open-research-lite 0.1.1

Token-Efficient Ingestion Middleware & Concept-Diff Engine for Deep Research AI Agents.

DeepResearch-Lite ⚡

Token-Efficient Ingestion Middleware & Concept-Diff Engine for Deep Research AI Agents

DeepResearch-Lite is an intelligent, high-performance gatekeeper layer designed for AI Research Agents (such as langchain-ai/open_deep_research, smolagents, AutoGPT, CrewAI, or custom LangGraph workflows).

It solves the Extreme Token Bloat & Repetitive Fluff flaw in existing deep research systems by shifting the paradigm from "Read raw 5,000-word scraped web pages" to "Compile knowledge incrementally as it arrives."

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🚀 Key Features

• Dual-Layer Extraction Architecture:
  • Layer 1 (LLM Mode): Sub-second structured JSON triplet extraction (Subject ──► Predicate ──► Object) via Gemini 2.5 Flash or OpenAI Mini when an API key is available.
  • Layer 2 (Professional Local NLP Mode): 0-ms offline local NLP parser (regex metric extraction, grammar triplet matching, entity resolution) when running without API keys.
• Session Knowledge Graph: Maintains an in-memory graph state of all entities, metrics, and claims learned during a research session.
• Concept-Diff Engine: Classifies scraped facts into 3 categories:
  • DISCARD: Repetitive background fluff deleted immediately ($0 LLM tokens spent).
  • DIFF_ADD: Novel assertions added to graph & passed in Diff Payload.
  • DIFF_CONFLICT: Contradictory statements (e.g. $110 vs $140/kWh) explicitly flagged.
• 80%+ Token & Cost Reduction: Delivers a ~150-word Diff Payload to the main reasoning model instead of 5,000 words of background history.

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⚡ Quickstart

1. Installation

pip install -e .

2. Basic Usage (Python API)

import asyncio
from open_deep_research.concept_diff import ConceptDiffEngine

async def main():
    # Automatically uses GEMINI_API_KEY if present, otherwise uses Professional Local NLP
    engine = ConceptDiffEngine()

    raw_scraped_text = """
    Electric vehicles have become popular over the last decade... 
    Lithium-ion batteries were invented by John Goodenough...
    In 2026, researchers demonstrated a solid-state cell achieving 500 Wh/kg energy density.
    Target production cell cost is $110/kWh.
    """

    # Process raw scrape into a condensed Diff Payload
    diff_payload = await engine.process_observation(
        raw_text=raw_scraped_text,
        source_url="https://tech-news.com/ev-batteries",
        source_title="EV Battery 2026 Report"
    )

    print(diff_payload)

if __name__ == "__main__":
    asyncio.run(main())

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📊 Benchmark & Scorecard

Running DeepResearch-Lite on real web searches (2026 solid state battery Wh/kg breakthroughs):

===========================================================================
🏆 DEEPRESEARCH-LITE HACKATHON SCORECARD
===========================================================================
METRIC                         | BASELINE AGENT     | DEEPRESEARCH-LITE 
---------------------------------------------------------------------------
Live Web Words Fetched         | 9,159 words        | 1,083 words       
Input Tokens Passed to LLM     | 11,906 tokens      | 1,407 tokens      
Estimated Cost per Search Run  | $0.0298            | $0.0035           
Token Reduction                | 0% (Full Bloat)    | 88.2% SAVED ⚡     
Fact-to-Fluff Signal Ratio     | ~15% High Signal   | ~95% High Signal ⚡
===========================================================================

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🛠️ Framework Integration

LangGraph / open_deep_research Integration

DeepResearch-Lite integrates seamlessly into open_deep_research/deep_researcher.py at the researcher_tools() node:

from open_deep_research.concept_diff import ConceptDiffEngine

diff_engine = ConceptDiffEngine()

# In researcher_tools():
processed_observations = []
for obs, tool_call in zip(observations, tool_calls):
    if len(obs) > 100:
        diff_payload = await diff_engine.process_observation(obs, source_title=tool_call['name'])
        processed_observations.append(diff_payload)

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🧪 Testing & Demonstration Scripts

• Run Unit Tests:

  python -m pytest tests/test_concept_diff.py
  

• Run Side-by-Side Hackathon Benchmark:

  python demo_comparison.py
  

• Run Live Internet Search Comparison:

  python run_live_test.py "your custom research prompt"
  

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📄 License

MIT License. Free for open-source and commercial use.