything online with a single prompt.
Product Hunt, 4 months ago (approx. November 2025)
Browserfly: AI agent that lives in your browser
Product Hunt, Not specified, but launched on Product Hunt.
Browser Use Raises $17 Million to Accelerate Development of Its Web Agent Competing with Operator
ActuIA, April 19 2025
Market signals
The AI browser market is experiencing significant growth, projected to reach $76.8 billion by 2034, from $4.5 billion in 2024, with a CAGR of 32.8%. North America dominates this market due to early adoption of AI and substantial investments in R&D. The market is shifting from passive browsing to active, intelligent agents capable of understanding user intent and automating complex tasks, driven by advancements in foundational AI models.
What frustrates people
- AI agents often get lost in advertisements and CAPTCHAs, stall out, or take an inconveniently long time to do something.
- Browser automation is often brittle; agents fail when websites change their structure, making them unreliable in real-world scenarios.
- Security and privacy concerns are significant, as AI agents with browser access can expose sensitive data or be vulnerable to prompt injection attacks.
- The complexity of web interaction, including dynamic content and various web protocols, makes it difficult for AI agents to consistently interact with web pages.
- AI agents struggle to handle the sheer size of modern web pages, which can exceed the context window of even powerful LLMs, preventing effective reasoning.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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