GraphRAG Agent Search Strategies: 8 Methods for Knowledge Graph Retrieval

The GraphRAG Agent implements eight distinct search strategies—Local, Global, Hybrid, Deep Research, Deeper Research, Chain Exploration, and Validation—that operate as modular tools within the graphrag_agent/search/ layer.

The 1517005260/graph-rag-agent repository provides a modular search architecture where each strategy is encapsulated as a tool class following a unified interface. These tools are registered in graphrag_agent/search/tool_registry.py and consumed by specialized agents to balance speed, coverage, and reasoning depth depending on the query complexity.

Overview of GraphRAG Agent Search Strategies

Each search strategy in the GraphRAG Agent is implemented as a Python class exposing search(), name, and description attributes. This design allows the HybridAgent, DeepResearchAgent, and other agent implementations to import only the retrieval tools they require.

The strategies reside in graphrag_agent/search/ and are divided between core retrieval logic (e.g., local_search.py, global_search.py) and tool wrappers that expose them to the agent framework (e.g., tool/hybrid_tool.py).

The 8 Core Search Strategies

Local Search

Local Search retrieves nodes and edges directly reachable from the user’s query using the graph’s built-in index, leveraging vector similarity or keyword matching. This strategy is optimized for fast, low-latency lookups when the answer is expected to reside in the immediate neighborhood of the query terms.

Implementation: graphrag_agent/search/local_search.py

Global Search

Global Search executes a traversal over the entire knowledge graph, optionally scoring nodes with LLM-driven relevance signals. Use this strategy when the answer may be far from the query entities or when broader contextual coverage is required.

Implementation: graphrag_agent/search/global_search.py

Hybrid Search

Hybrid Search combines the results of Local and Global searches, de-duplicates the merged set, and re-ranks them according to composite relevance scores. This approach delivers the speed of local retrieval with the safety net of global coverage.

Implementation: graphrag_agent/search/tool/hybrid_tool.py

Deep Research

Deep Research performs a multi-hop expansion (default depth = 2) using the graph’s relationships, then runs LLM-based summarization on the collected sub-graph. This strategy is suitable for complex questions requiring explanatory chains of reasoning across several hops.

Implementation: graphrag_agent/search/tool/deep_research_tool.py

Deeper Research

Deeper Research extends Deep Research with configurable depth greater than 2 and richer prompt engineering for the LLM summarization stage. Deploy this when the required knowledge spans many layers of the graph, such as multi-step scientific reasoning.

Implementation: graphrag_agent/search/tool/deeper_research_tool.py

Chain Exploration

Chain Exploration executes a chain-of-thought style search: it prompts the LLM to suggest the next sub-query, runs a fresh search for that sub-query, and repeats until a stopping condition is met. This is ideal for open-ended queries where the optimal retrieval path is not known a priori.

Implementation: graphrag_agent/search/tool/chain_exploration_tool.py

Validation

Validation runs a secondary check after a primary search finishes, usually a cheaper local search, to confirm the relevance of the top results. This adds robustness to downstream answer generation, particularly when mitigating LLM hallucinations.

Implementation: graphrag_agent/search/tool/validation_tool.py

How Search Tools Are Registered and Used

All search strategies are registered in graphrag_agent/search/tool_registry.py, which provides a central catalog for agent construction. Agents such as HybridAgent and DeepResearchAgent import specific tools from this registry rather than implementing retrieval logic themselves.

from graphrag_agent.search.tool_registry import register_all
from graphrag_agent.agents.hybrid_agent import HybridAgent

# Register all available tools

register_all()

# Initialize agent with automatically registered tools

agent = HybridAgent()
answer = agent.run(query="What are the environmental impacts of electric vehicle battery production?")
print(answer)

Practical Code Examples

Running a Single Strategy

To execute a standalone search without an agent wrapper, initialize the specific tool class with a RetrievalAdapter:

from graphrag_agent.search.tool.hybrid_tool import HybridSearchTool
from graphrag_agent.search.retrieval_adapter import RetrievalAdapter

# Initialize adapter (handles vector store / Neo4j connection)

adapter = RetrievalAdapter()

# Create the tool

hybrid = HybridSearchTool(adapter)

# Perform search

results = hybrid.search(query="How does photosynthesis convert light into chemical energy?")
for node in results:
    print(node.id, node.title)

Switching Strategies Dynamically

You can implement fallback logic by switching between fast local and exhaustive global search based on initial result quality:

from graphrag_agent.search.tool.local_search_tool import LocalSearchTool
from graphrag_agent.search.tool.global_search_tool import GlobalSearchTool

adapter = RetrievalAdapter()
local_tool = LocalSearchTool(adapter)
global_tool = GlobalSearchTool(adapter)

# Attempt fast local search first

hits = local_tool.search("Explain quantum tunnelling")
if not hits:
    # Fallback to slower but exhaustive global search

    hits = global_tool.search("Explain quantum tunnelling")

Summary

• The GraphRAG Agent provides eight modular search strategies: Local, Global, Hybrid, Deep Research, Deeper Research, Chain Exploration, and Validation.
• Each strategy is implemented as a tool class in graphrag_agent/search/ with a unified interface exposing search(), name, and description.
• Tools are registered centrally in graphrag_agent/search/tool_registry.py and consumed by specialized agents such as HybridAgent and DeepResearchAgent.
• Developers can mix strategies to balance latency versus coverage, or chain them for multi-hop reasoning and validation.

Frequently Asked Questions

What is the difference between Deep Research and Deeper Research in GraphRAG Agent?

Deep Research performs a fixed multi-hop expansion with a default depth of 2 and summarizes the resulting sub-graph, while Deeper Research extends this with configurable depth greater than 2 and enhanced prompt engineering for complex, multi-layer reasoning tasks.

How do I choose between Local Search and Global Search?

Use Local Search when you need fast, low-latency retrieval and expect the answer to be in the immediate neighborhood of your query entities. Use Global Search when the answer might be distant in the graph or when you need comprehensive coverage across the entire knowledge base, accepting higher latency for better recall.

Can I combine multiple search strategies in a single query?

Yes, the Hybrid Search strategy explicitly combines Local and Global search results, de-duplicating and re-ranking them. Additionally, you can manually chain tools in your own agent logic—such as running a Local Search first and falling back to Global Search if no results are found, or adding a Validation step after Deep Research.

Where are the search tools registered in the codebase?

All search tools are registered in graphrag_agent/search/tool_registry.py. This registry provides a central catalog that agents use to instantiate their tool sets. The register_all() function makes all strategies available to agents like HybridAgent and DeepResearchAgent without requiring manual imports of each tool class.

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