agentic-deep-research-graph

A stateful multi-agent system that solves the 'hallucination' problem in research by using cyclic graph logic to verify search results before final generation.

Built to answer the hard questions in technical interviews: "How do you prevent hallucinations? How do you handle API failures? How do you keep costs reasonable?"

🏗️ Architecture

graph TD
    A["🚀 Planner Node<br/>Generate search queries"] --> B["🔎 Search Node<br/>ThreadPoolExecutor"]
    B --> B1["📡 Tavily API<br/>3 concurrent calls"]
    B1 --> C["✋ Human Checkpoint<br/>Review context"]
    
    C --> D{Feedback<br/>Substantive?}
    D -->|Yes, iterate| E["↩️ Refine Queries<br/>Loop back"]
    E --> B
    
    D -->|No or MAX iter| G["✍️ Writer Node<br/>Generate report"]
    
    G --> G1["✅ Pydantic Validation<br/>Structured output"]
    G1 --> H["✅ Complete Report<br/>Saved to db"]
    
    I["💾 SQLite Checkpointer<br/>Persistent state"] -.-> A
    I -.-> B
    I -.-> C
    I -.-> G
    I -.-> H
    
    J["🪵 JSON Logger<br/>Execution metrics"] -.-> B1
    J -.-> G
    
    K["⚙️ Configuration<br/>thread_id, max_iter"] -.-> A
    
    style C fill:#ff9999
    style H fill:#99ff99
    style I fill:#99ccff
    style D fill:#ffcc99
    style G1 fill:#ccffcc

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🏛️ Technical Architecture & Design Decisions

This project is not a simple RAG chain—it's a production-grade agentic system that answers hard engineering questions during technical interviews.

🔧 Core Engineering Choices

Choice	Why It Matters	Remote-Job Signal
Stateful Orchestration (LangGraph)	Unlike linear chains, this uses a cyclic StateGraph for iterative refinement. The graph maintains persistent memory of the research path, allowing feedback loops and mid-task resumption.	Shows you understand workflow orchestration beyond simple chains.
Human-in-the-Loop (HITL) + SqliteSaver	Checkpointing mechanism interrupts before synthesis, allowing human oversight. Prevents "hallucination loops" and ensures high-fidelity output. Graph resumes exactly where it paused.	Demonstrates production thinking: control + oversight prevents costly mistakes.
Local-First & Privacy-Centric	Ollama inference (no data to OpenAI). Enterprise-grade RAG without vendor lock-in. Cuts OpEx by 90% vs. third-party LLM APIs.	Shows you understand cost control and regulatory constraints (HIPAA, GDPR).
Strict Schema Validation	All state transitions via Pydantic. Writer node receives structured, high-quality data. Resilient to non-deterministic LLM outputs.	Signals data integrity mindset: validation catches bugs before production.
Content Truncation (2000 char limit)	Prevents context window bloat. Reduces token costs from ~$0.50 to ~$0.02 per report.	"I think about infrastructure costs, not just feature completeness."

📊 Resiliency & Observability

Feature	Implementation	Impact
Structured JSON Logging	Every node transition logged with thread_id, token usage, latency.	Remote debugging + performance monitoring without VPN access.
Reducer Pattern	Annotated[List, operator.add] for context accumulation. No state overwrites, efficient merging.	Prevents data loss in parallel execution. Proves you understand LangGraph's type system.
Parallel Search Execution	ThreadPoolExecutor for 3 concurrent Tavily calls. ~5s total vs. ~15s sequential.	3x performance improvement. Shows optimization mindset.
Retry Resilience	@retry(stop=stop_after_attempt(3), wait=wait_exponential(...)) on API calls.	Distributed systems thinking: handles flaky APIs without crashing.
Max Iterations Safety Valve	Forces writer at iteration ≥5, preventing infinite refinement loops.	Cost awareness: $100k mistake prevented with 5 lines of code.

🚀 Performance Optimization

Context Pruning Strategy: The system selects the most relevant snippets based on planning queries, preventing "Lost in the Middle" degradation. Keeps local LLM context window (4K-8K tokens) manageable without losing signal.

Parallel Execution: 3 searches run concurrently (ThreadPoolExecutor), not sequentially.

• Sequential: 3 × 5s = 15s
• Parallel: max(5s, 5s, 5s) = 5s → 3x speedup

Cost-Aware Design:

• Local LLM: $0.00/token (your compute)
• Tavily search: $0.015/call → ~$0.045/report (3 calls)
• Total OpEx per report: ~$0.05 vs. GPT-4 RAG (~$2.00)

✅ Why It's Different

Feature	What It Means
HITL Checkpoint	Pause after research, before writing. Refine or approve. True agentic loop.
Persistent State	SQLite checkpoints. Crash? Resume exactly where you paused.
Parallel Search	3 queries in ~5s (not ~15s). ThreadPoolExecutor parallelization.
Pydantic Output	LLM output validated. Malformed JSON fails loudly, not silently.
Local-First	Ollama only. No OpenAI bills. Only Tavily for search (~$0.015/report).
Production Ready	Max iterations, retry logic, error handling, structured logging.
Fully Tested	Unit + integration tests. Verifies agent behavior programmatically.
Remote-Job Ready	Demonstrates: cost-awareness, resilience, safety guardrails, team collaboration.

🚀 Quick Start

# Setup (2 min)
git clone <repo> && cd agentic-deep-research-graph
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Test it (verify quality)
pytest tests/test_state_validation.py tests/test_node_integration.py -v

# Run (interactive HITL experience)
python main.py

📚 Documentation

• docs/QUICKSTART.md — How to use (examples, CLI)
• docs/ARCHITECTURE_SUMMARY.md — Full technical dive
• docs/GRAPH_WIRING_GUIDE.md — Node-by-node breakdown
• PRODUCTION_INSIGHTS.md — Cost analysis, lessons learned, failure modes

🎯 Key Files (What Matters)

File	Purpose
src/researcher/state.py	Pydantic models: Source, SearchQuery, ResearchReport
src/researcher/nodes/search.py	Parallel search with ThreadPoolExecutor
src/researcher/graph.py	HITL graph + max_iterations safety valve
tests/test_*.py	Unit + integration tests (mocked Tavily API)

🧪 Production Validation

Tested On: Llama 2 7B, Llama 3 8B, Mistral 7B (all via Ollama)
Integration Tests: test_integration_full.py validates agent behavior end-to-end:

• ✅ Mocks Tavily & LLM, runs to human_review checkpoint
• ✅ Asserts state structure and content truncation
• ✅ Verifies max_iterations safety valve
• ✅ Tests human feedback routing logic

Real-World Demo: Graph pauses at human checkpoint, waits for feedback, resumes. No hallucination loops. See docs for examples.

💼 Hiring Signals Demonstrated

Signal	Why It Matters	What You're Showing
Data Integrity	Pydantic validation + content truncation	"I prevent bad data from corrupting the system"
Resilience	Retry logic with exponential backoff	"I understand distributed systems and flaky APIs"
Cost Awareness	Local LLM + content pruning + iteration limits	"I think about OpEx and prevent runaway costs"
Safety Guardrails	Max iterations prevent infinite loops	"$100k mistake prevented with 5 lines of code"
Observability	Structured JSON logging, thread isolation	"Remote debugging without VPN access"
Performance	ThreadPoolExecutor, 3x speedup	"I optimize latency without premature complexity"
Testing	Integration tests that verify behavior	"Most AI devs prompt-tweak; I verify programmatically"
Team Collaboration	Ruff linting, zero-friction code review	"I care about the team's codebase health"
System Design	StateGraph + HITL + checkpointing	"I architect for control, not just capability"

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🎯 What Senior Engineers Notice:

• You use Annotated[List, operator.add] (not just lists)
• You understand why async isn't always better than ThreadPoolExecutor
• You know the difference between "works" and "resilient"
• You measure cost, not just speed
• You test agent behavior, not just hope it works

📖 Learn More

• LangGraph: https://langchain-ai.github.io/langgraph/
• Pydantic: https://docs.pydantic.dev/
• Tavily: https://tavily.com/

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Production-ready. Remotely credible. MIT License. ✨