---

title: open research-lite colorFrom: red colorTo: crimson sdk: gradio sdk_version: 4.44.0 app_file: app.py pinned: false license: mit short_description: Autonomous Deep Research Agent with Differential Knowledge Graphs and Dual-Model Architecture tags:

• deep-research
• llm-agents
• gpt-researcher
• knowledge-graph
• agentic-rag
• token-optimization
• dual-model
• paper:10.5281/zenodo.22168098

---

open research-lite

Autonomous Deep Research Agent with Differential Knowledge Graphs and Dual-Model Pipeline
Slashes 70%+ token bloat, extracts atomic fact assertions, catches metric contradictions deterministically, and synthesizes executive research dossiers.

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Why open research-lite over GPT-Researcher?

In conventional research agents (such as GPT-Researcher), over 70% of prompt tokens are consumed ingesting repetitive introductory fluff, boilerplate history, and SEO prose across multi-page web search streams. Furthermore, GPT-Researcher's "Fast LLM" is only used for generating search sub-queries, while raw scraped web pages (50,000+ tokens) are dumped into the expensive "Smart LLM". When two sources present divergent metrics (for example, 26W vs 31W peak package power), conventional agents silently average or hallucinate.

open research-lite introduces an architectural layer add-on with Session Knowledge-State Tracking ($G_t = G_{t-1} \cup \Delta G_t$) that supercharges the Fast LLM & Smart LLM pipeline:

1. Fast LLM (fast_llm / extractor_model): A fast, cost-efficient model (e.g., gemini-2.5-flash, gpt-4o-mini, claude-3-5-haiku, llama-3.1-8b) acts as an intelligent gatekeeper. It sifts raw web scrapes, isolates Subject-Predicate-Object triplets and quantitative metrics, prunes 70%+ redundant text, and detects metric contradictions.
2. Smart LLM (smart_llm / writer_model): A flagship reasoning model (e.g., claude-3-5-sonnet, gpt-4o, gemini-2.5-pro, o1, deepseek-r1) receives only the high-signal, distilled differential graph assertions (~1,200 tokens instead of 50,000+ raw tokens) and writes a comprehensive, structured research dossier.

Capability	GPT-Researcher	open research-lite	Why It Matters
Ingestion Pipeline	Raw document stuffing (50k+ tokens)	Dual-Model Differential Graph	70%+ Cheaper API costs and sub-second synthesis
Fast LLM Role	Search sub-query generator only	Fact Extractor & Contradiction Radar	Converts raw scrapes into verified fact graph
Contradiction Detection	None (silently averages)	Deterministic Contradiction Radar	Flags real metric disputes across sources
Visual Knowledge Graph	None	Interactive HTML / Mermaid Dossier	Visual entity relationship mapping
Interactive Terminal UI	Basic stdout	Crimson Terminal Dashboard (Claude Code-inspired)	Full model dropdowns, live progress, metric scorecards
Multi-Tenancy & Isolation	Varies	Thread-Safe Session Isolation	Zero state leakage between concurrent runs
Drop-in Compatibility	GPTResearcher	from open_research_lite import GPTResearcher	1-line seamless replacement

---

Quickstart

1. Installation

pip install open-research-lite

2. Custom Terminal Commands (No python -m Required)

Launch deep autonomous research straight from your terminal using custom executable commands:

# Windows
.\open-research "High-Bandwidth Memory HBM4 Architecture and Interconnect Scaling"

# Or run the interactive crimson TUI wizard
.\open-research

# macOS / Linux
./open-research "Fault-Tolerant Quantum Computing and Surface Code Thresholds"

Automatically generates both a clean Markdown dossier (research_report.md) and a standalone visual HTML report (research_report.html) with embedded interactive Mermaid knowledge graphs!

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3. Supported Production Models

open research-lite supports 18+ production models selectable directly from interactive dropdowns:

• Google: Gemini 2.5 Flash, Gemini 2.5 Pro
• OpenAI: GPT-4o, GPT-4o Mini, GPT-4.1, o1, o3-mini
• Anthropic: Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus
• DeepSeek: DeepSeek-R1, DeepSeek-V3
• Meta / Groq: Llama 3.3 70B, Llama 3.1 8B, Mixtral 8x7B
• Mistral: Mistral Large, Mistral Small

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4. Python API (Drop-in GPT-Researcher Replacement)

Use open research-lite as a drop-in replacement for GPT-Researcher with identical fast_llm and smart_llm parameters:

import asyncio
from open_research_lite import GPTResearcher, Researcher

async def main():
    # 1. Initialize Researcher with Fast LLM and Smart LLM
    researcher = GPTResearcher(
        query="High-Bandwidth Memory HBM4 Architecture Trends",
        report_type="research_report",
        fast_llm="gpt-4o-mini",               # Fast LLM for fact extraction & graph building
        smart_llm="claude-3-5-sonnet-latest", # Smart LLM for analytical dossier writing
        search_api="tavily",                  # Or "duckduckgo" for free search
        max_results=5
    )

    # 2. Conduct research (extracts facts and updates session graph)
    await researcher.conduct_research()

    # 3. Synthesize structured report
    report = await researcher.write_report()
    print(report)

    # 4. Export visual HTML dossier and Markdown
    researcher.export_markdown("hbm4_report.md")
    researcher.export_html("hbm4_dossier.html")

    # 5. Inspect telemetry & graph stats
    stats = researcher.get_stats()
    print(f"Token reduction: {stats['telemetry']['token_reduction_pct']}%")
    print(f"Verified assertions: {stats['graph']['total_facts']}")

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

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5. Standalone Middleware Usage (for LangGraph, CrewAI, AutoGPT)

You can use the core ConceptDiffEngine as gatekeeper middleware for existing agent architectures:

import asyncio
from open_research_lite import ConceptDiffEngine

async def main():
    engine = ConceptDiffEngine(model_name="gemini-2.5-flash")

    raw_scrape = """
    High-Bandwidth Memory (HBM) stacks integrate multiple DRAM dies via TSVs.
    In 2026, engineers validated an HBM4 memory stack delivering 2.8 TB/s peak bandwidth per stack.
    Operating power consumption is measured at 26W under full saturation.
    """

    # Intercept raw scrape and convert into dense diff payload
    diff_payload = await engine.process_observation(
        raw_text=raw_scrape,
        source_url="https://semiconductor-audit.org/hbm4-2026",
        source_title="HBM4 Interface Milestone"
    )

    print(diff_payload)

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

---

Interactive Visual HTML Dossier

Every research mission exports an interactive dark-mode HTML dossier containing:

1. Mermaid.js Knowledge Graph: Pan, zoom, and inspect verified entity links and relationships.
2. Contradiction Radar: Callout cards highlighting conflicting claims between sources.
3. Efficiency Scorecard: Raw input tokens, pruned redundant tokens, and unique mapped entities.
4. Structured Executive Dossier: Executive summary, key metrics, and linked source citations.

---

Repository Structure

open-research-lite/
├── src/
│   └── open_research_lite/         # Production package (v0.3.0)
│       ├── __init__.py             # Top-level exports (Researcher, GPTResearcher, exceptions)
│       ├── __main__.py             # Interactive crimson TUI wizard
│       ├── researcher.py           # Core orchestrator with bounded async concurrency
│       ├── diff_engine.py          # Differential fact extraction & contradiction engine
│       ├── knowledge_graph.py      # Thread-safe SessionKnowledgeGraph (RLock)
│       ├── models.py               # Multi-provider LLM factory (Google, OpenAI, Anthropic, etc.)
│       ├── search.py               # Search engine integrations (DuckDuckGo, Tavily, Google)
│       ├── exceptions.py           # Typed exception hierarchy
│       └── telemetry.py            # Token cost and latency metrics
├── docs/                           # Whitepapers, technical specifications & proposals
│   ├── RESEARCH_PAPER.md           # Formal paper on Concept-Diffing architecture
│   ├── paper.tex                   # LaTeX preprint
│   └── proposals/                  # Platform integration proposals
├── benchmarks/                     # GAIA & deep research benchmarks and verified results
│   ├── results/                    # Benchmark scorecards and evaluation outputs
│   └── legacy_eval/                # Historical benchmark harnesses
├── examples/                       # Developer guides & quickstart scripts
│   ├── quickstart_researcher.py    # Complete Python API walkthrough
│   └── reports/                    # Domain-specific sample reports
├── tests/                          # Production unit & integration test suites
│   ├── test_researcher.py          # Researcher API, dual models, custom search tests
│   └── test_concept_diff.py        # Fact diffing and knowledge graph tests
├── cli.py                          # Direct launcher
├── open-research.cmd               # Custom Windows terminal command
└── pyproject.toml                  # Package configuration (v0.3.0)

---

Enterprise Production Features (v0.3.0)

• Bounded Asynchronous Concurrency: Parallel source processing via asyncio.Semaphore(max_concurrency) prevents API rate limits and thread starvation during massive web crawls.
• Custom Search Provider Injection: Supply your own search callback (search_func) to query internal enterprise databases, vector indices, or proprietary APIs seamlessly.
• Structured JSON Export: Call export_json("output.json") or to_dict() to ingest research graph nodes, facts, contradiction warnings, and telemetry directly into microservice pipelines.
• Strongly-Typed Exceptions: Catch granular errors (ConfigurationError, SearchProviderError, FactExtractionError, ReportSynthesisError) without unhandled crashes or silent mock fallbacks.
• Thread-Safe Multi-Tenancy: SessionKnowledgeGraph and ResearchTelemetry employ reentrant locking (threading.RLock) to guarantee complete thread safety across parallel worker threads.

---

Testing & Verification

# Run unit tests
python tests/test_concept_diff.py
python tests/test_researcher.py

# Run quickstart demo
python examples/quickstart_researcher.py

---

Citation

@article{openresearchlite2026,
  title={Differential Knowledge-State Tracking for Token-Efficient Autonomous Deep Research Agents},
  author={open research-lite Team},
  year={2026},
  doi={10.5281/zenodo.22168098}
}

License

MIT License. Open source and production ready.