Literature Review Agent

An autonomous multi-agent system built with LangGraph that generates comprehensive literature reviews from academic papers. Given a research topic, it searches arXiv, plans the review structure, retrieves and synthesizes relevant papers, and produces a grounded, citation-verified literature survey.

Architecture

The system consists of two main sub-agents orchestrated by a parent graph:

Planning Agent

• Refines the research topic through human interaction
• Searches arXiv and web for relevant papers
• Creates a structured plan with sections and key points
• Maps papers to specific subsections

Refinement Agent

• Processes each subsection sequentially
• Downloads papers and creates vector embeddings (ChromaDB)
• Generates content using RAG with relevant paper fragments
• Reviews content quality and grounding/citations
• Iteratively refines until quality standards are met

Key Features

• RAG-based writing: Uses vector similarity search to retrieve relevant paper fragments
• Citation verification: Validates that claims are grounded in cited papers
• Iterative refinement: Content and grounding review loops until quality passes
• Tool access: LLM can search paper fragments during verification
• Parallel processing: Papers are processed concurrently where possible

Graph Diagrams

Main Graph

Planning Sub-Graph

Refinement Sub-Graph

Installation

# Clone the repository
git clone <repo-url>
cd langgraph-research

# Install dependencies with Poetry
poetry install

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys:
# - OPENROUTER_API_KEY (required)
# - TAVILY_API_KEY (for web search)

Usage

# Run the literature review agent
poetry run python main.py

You'll be prompted to enter:

1. Research topic
2. Paper recency filter (e.g., "last 5 years")

The agent will then:

1. Search for relevant papers and create a review plan
2. Generate each subsection with proper citations
3. Review and refine content until quality standards are met
4. Output the final literature survey

Project Structure

agents/
├── graph.py                    # Main orchestration graph
├── planning_agent/
│   ├── graph.py               # Planning subgraph
│   ├── agent_config.py        # Planning agent configuration
│   ├── nodes/                 # Planning nodes (refine topic, search, plan)
│   ├── tools.py               # arXiv search, web search, human input
│   └── prompts.py             # Planning prompts
├── refinement_agent/
│   ├── graph.py               # Refinement subgraph
│   ├── agent_config.py        # Refinement agent configuration
│   ├── nodes/                 # Writing, review, feedback, state management
│   ├── tools.py               # Paper fragment search tool
│   └── prompts.py             # Writing and review prompts
└── shared/
    ├── main_config.py         # Base configuration dataclass
    ├── state/                 # Pydantic state models
    └── utils/                 # LLM utils, callbacks, logging

data/
├── database/                  # SQLite for paper/review tracking
├── vector_store/              # ChromaDB vector collections
└── temp_cache/                # Temporary paper downloads

graph_diagrams/                # Generated workflow diagrams

Configuration

Key settings in agents/shared/main_config.py:

• orchestrator_model: Main LLM for complex tasks
• text_model: LLM for content generation
• embedding_model: Model for vector embeddings

Agent-specific settings in agents/*/agent_config.py.