BrowseAI Dev
Reliable research infrastructure for AI agents — real-time web search, evidence extraction, and structured citations. Every claim is backed by a URL. Every answer has a confidence score.
Agent → BrowseAI → Internet → Verified answers + sources
Website · Playground · API Docs · Discord
How It Works
search → fetch pages → extract claims → build evidence graph → cited answer
Every answer goes through a 5-step verification pipeline. No hallucination. Every claim is backed by a real source. Confidence scores are evidence-based — computed from source count, domain diversity, claim grounding, and citation depth.
Quick Start
Python SDK
pip install browseai
from browseai import BrowseAI
client = BrowseAI(api_key="bai_xxx")
# Research with citations
result = client.ask("What is quantum computing?")
print(result.answer)
print(f"Confidence: {result.confidence:.0%}")
for source in result.sources:
print(f" - {source.title}: {source.url}")
Framework integrations:
pip install browseai[langchain] # LangChain tools
pip install browseai[crewai] # CrewAI integration
# LangChain
from browseai.integrations.langchain import BrowseAIAskTool
tools = [BrowseAIAskTool(api_key="bai_xxx")]
# CrewAI
from browseai.integrations.crewai import BrowseAITool
researcher = Agent(tools=[BrowseAITool(api_key="bai_xxx")])
MCP Server (Claude Desktop, Cursor, Windsurf)
npx browse-ai setup
Or manually add to your MCP config:
{
"mcpServers": {
"browse-ai": {
"command": "npx",
"args": ["-y", "browse-ai"],
"env": {
"SERP_API_KEY": "your-search-key",
"OPENROUTER_API_KEY": "your-llm-key"
}
}
}
}
REST API
# With your own keys (BYOK — free, no limits)
curl -X POST https://browseai.dev/api/browse/answer \
-H "Content-Type: application/json" \
-H "X-Tavily-Key: tvly-xxx" \
-H "X-OpenRouter-Key: sk-or-xxx" \
-d '{"query": "What is quantum computing?"}'
# With a BrowseAI API key
curl -X POST https://browseai.dev/api/browse/answer \
-H "Content-Type: application/json" \
-H "Authorization: Bearer bai_xxx" \
-d '{"query": "What is quantum computing?"}'
Self-Host
git clone https://github.com/BrowseAI-HQ/BrowserAI-Dev.git
cd BrowserAI-Dev
pnpm install
cp .env.example .env
# Fill in: SERP_API_KEY, OPENROUTER_API_KEY
pnpm dev
API Keys
Three ways to authenticate:
| Method | How | Limits |
|---|---|---|
| BYOK (recommended) | Pass X-Tavily-Key and X-OpenRouter-Key headers |
Unlimited, free |
| BrowseAI API Key | Pass Authorization: Bearer bai_xxx |
Unlimited (uses your stored keys) |
| Demo | No auth needed | 5 queries/hour per IP |
Get a BrowseAI API key from the dashboard — it bundles your Tavily + OpenRouter keys into one key for CLI, MCP, and API use.
Project Structure
/apps/api Fastify API server (port 3001)
/apps/mcp MCP server (stdio transport, npm: browse-ai)
/packages/shared Shared types, Zod schemas, constants
/packages/python-sdk Python SDK (PyPI: browseai)
/src React frontend (Vite, port 8080)
/supabase Database migrations
API Endpoints
| Endpoint | Description |
|---|---|
POST /browse/search |
Search the web |
POST /browse/open |
Fetch and parse a page |
POST /browse/extract |
Extract structured claims from a page |
POST /browse/answer |
Full pipeline: search + extract + cite |
POST /browse/compare |
Compare raw LLM vs evidence-backed answer |
GET /browse/share/:id |
Get a shared result |
GET /browse/stats |
Total queries answered |
GET /browse/sources/top |
Top cited source domains |
GET /browse/analytics/summary |
Usage analytics (authenticated) |
MCP Tools
| Tool | Description |
|---|---|
browse_search |
Search the web for information on any topic |
browse_open |
Fetch and parse a web page into clean text |
browse_extract |
Extract structured claims from a page |
browse_answer |
Full pipeline: search + extract + cite |
browse_compare |
Compare raw LLM vs evidence-backed answer |
Python SDK
| Method | Description |
|---|---|
client.search(query) |
Search the web |
client.open(url) |
Fetch and parse a page |
client.extract(url, query=) |
Extract claims from a page |
client.ask(query) |
Full pipeline with citations |
client.compare(query) |
Raw LLM vs evidence-backed |
Async support: AsyncBrowseAI with the same API.
Examples
See the examples/ directory for ready-to-run agent recipes:
| Example | Description |
|---|---|
| research-agent.py | Simple research agent with citations |
| code-research-agent.py | Research libraries/docs before writing code |
| hallucination-detector.py | Compare raw LLM vs evidence-backed answers |
| langchain-agent.py | BrowseAI as a LangChain tool |
| crewai-research-team.py | Multi-agent research team with CrewAI |
Environment Variables
| Variable | Required | Description |
|---|---|---|
SERP_API_KEY |
Yes | Web search API key (Tavily) |
OPENROUTER_API_KEY |
Yes | LLM API key (OpenRouter) |
REDIS_URL |
No | Redis URL (falls back to in-memory cache) |
SUPABASE_URL |
No | Supabase project URL |
SUPABASE_SERVICE_ROLE_KEY |
No | Supabase service role key |
PORT |
No | API server port (default: 3001) |
Tech Stack
- API: Node.js, TypeScript, Fastify, Zod
- Search: Tavily API
- Parsing: @mozilla/readability + linkedom
- AI: Gemini 2.5 Flash via OpenRouter
- Caching: In-memory with intelligent TTL (time-sensitive queries get shorter TTL)
- Frontend: React, Tailwind CSS, shadcn/ui, Framer Motion
- MCP: @modelcontextprotocol/sdk
- Python SDK: httpx, Pydantic
- Database: Supabase (PostgreSQL)
Community
- Discord — questions, feedback, showcase
- GitHub Issues — bugs, feature requests
Contributing
See CONTRIBUTING.md for setup instructions, coding conventions, and PR process.
License
MIT
