Common Questions
General
What is Unbrowse?
Unbrowse is infrastructure for AI agents that enables them to interact with any website at the network level — 100x faster and more reliably than traditional browser automation.
Think of it as "Google for agent actions" — we index how to do things on websites, not just information about them.
How is this different from browser automation tools?
Traditional automation (Selenium, Puppeteer):
Slow (30-45 seconds per action)
Unreliable (70-85% success rate)
Requires screenshots and visual parsing
Breaks when UIs change
Expensive compute costs
Unbrowse (network-level):
Fast (<1 second per action)
Reliable (95%+ success rate)
Direct HTTP communication
Stable (works even when UIs change)
Minimal infrastructure costs
Who is Unbrowse for?
Three main groups:
Indexers - Anyone who browses the web
Earn passive income from browsing normally
No technical skills required
Developers - Building AI agents
Get universal web access via API
No need to build custom integrations
Token Holders - Investors
Benefit from usage-based burns
Value grows with platform adoption
Is this legal?
Yes. Unbrowse operates within legal boundaries:
Uses user-provided credentials (authorized access)
Respects robots.txt and site policies
Similar to browser automation (established legal precedent)
Website owners earn 40% of fees (incentive to allow)
Key legal precedents:
hiQ Labs v. LinkedIn (web scraping with public data is legal)
Users can automate their own accounts
Network-level access not prohibited by law
How do you handle websites that block automation?
Multiple strategies:
TLS/HTTP/2 fingerprinting
Match real browser signatures
Indistinguishable from human browsing
Cloudflare bypass
FlareSolverr integration
Challenge solving
User credentials
Agents use real user accounts
Legitimate authenticated access
Website owner cooperation
40% revenue share incentivizes allowing access
Better than blocked scrapers
For Indexers
How much can I earn?
Depends on:
Quality of abilities (enterprise > consumer)
Usage volume (growing with AI adoption)
First-mover advantage (early = more)
Realistic ranges:
Casual (10 abilities, consumer focus):
$5-50/month initially
$100-500/month at scale
Serious (100 abilities, mix of tiers):
$50-200/month initially
$1,000-5,000/month at scale
Power (500+ abilities, enterprise focus):
$200-1,000/month initially
$10,000-50,000/month at scale
Plus FDRY appreciation from burns!
Is my browsing data private?
Yes. Zero-knowledge architecture:
Passwords encrypted client-side
We never see plain-text credentials
Pattern extraction only (not personal data)
You control what gets captured
Exclude sensitive sites
What we capture:
Network request patterns (how sites work)
API endpoints and structures
Response schemas
What we DON'T capture:
Screenshots or recordings
Keystrokes
Plain-text passwords
Personal browsing history (anonymized)
What if multiple people index the same site?
Variant system:
Multiple variants can exist for same action
LAM chooses best variant for each request
Quality metrics determine priority:
Success rate
Speed
Error handling
Recency
First-mover advantage:
Early high-quality variants get most traffic
But better variants can compete
Incentivizes quality improvement
Can I index on work accounts?
Check your employer's policies first.
Considerations:
Company data policies
Terms of employment
Account ownership
Intellectual property
Safe approach:
Use personal accounts
Index public SaaS tools
Avoid proprietary systems
We recommend: Stick to personal subscriptions you pay for.
For Developers
How do I integrate Unbrowse into my agent?
Quick start:
from unbrowse import Client
# Initialize
client = Client(api_key="your_api_key")
# Search for ability
abilities = client.search("post to LinkedIn with image")
# Execute
result = client.execute(
ability_id=abilities[0].id,
params={
"text": "Exciting news!",
"image_url": "https://...",
"tags": ["AI", "automation"]
}
)
print(result.data)Full docs: For Developers
What's the pricing?
Simple, transparent:
Search: $0.0001 (0.01¢) per query
Execute: $0.001 (0.1¢) per action
Typical workflow:
1 search + 1 execution = $0.0011 (0.11¢)
Volume discounts: Available for >1M requests/month
Compare to alternatives:
Browser automation: $0.01-$0.10 per action (10-100x more expensive)
Custom scrapers: $5,000-$50,000 setup (never breaks even)
Are there rate limits?
Default limits:
1,000 searches/hour
100 executions/hour
10,000 searches/day
1,000 executions/day
Upgraded plans:
Pro: 10x limits
Enterprise: Unlimited (contact us)
Why limits:
Prevent abuse
Ensure quality of service
Fair resource allocation
What if an ability fails?
Automatic handling:
Retry logic (3 attempts with backoff)
Fallback variants (try alternative patterns)
Error reporting (detailed failure info)
Your code:
try:
result = client.execute(ability_id, params)
except UnbrowseError as e:
print(f"Error: {e.message}")
print(f"Suggestions: {e.suggestions}")Success rate guarantees:
95%+ success rate on most abilities
Refund on verified failures
Continuous improvement via LAM
Can I create private abilities?
Coming soon:
Private ability creation via SDK
Custom patterns for internal tools
Team-level sharing
Usage tracking and analytics
Current workaround:
Use indexer extension on internal tools
Set privacy level to "private"
Only your agents can access
Tokenomics
Why does FDRY have value?
Three value drivers:
Utility - Required for platform access
Agents pay in USDC, converted to FDRY
Indexers earn in FDRY
Platform usage creates demand
Scarcity - Deflationary burns
40% of revenue → buybacks
50% of buybacks → permanent burns
Supply decreases daily
Network effects - Growing platform
More abilities → better coverage
More agents → more usage
More usage → more burns
More burns → higher value
Not speculation — usage-backed value.
How do the burns work?
Automatic process:
Agent pays $0.001 for execution
40% ($0.0004) goes to FDRY Treasury
Treasury buys FDRY from open market
50% of bought FDRY sent to burn address
Tokens permanently removed from circulation
Scale:
$1M monthly revenue → $400K to buyback → $200K burned
$100M monthly revenue → $40M to buyback → $20M burned
Cumulative effect:
Month 1: $73 burned
Month 12: $16,335 burned
Month 24: $199,650 burned
At scale: Millions burned monthly
When should I buy FDRY?
Not financial advice, but consider:
Bull case for early entry:
AI agents inevitable (all major companies building)
Current solutions failing (70-85% reliability)
First-mover network effects
Deflationary burns from day one
Early holders benefit most from scarcity
Risks:
Platform adoption may be slower than projected
Competitors may emerge
Regulatory changes possible
Token price volatile
Investment thesis:
Bet on AI agents needing internet access
Bet on Unbrowse execution
Bet on usage-based burns creating value
Time horizon: 12-24 months for significant appreciation
What's the token distribution?
Total supply: 1,000,000,000 FDRY (1 billion)
Distribution:
40% Community (indexers, early adopters)
30% Treasury (development, partnerships)
20% Team (4-year vesting)
10% Early investors (2-year vesting)
Vesting prevents dumps:
Team tokens unlock gradually
Investor tokens locked for 2 years
Community tokens earned via contribution
Can I stake FDRY?
Coming Q1 2026:
Stake FDRY for additional yield
Earn from platform revenue share
Governance voting power
Early access to features
Current:
Hold FDRY → benefit from burns
Earn FDRY → index abilities
Spend FDRY → execute abilities
Technical
What technology does Unbrowse use?
Frontend:
Next.js 16 (web interface)
React 19 (UI components)
Better Auth (authentication)
Backend:
Mastra 0.17 (AI agent framework)
Node.js 20 / Bun (runtime)
PostgreSQL + pgvector (database)
Infraxa (vector search)
AI:
XAI Grok (ability generation)
Google Gemini (embeddings)
Custom LAM (Large Action Model)
Execution:
curl-impersonate (TLS fingerprinting)
FlareSolverr (Cloudflare bypass)
E2B (sandboxed code execution)
Payments:
X402 (Solana smart contracts)
Atomic multi-party splits
Is the code open source?
Partially:
Open source:
SDK and client libraries
Extension (coming soon)
API documentation
Smart contracts (audited)
Proprietary:
LAM training data
Ability generation algorithms
Network execution optimizations
Why: Competitive advantage while maintaining transparency where it matters (payments, security).
How do you ensure security?
Multi-layered approach:
Credential encryption
Client-side AES-256-GCM
Zero-knowledge architecture
Local-only decryption
Sandboxed execution
E2B isolated environments
No access to host system
Resource limits enforced
Network security
HTTPS/TLS everywhere
Certificate pinning
DDoS protection (Cloudflare)
Code audits
Third-party security reviews
Automated vulnerability scanning
Bug bounty program (coming soon)
Compliance
SOC 2 Type II (in progress)
GDPR compliant
Regular penetration testing
What happens if Unbrowse goes down?
High availability design:
99.9% uptime SLA
Multi-region deployment
Automatic failover
Database replication
If we go offline:
Cached abilities still work
Local extension continues capturing
Payment processing queued
Automatic retry logic
Disaster recovery:
Daily backups
Point-in-time recovery
Ability export API (you own your data)
Can I export my abilities?
Yes! You own your contributions.
Export options:
JSON export - Raw ability definitions
Code export - Executable wrappers
HAR export - Original captures
Access:
unbrowse export --type=json --output=my-abilities.jsonUse cases:
Backup your work
Migrate to self-hosted
Custom implementations
Support & Community
Is there a community?
Yes! Join us:
Discord:
General chat
Indexer tips
Developer Q&A
Announcements
Twitter/X:
Product updates
Success stories
Technical deep dives
GitHub:
SDKs and examples
Issue tracking
Feature requests
Monthly calls:
Community AMAs
Product roadmap
Technical workshops
How can I provide feedback?
We love feedback!
Feature requests:
GitHub issues
Discord #feature-requests
Email: feedback@unbrowse.ai
Bug reports:
GitHub issues (preferred)
Discord #bug-reports
Email: bugs@unbrowse.ai
General feedback:
Discord
Twitter/X
Email: hello@unbrowse.ai
User research:
Participate in user interviews
Beta testing programs
Early access features
Still have questions? Join our Discord or email lewis@getfoundry.app.
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