Open Source Neo4j Alternatives

Written from scratch in Rust, HelixDB is a graph-vector database for knowledge graphs and AI memory. The argument is consolidation. Rather than an application database, a vector store, a graph store and glue code between them, agents get federated access to company data from one engine.

The primary data model is graph plus vector, with key-value, document and relational data supported alongside. Queries are authored in a DSL for Rust, TypeScript, Go and Python, all producing the same JSON abstract syntax tree and posted to a running instance over one HTTP query endpoint, with no build or deploy step.

Getting an instance running is the CLI's job.

• Install script: a shell one-liner on macOS and Linux, a PowerShell command on Windows, and an update subcommand afterwards.
• Interactive bootstrap: the chef command scaffolds a project, starts a local instance, seeds example data and installs the query skills and docs server.
• Agent handoff: chef detects Claude Code, OpenAI Codex, OpenCode or Cursor Agent and can hand over a one-line description to build a working app.
• SDK packages: published releases on crates.io, npm, PyPI and the Go module proxy.
• Local defaults: the dev instance listens on port 6969, which is where every SDK example points.

Version names deserve care, since HelixDB v3 is current while the wire endpoint keeps a v2 path meaning something else. A managed cloud runs the same engine on object storage with full ACID transactions, a single writer with auto-scaling readers and high availability, and the CLI authenticates against it.

Because FalkorDB holds graphs as sparse adjacency matrices, its queries run as linear algebra. The project describes itself as the first queryable property graph database to represent adjacency this way, with low latency for knowledge graphs that feed large language models as the stated goal.

Data follows the property graph model, so nodes and relationships carry attributes, and queries are written in openCypher with extensions of the project's own. A single container starts the database together with a browser interface, and any Redis client can send its graph commands directly.

• Multi-tenancy: one instance holds many graphs, each selected by name from the client.
• Official clients: Python, Java, Node, Rust and Go libraries are maintained by the project itself.
• Browser UI: a visualization interface is reachable on a local port once the container is running.
• Vector search: graph traversal combines with vector similarity for agent memory and recommendations.
• GraphRAG SDK: a companion project builds retrieval applications on top of the database.

The use cases the project names are GraphRAG, agentic AI, chatbots, fraud detection, security graphs and AI retrieval, all places where the relationships matter as much as the records.

The code is published under the Server Side Public License, a managed cloud runs alongside the self-hosted build, and demos in the repository give a first look at the engine.

Memgraph is an in-memory graph database built in C and C++, aimed at workloads where a multi-hop traversal has to return in under a millisecond. It serves as the graph engine for GraphRAG pipelines, AI memory systems and agentic workflows, and equally for fraud detection, network analysis and infrastructure monitoring.

What makes the retrieval side work is that no second system is required. Vector and text indexes sit in the same query layer as graph traversal, so similarity search, graph expansion and ranking run as one atomic database operation. The query language is Cypher, compatible with Neo4j, and the database is ACID-compliant.

Several capabilities extend the core engine.

• MAGE library: more than forty graph algorithms in C++, Python and CUDA, from PageRank and community detection to link prediction and embeddings.
• Deep-path traversals: accumulators and path filtering handled in the query rather than in application code.
• Custom query modules: the engine is extended natively with Python, Rust or C and C++.
• Streaming ingestion: Kafka, Pulsar and Redpanda feed dynamic algorithms that react to changes as they arrive.
• Schema introspection: one command returns the full graph ontology for Text2Cypher and agent integration.

Data loads natively from Parquet and JSONL on local disk, S3 or HTTP endpoints. Installation covers Docker on Windows, macOS and Linux, native Linux packages, and official Helm charts for standalone or high-availability Kubernetes clusters. Raft-based failover, multi-tenancy, label-level access control and SSO belong to the enterprise build, and a hosted playground lets you try the query language without installing anything.