BrowseAI Dev

BrowseAI Dev

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Research infrastructure for AI agents with Grounded Intelligence — real-time web search, evidence extraction, verification, and structured citations. Every claim is backed by a URL. Every answer has a confidence score.

Agent → BrowseAI Dev → Internet → Verified answers + sources

Website · Playground · API Docs · Alternatives · Discord

> Package names: npm: browseai-dev · PyPI: browseaidev · LangChain: langchain-browseaidev — Previously browse-ai and browseai. Old names still work and redirect automatically.

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How It Works

search → fetch pages → neural rerank → extract claims → verify → cited answer (streamed)

Every answer goes through a multi-step verification pipeline. No hallucination. Every claim is backed by a real source.

Verification & Confidence Scoring

Confidence scores are evidence-based — not LLM self-assessed. After the LLM extracts claims and sources, a post-extraction verification engine checks every claim against the actual source page text:

1. Atomic claim decomposition — Compound claims are auto-split into individual verifiable facts. "Tesla had $96B revenue and 1.8M deliveries" becomes two atomic claims, each verified independently.
2. Hybrid retrieval combining keyword and semantic matching — For each claim, keyword matching finds lexical matches and dense embeddings find semantic matches from source text. Rankings are fused to catch paraphrased evidence that keyword matching alone misses (e.g., "prevents fabricated answers" matching "reduces hallucinations"). Premium tier only, with graceful keyword-only fallback.
3. Semantic evidence reranking — Top candidates per claim are reranked by a purpose-built verification model trained on 1.4M+ claim-evidence pairs that improves with every query. Selects the best supporting evidence, applies contradiction penalties and paraphrase boosts.
4. Multi-provider search — Parallel search across multiple providers for broader source diversity. More independent sources = stronger cross-reference = higher confidence.
5. Domain authority scoring — 10,000+ domains across 5 tiers (institutional .gov/.edu → major news → tech journalism → community → low-quality). Dynamic scoring that improves from real verification data.
6. Source quote verification — LLM-extracted quotes verified against actual page text using multi-strategy matching.
7. Cross-source consensus — Each claim verified against all available page texts. Claims supported by 3+ independent domains get "strong consensus". Single-source claims flagged as "weak".
8. Contradiction detection — Claim pairs analyzed for semantic conflicts using topic overlap and contradiction classification. Detected contradictions surfaced in the response and penalize confidence.
9. Multi-pass consistency — In thorough mode, claims are cross-checked across independent extraction passes. Claims confirmed by both passes get boosted; inconsistent claims are penalized.
10. Auto-calibrated confidence — Multi-factor confidence formula auto-adjusts from real user feedback. Predicted confidence aligns with actual accuracy over time. Factors: verification rate, domain authority, source count, consensus, domain diversity, claim grounding, source recency, and citation depth.
11. Per-claim evidence retrieval — Weak claims get targeted search queries generated by LLM, then searched individually across all providers. Each claim gets its own evidence pool instead of sharing the same corpus.
12. Counter-query verification — Verified claims are stress-tested with adversarial "what would disprove this?" search queries. If counter-evidence is found, claim confidence is penalized.
13. Iterative confidence-gated retrieval — Thorough mode uses a confidence-gated loop: verify → if weak claims remain → generate targeted query → search → re-verify. Loops up to 3 iterations with early termination when queries repeat or confidence meets threshold.

Claims include verified, verificationScore, consensusCount, and consensusLevel fields. Sources include verified and authority. Detected contradictions are returned at the top level. Agents can use these fields to make trust decisions programmatically.

> Graceful fallback: When premium keys are not set, the system runs keyword-only verification. Semantic retrieval and reranking are transparent premium enhancements — no degradation, no errors.

Depth Modes

Three depth levels control research thoroughness:

| Depth | Behavior | Use case | |-------|----------|----------| | fast (default) | Single search → extract → verify pass | Quick lookups, real-time agents | | thorough | Iterative confidence-gated loop (up to 3 passes), per-claim evidence retrieval, counter-query verification, multi-pass consistency checking | Important research, fact-checking | | deep | Premium multi-step agentic research: iterative think-search-extract-evaluate cycles (up to 4 total steps). Gap analysis identifies missing info, generates follow-up queries. Claims/sources merged across steps with final re-verification. Target confidence: 0.85. Requires BAI key + sign-in. Falls back to thorough when quota exhausted. | Complex research questions, comprehensive analysis |

# Thorough mode
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?", "depth": "thorough"}'

# Deep mode (uses premium features)
curl -X POST https://browseai.dev/api/browse/answer \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer bai_xxx" \
  -d '{"query": "Compare CRISPR approaches for sickle cell disease", "depth": "deep"}'

Deep mode runs iterative think-search-extract-evaluate cycles: each step performs gap analysis to identify what's missing, generates targeted follow-up queries, and merges claims/sources across steps with a final re-verification pass. It targets a confidence threshold of 0.85 (DEEP_CONFIDENCE_THRESHOLD) and runs up to 3 follow-up steps (MAX_FOLLOW_UP_STEPS, 4 total including the initial pass). Uses semantic reranking, multi-provider search, and multi-pass consistency. Each deep query costs 3x quota (100 deep queries/day). When quota is exhausted, deep mode gracefully falls back to thorough. Without a BAI key, deep mode also falls back to thorough.

Deep mode responses include reasoningSteps showing the multi-step research process (step number, query, gap analysis, claim count, confidence per step).

Streaming API

Get real-time progress with per-token answer streaming. The streaming endpoint sends Server-Sent Events (SSE) as each pipeline step completes. Deep mode steps are grouped by research pass for clean progress display:

curl -N -X POST https://browseai.dev/api/browse/answer/stream \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer bai_xxx" \
  -d '{"query": "What is quantum computing?"}'

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

Retry with Backoff

All external API calls (search providers, LLM, page fetching) automatically retry on transient failures (429 rate limits, 5xx server errors) with exponential backoff and jitter. Auth errors (401/403) fail immediately — no wasted retries.

Research Memory (Sessions)

Persistent research sessions that accumulate knowledge across multiple queries. Later queries automatically recall prior verified claims, building deeper understanding over time.

> Sessions require a BrowseAI Dev API key (bai_xxx) for identity and ownership. Get a free key at browseai.dev/dashboard. For MCP, set BROWSE_API_KEY env var. For Python SDK, pass api_key="bai_xxx". For REST API, use Authorization: Bearer bai_xxx.

# Python SDK
session = client.session("quantum-research")
r1 = session.ask("What is quantum entanglement?")       # 13 claims stored
r2 = session.ask("How is entanglement used in computing?")  # 12 claims recalled!
knowledge = session.knowledge()  # Export all accumulated claims

# Share with other agents or humans
share = session.share()  # Returns shareId + URL
# Another agent forks and continues the research
forked = client.fork_session(share.share_id)

# REST API
curl -X POST https://browseai.dev/api/session \
  -H "Authorization: Bearer bai_xxx" \
  -d '{"name": "my-research"}'
# Returns session ID, then:
curl -X POST https://browseai.dev/api/session/{id}/ask \
  -H "Authorization: Bearer bai_xxx" \
  -d '{"query": "What is quantum entanglement?"}'

# Share a session publicly
curl -X POST https://browseai.dev/api/session/{id}/share \
  -H "Authorization: Bearer bai_xxx"

# Fork a shared session (copies all knowledge)
curl -X POST https://browseai.dev/api/session/share/{shareId}/fork \
  -H "Authorization: Bearer bai_xxx"

Each session response includes recalledClaims and newClaimsStored. Sessions can be shared publicly and forked by other agents — enabling collaborative, multi-agent research workflows.

Query Planning

Complex queries are automatically decomposed into focused sub-queries with intent labels (definition, evidence, comparison, counterargument, technical, historical). Each sub-query targets a different aspect of the question, maximizing source diversity. Simple factual queries skip planning entirely — no added latency.

Self-Improving Accuracy

The entire verification pipeline improves automatically with usage:

• Domain authority — Dynamic scoring adjusts domain trust scores as evidence accumulates. Static tier scores dominate initially, then real verification rates take over.
• Adaptive verification thresholds — Claim verification thresholds tune per query type based on observed verification rates. Too strict? Loosens up. Too lenient? Tightens.
• Consensus threshold tuning — Cross-source agreement thresholds adapt based on query type performance.
• Confidence weight optimization — The multi-factor confidence formula rebalances weights per query type when user feedback indicates inaccuracy.
• Page count optimization — Source fetch counts adjust based on confidence outcomes per query type.

Feedback Loop

Submit feedback on results to accelerate learning. Agents and users can rate results as good, bad, or wrong — this feeds directly into the adaptive threshold engine.

curl -X POST https://browseai.dev/api/browse/feedback \
  -H "Content-Type: application/json" \
  -d '{"resultId": "abc123", "rating": "good"}'

client.feedback(result_id="abc123", rating="good")
# Or flag a specific wrong claim:
client.feedback(result_id="abc123", rating="wrong", claim_index=2)

Quick Start

Python SDK

pip install browseaidev

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  Get a free API key at [browseai.dev/dashboard](https://browseai.dev/dashboard).

### REST API

```bash
# Basic query
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?"}'

# Thorough mode (auto-retries if confidence  The verification engine (API server) is in a separate private repository ([BrowseAI-HQ/browseaidev-engine](https://github.com/BrowseAI-HQ/browseaidev-engine)) and runs as a hosted service.

## 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. `depth`: `"fast"`, `"thorough"`, or `"deep"` |
| `POST /browse/answer/stream` | Streaming answer via SSE — real-time token streaming + progress events |
| `POST /browse/compare` | Compare raw LLM vs evidence-backed answer |
| `POST /browse/clarity` | Clarity — anti-hallucination answer engine. Three modes: `mode: "prompt"` (enhanced prompts only), `mode: "answer"` (LLM answer, default), `mode: "verified"` (LLM + web fusion). Legacy `verify: true` = `mode: "verified"` |
| `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) |
| `POST /session` | Create a research session |
| `POST /session/:id/ask` | Research with session memory (recalls + stores claims) |
| `POST /session/:id/recall` | Query session knowledge without new search |
| `GET /session/:id/knowledge` | Export all session claims |
| `POST /session/:id/share` | Share a session publicly (returns shareId) |
| `GET /session/share/:shareId` | View a shared session (public, no auth) |
| `POST /session/share/:shareId/fork` | Fork a shared session into your account |
| `GET /session/:id` | Get session details |
| `GET /sessions` | List your sessions (authenticated) |
| `DELETE /session/:id` | Delete a session (authenticated) |
| `POST /browse/feedback` | Submit feedback on a result (good/bad/wrong) |
| `GET /browse/learning/stats` | Self-learning engine stats |
| `GET /user/stats` | Your query stats (authenticated) |
| `GET /user/history` | Your query history (authenticated) |
| `DELETE /user/data` | Delete all your data (GDPR right to erasure) |

## 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. `depth`: `"fast"`, `"thorough"`, or `"deep"` |
| `browse_compare` | Compare raw LLM vs evidence-backed answer |
| `browse_clarity` | Anti-hallucination answer engine — three modes: prompt (prompts only), answer (LLM), verified (LLM + web fusion) |
| `browse_session_create` | Create a research session (persistent memory) |
| `browse_session_ask` | Research within a session (recalls prior knowledge) |
| `browse_session_recall` | Query session knowledge without new web search |
| `browse_session_share` | Share a session publicly (returns share URL) |
| `browse_session_knowledge` | Export all claims from a session |
| `browse_session_fork` | Fork a shared session to continue the research |
| `browse_feedback` | Submit feedback on a result to improve accuracy |

## 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, depth=)` | Full pipeline with citations. `depth`: `"fast"`, `"thorough"`, or `"deep"` |
| `client.compare(query)` | Raw LLM vs evidence-backed |
| `client.session(name)` | Create a research session |
| `session.ask(query, depth=)` | Research with memory recall |
| `session.recall(query)` | Query session knowledge |
| `session.knowledge()` | Export all session claims |

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [BrowseAI-HQ](https://github.com/BrowseAI-HQ)
- **Source:** [BrowseAI-HQ/BrowseAI-Dev](https://github.com/BrowseAI-HQ/BrowseAI-Dev)
- **License:** Apache-2.0
- **Homepage:** https://browseai.dev

Install and usage instructions live in the source repository linked above.