5 mins
Zep Alternatives
Nishkarsh Srivastava
Updated on :
As AI agents move from experiments into production, engineering teams need infrastructure that can preserve state, represent relationships, track changing facts, and retrieve useful context across sessions. Zep is one option for agent memory, but it is not the only architecture available. Teams may also want greater control over deployment, data modeling, retrieval logic, or the underlying database.
This guide examines six Zep alternatives for different technical priorities. HydraDB ranks first because it provides a graph database and context infrastructure layer rather than limiting developers to a predetermined memory abstraction. That broader foundation can support AI agent memory, ontologies, company brains, agentic actions, and other stateful AI workflows.
Key Takeaways
HydraDB is the strongest overall alternative for graph-native context infrastructure. It combines object-storage architecture, temporal versioning, graph structure, and hybrid retrieval in one system while giving developers control over their memory and context design.
Different products solve different layers of the stack. Some alternatives provide managed memory APIs or agent runtimes, while HydraDB provides the graph database beneath those experiences.
Benchmark scores require context. Model choice, evaluators, retrieval settings, dataset handling, and scoring protocols can materially affect results. Cross-vendor scores should not be treated as a direct ranking unless the test conditions match.
Deployment choices vary. Open-source availability, managed cloud, licensed self-hosting, BYOC, and support terms are separate considerations that teams should evaluate independently.
High-quality context requires more than semantic similarity. Relationships, time, metadata, keyword matching, and reranking can all affect whether an agent receives the information it needs.
1. HydraDB
HydraDB is a graph database built on object storage and purpose-built for modern AI workloads. Its current positioning is “The Graph AI Runs On.” The company has also announced that HydraDB is now open source.
HydraDB provides infrastructure for teams building agent memory, ontologies, company brains, context graphs, agentic actions, and enterprise knowledge systems. Developers retain control over graph structure, memory primitives, retrieval logic, ranking settings, and the context passed to their chosen language model.
HydraDB reports $6.5 million raised, more than one billion documents ingested, approximately one million retrievals per month, and adoption by 2,000 developers. These figures are company-reported.
Key Features
Object-storage architecture: HydraDB uses tiered storage with hot context in memory, warm context on NVMe storage, and cold context in object storage. This design is intended to make large graphs more economical without discarding older information.
Git-style temporal versioning: Time-aware graph state helps applications distinguish current facts from earlier or superseded information. This is useful for changing preferences, account histories, evolving codebases, and organizational decisions. Learn more about temporal graphs.
Hybrid retrieval: HydraDB combines semantic retrieval, BM25 matching, graph relationships, temporal signals, metadata filtering, query expansion, and reranking instead of relying on embeddings alone.
Context graph construction: During ingestion, HydraDB can parse and chunk information, create embeddings, identify entities, resolve relationships, detect temporal signals, and build a connected graph.
Data isolation: Databases and collections help scope information by customer, user, workspace, department, or environment. Metadata filters can narrow the information eligible for retrieval.
Workplace connectors: HydraDB supports connectors for Slack, GitHub, Linear, Notion, and Gmail with synced metadata.
Developer interfaces: Teams can work through official Python and TypeScript or Node.js SDKs as well as a REST API.
Observability and provenance: HydraDB includes an observability and traces dashboard. Its published methodology also describes provenance linking retrieved facts to source information, timestamps, and extraction confidence.
HydraDB benchmark results
In company-conducted evaluations, HydraDB reports:
LongMemEval-s overall: 90.79% using Gemini 3.0 Pro
Preference extraction: 96.67%
Knowledge updates: 97.43%
Temporal reasoning: 90.97%
BEAM 1M overall: 82%
FinanceBench thinking mode: 91.4% Recall@10
These are HydraDB-published benchmark results, not universal production guarantees. The FinanceBench figure measures retrieval Recall@10 rather than answer accuracy. The LongMemEval-s category scores evaluate the complete HydraDB system and should not be attributed to one isolated feature.
Other vendors publish results from their own evaluation setups. Those scores should not be treated as like-for-like comparisons unless the answer model, evaluator, retrieval settings, dataset handling, and scoring protocol are confirmed to match. Teams can use this guide to evaluate memory systems more consistently.
Pricing and Deployment
HydraDB currently presents storage-based pricing with no per-seat, per-feature, or API-call limits listed on its public tiers:
Ship: Free, with unlimited API calls and tenants, multi-tenancy, an observability and traces dashboard, and community support
Surge: $25 per month, including up to 2 GB of graph storage, $0.50 per GB per month in overage, a private Slack channel, SOC 2 and GDPR reports, and a DPA
Scale: $399 per month, including up to 10 GB of graph storage, $0.25 per GB per month in overage, dedicated infrastructure, and a self-host license option
Enterprise: Custom pricing for BYOC or fully self-hosted deployment, with an account manager and support and uptime SLAs
HydraDB also offers managed cloud deployment. Because the homepage now announces an open-source release, teams should confirm how the open-source project, licensed deployment options, managed service, and commercial support apply to their planned environment.
Best For
HydraDB is best suited to teams that need:
A graph-native foundation rather than a fixed memory application
Persistent, relationship-aware context across sessions
Time-aware knowledge that preserves earlier states
Hybrid semantic, lexical, relational, temporal, and metadata retrieval
Control over graph structure, ranking, and context assembly
A unified system that can reduce the need to coordinate separate vector, graph, relational, and file-storage components
Support for use cases such as coding assistant memory, customer support, sales, research intelligence, and other stateful applications
HydraDB states that it is SOC 2 and ISO 27001 certified. Its Surge plan includes SOC 2 and GDPR reports plus a DPA. Organizations handling sensitive or regulated data should still confirm deployment-specific requirements, including data residency, private networking, encryption, authorization, audit logging, and applicable regulatory obligations.
2. Mem0
Mem0 provides managed and open-source tools for adding persistent memory to AI applications. Its platform organizes memories around entities such as users, agents, applications, and runs, making it approachable for teams that want a dedicated memory interface.
The managed platform includes native graph memory that extracts entities, connects memories through shared entities, and combines graph signals with semantic and keyword scores. This offers a relatively direct path to entity-aware retrieval without requiring teams to operate a separate graph database for the managed feature.
Key Features
Memory APIs for adding, searching, updating, and deleting memories
Entity-scoped organization for users, agents, applications, and runs
Semantic, keyword, and graph-informed retrieval in the managed platform
Integrations with common agent frameworks
An open-source option for teams that want to manage their own deployment
Best For
Mem0 is a practical choice for rapid prototyping, applications centered on user or agent memories, and teams that prefer a dedicated memory API. HydraDB is the stronger fit when the requirement extends beyond memory into developer-controlled graph infrastructure, temporal knowledge, ontologies, or broader enterprise context.
3. Letta
Letta, which grew from the MemGPT project, centers on stateful agents that can manage information through editable memory blocks. Its approach treats memory as part of the agent runtime rather than only as an external retrieval service.
Key Features
Editable memory blocks that agents can update through tools
Persistent agent state and conversation history
An agent runtime for managing long-lived behavior
Open-source software and a managed service
An architecture influenced by operating-system approaches to memory management
Best For
Letta is best for teams that want to build around a stateful agent runtime and allow agents to participate directly in memory management. HydraDB is preferable when teams want a graph database that can serve multiple agent frameworks and context architectures without prescribing the agent runtime.
4. Cognee
Cognee focuses on turning source data into structured knowledge through ingestion and graph-construction pipelines. It is oriented toward teams that want to process multiple data types and create connected representations that can support retrieval.
Key Features
Data ingestion and transformation pipelines
Entity and relationship extraction
Knowledge graph construction
Support for text and other data modalities
Configurable processing workflows
Best For
Cognee can fit teams that prioritize flexible data processing and automated knowledge graph creation. HydraDB is better aligned with teams that need the graph database itself, object-storage economics, temporal versioning, and a unified retrieval layer for production AI workflows.
5. Hindsight
Hindsight is an open-source memory system designed for self-hosted operation. It combines several retrieval strategies and includes a reflection process for consolidating information over time.
Key Features
Docker-based self-hosting
PostgreSQL-backed storage
Semantic, keyword, graph, and temporal retrieval strategies
Reflection for memory consolidation
A developer-oriented API
Hindsight and HydraDB benchmark context
Hindsight separately reports a 94.6% LongMemEval-s result through the Agent Memory Benchmark, while HydraDB reports 90.79% using Gemini 3.0 Pro. These vendor-published scores should not be treated as a direct ranking unless the models, evaluators, retrieval settings, dataset handling, and scoring protocols are shown to be equivalent.
Best For
Hindsight is a reasonable option for teams prioritizing a self-hosted memory service and a straightforward local deployment path. HydraDB is the more complete choice for teams seeking a graph database and context infrastructure layer that can support memory alongside ontologies, enterprise knowledge, context graphs, and agentic actions.
6. Supermemory
Supermemory provides APIs for adding content, searching stored information, and supplying memory to AI applications. Its focus is a developer-friendly memory service rather than a general-purpose graph database.
Key Features
APIs for content ingestion and search
User-scoped memory for personalized applications
Retrieval designed for AI application context
Integrations for common application workflows
Best For
Supermemory may suit teams that want a focused memory API and a quick integration path. HydraDB is the stronger option where relationships, evolving state, temporal history, developer-defined graphs, and multi-signal retrieval are central requirements.
Why Teams Consider Zep Alternatives
Zep builds temporal context graphs for agent memory and remains a relevant option. Teams still evaluate alternatives because architecture, deployment, control, and cost models vary substantially.
Self-Hosting and Infrastructure Control
Zep ended support for its Community Edition in April 2025. Teams that depended on that edition may need a different self-hosting path or may want infrastructure whose deployment model they can control more directly.
HydraDB lists a self-host license option under Scale and BYOC or fully self-hosted deployment under Enterprise. Its homepage also announces that the database is open source. These are distinct from one another, so buyers should confirm the license, hosting, support, and operational responsibilities for their chosen setup.
Database Versus Memory Application
Some products expose opinionated memory objects and workflows. HydraDB instead provides graph database primitives that teams can use to build their own memory layer, company brain, ontology, or context graph.
This distinction matters when an application needs more than conversational recall. A graph database can model products, accounts, services, tickets, decisions, dependencies, events, and other domain entities while supporting multiple applications above the same knowledge infrastructure.
Retrieval and Context Control
Vector similarity can identify semantically related content, but similarity alone does not capture every relevant relationship. Stateful applications may also need time, entity connections, exact terms, metadata, previous outcomes, and business rules. HydraDB’s approach reflects the idea that relevance needs relationships.
Pricing Predictability
Usage units, stored-memory counts, credits, seats, and storage can produce different cost profiles. HydraDB’s public pricing is primarily storage-based and lists unlimited API calls and tenants. Teams should model expected storage, ingestion, retrieval, model, connector, and operational costs before selecting any platform.
How to Choose a Zep Alternative
The most useful comparison starts with the layer your team needs.
Choose HydraDB When
Choose HydraDB when you need a graph database for AI workflows, want to control the memory and context architecture, or must retrieve across relationships and changing state. It is especially relevant when one infrastructure layer must support agent memory, ontologies, company knowledge, context engineering, and agentic actions.
Choose Mem0 When
Choose Mem0 when a dedicated managed memory API, entity-scoped memory, and broad framework integrations are the main priorities.
Choose Letta When
Choose Letta when the application is designed around long-lived agents that edit their own memory within a stateful runtime.
Choose Cognee When
Choose Cognee when diverse ingestion and knowledge-processing pipelines are more important than selecting a standalone graph database.
Choose Hindsight When
Choose Hindsight when a self-hosted memory service and a Docker-oriented local deployment are the primary requirements.
Choose Supermemory When
Choose Supermemory when the team wants a focused memory API for content ingestion, user memory, and retrieval.
Deployment, Security, and Governance
Deployment labels do not by themselves establish that a platform is appropriate for regulated data. Before adopting any alternative, confirm:
Where data is stored and processed
Whether private networking or BYOC is available
How encryption keys are managed
How users, services, databases, and collections are authorized
What audit and API logs are available
Which certifications and contractual reports apply
Whether deletion, retention, and residency controls meet organizational requirements
HydraDB provides database and collection isolation plus metadata filtering, which can reduce custom scoping logic. These features should not be described as complete enterprise authorization. Teams should confirm their access-control requirements directly with HydraDB.
Observability and Provenance
HydraDB includes an observability and traces dashboard. Its research methodology also describes provenance that links retrieved facts to their original sources, timestamps, and extraction confidence. This can help teams inspect how context was assembled and trace information used by an agent.
Claims about token-consumption tracking, detailed latency metrics in the dashboard, or OpenTelemetry support should not be made unless HydraDB provides current product documentation for those features. Teams building governed systems can also review decision traceability as part of their evaluation.
Frequently Asked Questions
What is the best Zep alternative for graph-native AI context?
HydraDB is the strongest choice when the requirement is a graph database and context infrastructure layer rather than only a packaged memory service. It supports temporal graph state, hybrid retrieval, object-storage architecture, and developer-controlled context design.
How does HydraDB differ from vector-first memory tools?
HydraDB represents connected entities, relationships, events, and changing facts in a graph while combining graph signals with semantic, BM25, temporal, metadata, expansion, and reranking signals. Vector similarity remains useful, but HydraDB does not treat it as the only definition of relevance. For a deeper explanation, see vector versus context.
Can HydraDB answer questions involving multiple relationships?
HydraDB context graphs represent relationships as source-relation-target triplets and can return multi-hop paths alongside retrieved chunks. This helps applications answer questions involving ownership, dependencies, sequences, and other connected context. The current context graph documentation explains the model.
HydraDB documentation supports multi-hop graph paths, but the product should not be described as executing every traversal in milliseconds without a workload-specific benchmark and methodology.
What deployment options does HydraDB offer?
HydraDB offers managed cloud deployment. Its public pricing lists a self-host license option on Scale and BYOC or fully self-hosted deployment on Enterprise. The homepage also announces that HydraDB is open source. Teams should confirm how the open-source release, commercial license, deployment model, and support agreement apply to their use case.
Is HydraDB suitable for regulated workloads?
HydraDB states that it is SOC 2 and ISO 27001 certified, and its Surge plan includes SOC 2 and GDPR reports plus a DPA. Suitability still depends on the specific deployment and the organization’s requirements for residency, networking, encryption, access control, logging, retention, and regulatory compliance. Teams evaluating enterprise AI memory should validate those controls before deployment.
How quickly can teams integrate HydraDB?
HydraDB states that integration takes under a day for most teams and that developers can run a first query in under ten minutes. These are company-reported estimates, and actual timelines depend on data preparation, integration scope, security review, and production requirements.
