graph-retriever
Retriever combining unstructured similarity and structured document traversal.
Decision gist · record as of 2026-08-14
Yes, if you need hybrid retrieval combining vector similarity with metadata graph traversal. The low install friction, permissive license, and active repository make it a reasonable choice for RAG applications. However, the package is in Beta with limited recent activity (last release April 2025), so verify that your vector store is supported and test thoroughly before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; you must have an existing vector store instance to wrap.
- Low friction installation with a small, stable dependency footprint: immutabledict, numpy, pytest, and typing-extensions.
- The package is in Beta status and has not released since April 2025, so maintenance activity is limited but the repository remains active.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but no reciprocal licensing is required.
last release 2025-04-04 (497 days) · last repo commit 2025-05-05 · 91 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 187,078 downloads/mo, #9,971 on PyPI
Alternatives
Verify before relying
pip install graph-retriever
from graph_retriever import GraphRetriever
retriever = GraphRetriever(
store=your_vector_store,
edges=[("mentions", "id"), "entities"]
)
results = retriever.invoke("your query")- Which vector store implementations are currently supported beyond the examples shown in the description.
- Whether the package works with vector stores outside the LangChain ecosystem.
- Performance characteristics and scalability limits for large graphs or high-cardinality metadata.
What it is and what it does
Graph Retriever is a Python library that extends vector stores with graph-based retrieval capabilities. It wraps existing vector stores to enable traversal of metadata relationships—such as following mentions or entity links—alongside traditional similarity search. This hybrid approach lets you retrieve documents based on both semantic similarity and structured connections in your metadata.
The library is designed for Retrieval-Augmented Generation (RAG) applications where you want to combine the strengths of vector search (finding semantically similar content) with graph traversal (following explicit relationships between documents). It supports Python 3.10 and later and depends on immutabledict, numpy, pytest, and typing-extensions. A separate LangChain integration package is available for applications already using LangChain.
Use it for
- Retrieve documents by following entity mentions and relationships in knowledge graphs alongside semantic similarity.
- Build RAG systems that navigate document metadata hierarchies to surface contextually related content.
- Combine vector search with structured metadata properties to refine retrieval results in domain-specific applications.
- Extend existing vector stores with graph traversal without replacing the underlying store infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need hybrid retrieval combining vector similarity with metadata graph traversal.
The low install friction, permissive license, and active repository make it a reasonable choice for RAG applications. However, the package is in Beta with limited recent activity (last release April 2025), so verify that your vector store is supported and test thoroughly before production use.
Install
graph-retriever on PyPI
Before you install
Low friction installation with a small, stable dependency footprint: immutabledict, numpy, pytest, and typing-extensions. The package is in Beta status and has not released since April 2025, so maintenance activity is limited but the repository remains active.
Requires Python 3.10 or later; you must have an existing vector store instance to wrap.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but no reciprocal licensing is required.
Quickstart
pip install graph-retriever
from graph_retriever import GraphRetriever
retriever = GraphRetriever(
store=your_vector_store,
edges=[("mentions", "id"), "entities"]
)
results = retriever.invoke("your query")Verify before relying
- Which vector store implementations are currently supported beyond the examples shown in the description.
- Whether the package works with vector stores outside the LangChain ecosystem.
- Performance characteristics and scalability limits for large graphs or high-cardinality metadata.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesimmutabledictnumpypytesttyping-extensions |
| Maintenance | Aging 497 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 187,078 / month, #9,971 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development :: Libraries :: Python Modules |
Evidence: graph_retriever-0.8.0-py3-none-any.whl
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See also langchain-graph-retriever · ragstack-ai-knowledge-store · neo4j-graphrag · nucliadb-utils · graphrag · llama-index-vector-stores-faiss · langchain-milvus · kuzu · farm-haystack · real-ladybug