browseaidev 0.3.2

Python SDK for BrowseAI Dev — reliable research infrastructure for AI agents

browseaidev

Reliable research infrastructure for AI agents. Python SDK for BrowseAI Dev — the research layer for LangChain, CrewAI, and custom agent pipelines.

Install

pip install browseaidev

Quick Start

from browseaidev import BrowseAIDev

client = BrowseAIDev(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}")

# Thorough mode — auto-retries if confidence < 60%
thorough = client.ask("What is quantum computing?", depth="thorough")

# Deep mode — multi-step agentic research with iterative gap analysis (requires BAI key + sign-in)
# Runs think→search→extract→evaluate cycles (up to 4 steps), 3x quota cost
deep = client.ask("Compare CRISPR approaches", depth="deep")
for step in deep.reasoning_steps or []:
    print(f"  Step {step.step}: {step.query} ({step.confidence:.0%})")

# Web search
results = client.search("latest AI news", limit=5)

# Page extraction
page = client.open("https://example.com")

# Structured extraction from a URL
extract = client.extract("https://example.com", query="pricing info")

# Compare raw LLM vs evidence-backed
compare = client.compare("Is Python faster than Rust?")

# Clarity — prompt mode (get enhanced prompts for your own LLM)
prompts = client.clarity("Write a blog post about quantum computing", mode="prompt")
print(prompts.system_prompt)  # Anti-hallucination system prompt
print(prompts.user_prompt)    # Rewritten user prompt with grounding cues
print(prompts.techniques)     # Which techniques were selected

# Clarity — answer mode (LLM answers with reduced hallucinations, no internet)
clarity = client.clarity("Write a blog post about quantum computing", mode="answer")
print(clarity.answer)         # LLM answer with reduced hallucinations
print(clarity.claims)         # Extracted claims (origin: "llm")
print(clarity.confidence)     # Confidence score

# Clarity — verified mode (LLM + web fusion for maximum accuracy)
verified = client.clarity("Explain CRISPR gene editing", mode="verified")
print(verified.answer)        # Fused answer (best of LLM + web sources)
print(verified.claims)        # Claims with origin: "confirmed", "llm", or "source"
print(verified.sources)       # Web sources used for verification

# Submit feedback to improve accuracy
client.feedback(result_id=result.share_id, rating="good")

Async

from browseaidev import AsyncBrowseAIDev

async with AsyncBrowseAIDev(api_key="bai_xxx") as client:
    result = await client.ask("What is quantum computing?")
    # Thorough mode works with async too
    thorough = await client.ask("What is quantum computing?", depth="thorough")
    # Deep mode — multi-step agentic research (requires BAI key + sign-in, 3x quota cost)
    deep = await client.ask("Complex research question", depth="deep")

Streaming (REST API)

For real-time progress events, use the streaming endpoint directly:

import httpx

with httpx.stream("POST", "https://browseai.dev/api/browse/answer/stream",
    json={"query": "What is quantum computing?"},
    headers={"X-API-Key": "bai_xxx"}
) as response:
    for line in response.iter_lines():
        if line.startswith("data: "):
            print(line[6:])

Events: trace (progress), sources (discovered early), token (streamed answer text), result (final answer), done.

Research Memory (Sessions)

Persistent research sessions that accumulate knowledge across multiple queries. Later queries recall prior knowledge — faster, cheaper, more coherent.

Sessions require a BrowseAI Dev API key (api_key="bai_xxx") for identity and ownership. Get a free API key at browseai.dev/dashboard.

from browseaidev import BrowseAIDev

client = BrowseAIDev(api_key="bai_xxx")

# Create a session
session = client.session("wasm-research")

# Each query builds on previous knowledge
r1 = session.ask("What is WebAssembly?")
r2 = session.ask("How does WASM compare to JavaScript performance?")
# ^ r2 recalls WASM knowledge from r1, only searches for JS perf

# Query accumulated knowledge without new searches
recalled = session.recall("WASM")
for entry in recalled.entries:
    print(f"  {entry.claim} (from: {entry.origin_query})")

# Export all knowledge
knowledge = session.knowledge()

# Delete a session
session.delete()

# List all your sessions
sessions = client.list_sessions()

# Resume an existing session by ID
session = client.get_session("session-id-here")

# Share with other agents
share = session.share()
print(share.url)  # https://browseai.dev/session/share/abc123def456

# Another agent forks and continues the research
forked = client.fork_session(share.share_id)

Async sessions work the same way:

async with AsyncBrowseAIDev(api_key="bai_xxx") as client:
    session = await client.session("my-project")
    r1 = await session.ask("What is WASM?")
    r2 = await session.ask("WASM vs JS?")

    # Share and fork work async too
    share = await session.share()
    forked = await client.fork_session(share.share_id)

Premium Features (with API Key)

Users with a BrowseAI Dev API key (bai_xxx) get enhanced verification:

• Semantic re-ranking — search results re-scored by semantic query-document relevance
• Semantic reranking — evidence matched by meaning, not just keywords
• Multi-provider search — parallel search across multiple sources for broader coverage
• Multi-pass consistency — claims cross-checked across independent extraction passes (in thorough mode)
• Deep reasoning mode — premium multi-step agentic research with iterative think-search-extract-evaluate cycles, gap analysis, and cross-step claim merging (up to 4 steps, targets 0.85 confidence, 3x quota cost, 100 deep queries/day). Falls back to thorough when quota is exhausted
• Token streaming — per-token answer delivery via SSE for real-time UI
• Research Sessions — persistent memory across queries

Free BAI key users get a generous daily quota (100 premium queries/day, or ~33 deep queries/day at 3x cost each). When exceeded, queries gracefully fall back to keyword verification (deep falls back to thorough) — still works, just basic matching. Quota resets every 24 hours. Check client.last_quota after any API call for current usage.

Sign in at browseai.dev for a free BAI key to unlock premium features.

Contradictions

Detect conflicts across sources on controversial topics:

result = client.ask("Is coffee good for your health?", depth="thorough")
if result.contradictions:
    for c in result.contradictions:
        print(f"Conflict on '{c.topic}':")
        print(f"  A: {c.claim_a}")
        print(f"  B: {c.claim_b}")

Enterprise Search Providers

Use your own data sources instead of — or alongside — public web search. Supports elasticsearch, confluence, and custom endpoints with optional data_retention="none" for compliance.

from browseaidev.models import SearchProviderConfig

# Using the typed model (snake_case fields)
provider = SearchProviderConfig(
    type="elasticsearch",
    endpoint="https://es.company.com/kb/_search",
    auth_header="Bearer token",
    index="docs",
)
result = client.ask("What is our refund policy?", search_provider=provider)

# Or pass a plain dict (camelCase keys, sent directly to API)
result = client.ask("What is our refund policy?", search_provider={
    "type": "elasticsearch",
    "endpoint": "https://es.company.com/kb/_search",
    "authHeader": "Bearer token",
    "index": "docs",
})

# Confluence
result = client.ask("PCI compliance?", search_provider={
    "type": "confluence",
    "endpoint": "https://company.atlassian.net/wiki/rest/api",
    "authHeader": "Basic base64-creds",
    "spaceKey": "ENG",
})

# Zero data retention (compliance mode — nothing stored, cached, or logged)
result = client.ask("Patient protocols", search_provider=SearchProviderConfig(
    type="elasticsearch",
    endpoint="https://es.hipaa.company.com/medical/_search",
    auth_header="Bearer token",
    data_retention="none",
))

Framework Integrations

LangChain

pip install langchain-browseaidev

from langchain_browseaidev import BrowseAIDevAnswerTool, BrowseAIDevSearchTool

tools = [BrowseAIDevAnswerTool(api_key="bai_xxx")]

CrewAI

pip install crewai-browseaidev

from crewai_browseaidev import BrowseAIDevAnswerTool

researcher = Agent(tools=[BrowseAIDevAnswerTool(api_key="bai_xxx")])

LlamaIndex

pip install llamaindex-browseaidev

from llamaindex_browseaidev import BrowseAIDevAnswerTool

answer_tool = BrowseAIDevAnswerTool(api_key="bai_xxx")