The Problem: Hallucinated Citations and Context Blowout
When autonomous AI agents conduct web research, two major failures occur:
- Citation Hallucination: Agents cite academic papers, URLs, or statistics that do not exist in the source material.
- Context Window Blowout: Ingesting ten 20-page web articles consumes over 80,000 tokens, degrading reasoning quality and costing dollars per query.
flowchart LR
Query["Research Query"] --> Search["Multi-Source Discovery Engine"]
Search --> Scrape["Zero-Key Native HTML Extraction"]
Scrape --> Distill["BM25 Lexical Paragraph Distillation (-85% Tokens)"]
Distill --> Provenance["Cryptographic Byte Provenance Ledger"]
Provenance --> Synthesis["Fact-Checked Synthesis Report"]
The Solution: BM25 Distillation + Provenance Ledger
agent-deep-research enforces rigorous academic research workflows for AI agents:
- BM25 Semantic Distillation: Ranks and extracts only high-signal paragraphs matching the query, cutting context bloat by 85%.
- Byte-Level Provenance Ledger: Hashes raw source documents with SHA-256. If a claim cannot be mapped to an exact byte offset in the source hash, it is quarantined.
- Zero-Cost Search Connectors: Built-in multi-source crawling without mandatory Google Search API or Tavily subscriptions.
Quickstart in Python
pip install agent-deep-researchfrom agent_deep_research import DeepResearchEngine, ResearchQuery
engine = DeepResearchEngine()
query = ResearchQuery("PostgreSQL 17 query optimizer improvements")
dossier = engine.execute_investigation(query, max_sources=5)
print(f"Processed {len(dossier.sources)} verified sources.")
print(f"Token reduction: {dossier.token_savings_percent:.1f}%")
print("Top Verified Insight:", dossier.key_findings[0])
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Access Operator Mode & Playbook →Frequently Asked Questions
How does agent-deep-research prevent hallucinated URL citations?
Every citation is cross-checked against raw scraped HTML byte snapshots stored in a cryptographic provenance ledger before inclusion in the final synthesis.
Why is BM25 distillation better than naive full-text stuffing?
BM25 scores and extracts the highest-information paragraphs from multi-megabyte web pages, reducing token context usage by over 85% while boosting synthesis relevance.
Does it require expensive SERP API keys?
No, it features pluggable native scraping, DuckDuckGo HTML parsing, and local caching with zero mandatory API subscriptions.