# GraphRAG Agent Search Strategies: 8 Methods for Knowledge Graph Retrieval

> Explore 8 GraphRAG Agent search strategies including Local, Global, Hybrid, and Deep Research for efficient knowledge graph retrieval. Optimize your agent's knowledge access.

- Repository: [GLK/graph-rag-agent](https://github.com/1517005260/graph-rag-agent)
- Tags: deep-dive
- Published: 2026-02-22

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**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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/local_search.py), [`global_search.py`](https://github.com/1517005260/graph-rag-agent/blob/main/global_search.py)) and tool wrappers that expose them to the agent framework (e.g., [`tool/hybrid_tool.py`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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.

```python
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`:

```python
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:

```python
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`](https://github.com/1517005260/graph-rag-agent/blob/main/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`](https://github.com/1517005260/graph-rag-agent/blob/main/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.