Types of Agents Available in GraphRAG Agent: A Complete Guide to RAG Strategies

The GraphRAG Agent repository provides six distinct agent types—Naïve RAG, Hybrid, Graph, Fusion, Deep Research, and a Multi-Agent Framework—each designed for specific retrieval-augmented generation strategies ranging from simple vector search to complex multi-step reasoning workflows.

The 1517005260/graph-rag-agent repository offers a comprehensive suite of purpose-built agents that implement various retrieval-augmented generation (RAG) strategies. These agents cover everything from straightforward retrieval-then-generation pipelines to sophisticated knowledge graph traversal and multi-agent orchestration systems. All agent implementations inherit from the common abstract base class BaseAgent defined in graphrag_agent/agents/base.py and reside under the graphrag_agent/agents/ directory, providing a unified run(query: str) -> str interface.

Overview of the GraphRAG Agent Architecture

Every agent in the GraphRAG Agent ecosystem extends the BaseAgent abstract class located at graphrag_agent/agents/base.py. This base class standardizes configuration loading, logging, caching, and the primary execution interface across all implementations. The six agent types range from simple, latency-optimized pipelines to complex, multi-step reasoning systems that leverage external knowledge graphs and composable orchestration frameworks.

Naïve RAG Agent: Simple Retrieval-Generation Pipelines

The Naïve RAG Agent (graphrag_agent/agents/naive_rag_agent.py) implements the most straightforward RAG strategy: retrieve relevant documents from a vector store, then prompt a language model with the retrieved context. This agent prioritizes low latency and minimal complexity, making it ideal for rapid prototyping and applications where reasoning depth is less critical than response speed.

from graphrag_agent.agents.naive_rag_agent import NaiveRagAgent

agent = NaiveRagAgent()
answer = agent.run("What are the key challenges in graph‑based RAG?")
print(answer)

Hybrid Agent: Combining Vector Search and LLM Reasoning

The Hybrid Agent (graphrag_agent/agents/hybrid_agent.py) merges local vector-store search capabilities with external LLM-driven reasoning modules. This dual approach allows the system to leverage fast lexical and semantic matches while simultaneously employing the language model for deeper contextual understanding and synthesis. Use this agent when your application requires both speed and nuanced comprehension of complex queries.

from graphrag_agent.agents.hybrid_agent import HybridAgent

agent = HybridAgent()
answer = agent.run("Explain the difference between vector search and graph traversal.")
print(answer)

Graph Agent: Neo4j Knowledge Graph Integration

The Graph Agent (graphrag_agent/agents/graph_agent.py) specializes in knowledge-graph-centric RAG by leveraging a Neo4j database to retrieve contextual entities and their relationships before prompting the LLM. This agent excels in domains where understanding the connections between entities—such as citation networks, organizational hierarchies, or biomedical relationships—is crucial for generating accurate responses.

from graphrag_agent.agents.graph_agent import GraphAgent

agent = GraphAgent()
answer = agent.run("Find recent research on graph neural networks.")
print(answer)

Fusion Agent: Multi-Backend Retrieval Merging

The Fusion Agent (graphrag_agent/agents/fusion_agent.py) implements a retrieval fusion strategy that aggregates results from multiple heterogeneous backends—including vector stores, knowledge graphs, and external APIs—into a single, coherent prompt. This approach maximizes answer coverage by combining complementary information sources, making it ideal for complex domains where no single retrieval method captures the full context.

from graphrag_agent.agents.fusion_agent import FusionAgent

agent = FusionAgent()
answer = agent.run("Summarize the latest developments in retrieval‑augmented generation.")
print(answer)

Deep Research Agent: Multi-Step Reasoning Workflows

The Deep Research Agent (graphrag_agent/agents/deep_research_agent.py) executes a sophisticated "research-plan-execute-reflect" loop, invoking specialized search tools and a dedicated reasoning module to perform iterative investigation. Unlike simpler agents that perform single-pass retrieval, this agent tracks evidence, generates citations, and refines its understanding through multiple rounds of analysis—making it suitable for in-depth investigations and academic research tasks.

from graphrag_agent.agents.deep_research_agent import DeepResearchAgent

agent = DeepResearchAgent()
answer = agent.run("How can I build a knowledge graph from unstructured PDFs?")
print(answer)

Multi-Agent Framework: Composable Orchestration Architecture

The Multi-Agent Framework (graphrag_agent/agents/multi_agent/) provides a composable architecture that powers the high-level agents while enabling custom pipeline construction. This framework consists of modular components including an Orchestrator (orchestrator.py), BasePlanner (planner/base_planner.py), ResearchExecutor (executor/research_executor.py), and BaseReporter (reporter/base_reporter.py).

You can assemble these components to build bespoke workflows or experiment with new reasoning strategies beyond the pre-built agent implementations.

from graphrag_agent.agents.multi_agent.orchestrator import Orchestrator
from graphrag_agent.agents.multi_agent.planner.base_planner import BasePlanner
from graphrag_agent.agents.multi_agent.executor.research_executor import ResearchExecutor
from graphrag_agent.agents.multi_agent.reporter.base_reporter import BaseReporter

# Assemble components (you can swap any concrete implementation)

planner = BasePlanner()
executor = ResearchExecutor()
reporter = BaseReporter()
orchestrator = Orchestrator(planner, executor, reporter)

answer = orchestrator.run("Investigate the impact of LLMs on software engineering.")
print(answer)

Summary

• GraphRAG Agent provides six distinct agent implementations, ranging from simple vector retrieval to complex multi-step research workflows.
• All agents inherit from BaseAgent (graphrag_agent/agents/base.py) and implement the unified run(query: str) -> str interface.
• The Naïve, Hybrid, Graph, Fusion, and Deep Research agents provide ready-to-use RAG strategies for different latency and reasoning requirements.
• The Multi-Agent Framework (graphrag_agent/agents/multi_agent/) offers composable components—Orchestrator, Planner, Executor, and Reporter—for building custom pipelines.

Frequently Asked Questions

What is the difference between the Naïve RAG Agent and the Hybrid Agent?

The Naïve RAG Agent performs a single-pass retrieval from a vector store followed by immediate generation, optimizing for low latency and simple use cases. The Hybrid Agent combines this vector search with external LLM-driven reasoning modules, enabling deeper contextual understanding at the cost of increased computational overhead.

When should I use the Graph Agent instead of the Fusion Agent?

Choose the Graph Agent when your domain relies heavily on entity relationships stored in a Neo4j knowledge graph, such as citation networks or organizational hierarchies. Opt for the Fusion Agent when you need to aggregate information from heterogeneous sources—combining vector stores, knowledge graphs, and external APIs—to maximize answer coverage across complex domains.

How does the Deep Research Agent differ from other agent types?

Unlike single-pass agents, the Deep Research Agent implements a multi-step "research-plan-execute-reflect" loop that iteratively refines its understanding, tracks evidence, and generates citations. This agent is designed for in-depth investigations requiring iterative refinement rather than quick question-answering tasks.

Can I combine multiple agents in a single workflow?

Yes, the Multi-Agent Framework (graphrag_agent/agents/multi_agent/) provides composable components including the Orchestrator, Planner, Executor, and Reporter that allow you to assemble custom pipelines. You can mix and match these components or extend the base classes to create bespoke workflows that leverage multiple retrieval strategies simultaneously.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}