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Benni Alencar
Benni Alencar

Posted on Originally published at benni-os.net

Autonomous Deep Research for AI Agents: Eliminating Hallucinated Citations and 85% Token Burn in Python

#ai #python #datascience #opensource

Building autonomous deep research pipelines (like Stanford STORM or OpenAI Deep Research) typically faces two massive hurdles:

  1. Insane Token Burn: Ingesting dozens of raw web pages consumes 200k to 500k tokens per research question, costing $5 to $15 per report.
  2. Hallucinated Citations: When LLMs summarize long contexts, they frequently misattribute quotes or fabricate URLs and reference numbers.

To solve this, we engineered agent-deep-research — a local-first Python engine that slashes token consumption by over 85% while guaranteeing zero hallucinated citations through cryptographic SHA-256 provenance ledgers.

The Core Innovation: BM25 Lexical Distillation

Instead of feeding raw HTML or giant text blobs into the LLM context:

  1. The engine fetches candidate sources using free, zero-key endpoints (DuckDuckGo, Wikipedia, ArXiv).
  2. Content is partitioned into 250-word passages and indexed in an in-memory BM25 Lexical Matrix.
  3. Only the top-k highest density factual passages matching the query sub-claims are extracted.
  4. Each extracted fact is stamped with a cryptographic SHA-256 hash of the exact source text. 
Raw Sources (250,000 tokens)
           |
           v
[BM25 Fact Distiller]  --> Cuts 87% Token Waste
           |
           v
Distilled Facts (<18,000 tokens) + SHA-256 Hash Ledger
           |
           v
[LLM Synthesizer]  --> 100% Verifiable Citations
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Production Usage in Python

Install the package:

pip install agent-deep-research
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Run a zero-key deep research task:

from agent_deep_research import DeepResearcher, ResearchQuery

researcher = DeepResearcher.create_default()

# Execute multi-stage synthesis
report = researcher.research(
    query="Solid-state battery commercialization breakthroughs 2026",
    max_sources=15,
    compress_ratio=0.15  # Keeps only top 15% highest-density information
)

print(f"Report Title: {report.title}")
print(f"Token Efficiency: Saved {report.tokens_saved_pct}% tokens")
print(f"Citations Verified: {report.verified_citations_count} / {report.total_citations_count}")
print(report.markdown_content)
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Native MCP Server Included

agent-deep-research also runs as a standalone Model Context Protocol (MCP) server:

python -m agent_deep_research.server
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Now Claude Desktop, Cursor, and open-source agents can invoke deep_research, quick_search, and verify_claim directly.

Sovereign Ecosystem & Open Source

Top comments (2)

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alexshev profile image
Alex Shev •

Citation quality improves when a claim is bound to the exact source span that supports it, not merely a URL somewhere in a bundle. A research agent should preserve retrieval time, source version, passage identifier, and the claim-to-evidence mapping so a reviewer can challenge one assertion without rerunning the whole search.

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fgrandon profile image
Francisco Grandón •

Partitioning raw scrapes into 250-word passages and using an in-memory BM25 index to feed only high-density factual extracts to the synthesizer is an effective way to stop burning hundreds of thousands of tokens per query. Stamping each extracted fact with a SHA-256 source hash provides the verifiable provenance chain that standard RAG pipelines often neglect. How does the lexical BM25 step perform when user queries rely on conceptual or semantic phrasing rather than the exact terminology found in the raw papers?