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