Zep Reviews in 2026

AI agents that forget previous conversations can deliver inconsistent experiences and repeatedly reconstruct context that should persist across sessions. As production AI applications become more stateful, engineering teams increasingly need infrastructure that can retain facts, relationships, and changes over time.

Zep is one option in this category. It combines temporal context graphs with managed memory infrastructure designed for AI agents. However, evaluating Zep requires looking beyond its core retrieval capabilities to factors such as benchmark performance, pricing, deployment options, and operational requirements.

This review examines Zep's capabilities, limitations, pricing, and market position in 2026. It also considers how its approach compares with graph-native context infrastructure such as HydraDB for teams evaluating persistent memory for production AI applications.

Key Takeaways

• Zep focuses on temporal agent memory: Its context graph tracks evolving facts and relationships to help agents retrieve information across sessions.

• Benchmark results vary by model: Zep scores 63.8% with GPT-4o-mini and 71.2% with GPT-4o on LongMemEval-S.

• Pricing is credit-based: Zep offers a free plan with 10,000 monthly credits, while paid Flex plans start at $125 per month.

• Deployment options extend beyond managed cloud: Enterprise customers can access additional deployment models, including BYOC.

• HydraDB takes a graph-native approach: It combines semantic, lexical, relational, and temporal retrieval with storage-based pricing for persistent agent context.

• Architecture should match the workload: Temporal accuracy, deployment control, retrieval patterns, and cost structure should all factor into the decision.

Zep in 2026: An Overview of Its AI Agent Context Capabilities

Zep positions itself as a context platform for AI agents, combining knowledge graph construction with semantic search capabilities. The system emerged from Y Combinator's W24 batch and has developed an ecosystem around its Graphiti temporal graph framework.

The core value proposition centers on Zep's bi-temporal knowledge graph model. Instead of recording only when information enters a system, its architecture can track validity periods for relationships so applications can distinguish historical information from newer facts.

Key capabilities Zep provides include:

• Temporal fact tracking: Distinguishing between information that was previously valid and information that is currently valid

• Managed context assembly: Preparing retrieved context for use by LLM applications

• Session and user scoping: Organizing memory around users, conversations, or applications

• Hybrid retrieval: Combining vector search, keyword retrieval, and graph relationships

The managed platform reduces the infrastructure work required to deploy an agent memory layer compared with operating each underlying component independently.

S&P Global Market Intelligence has also provided analyst coverage of Zep and described the company as a likely candidate to become a de facto partner within the enterprise agent stack, according to a third-party analysis. This should be understood as an analyst assessment rather than a definitive statement about Zep's market position.

Comparing Zep's Temporal Context: LongMemEval-S Benchmarks and Accuracy

Benchmark performance provides one way to evaluate how memory systems handle recall as conversations grow and information changes. LongMemEval-S tests several forms of long-term conversational memory, including temporal reasoning, knowledge updates, preference retrieval, and cross-session recall.

According to the Zep research paper, Zep scores:

• 63.8% with GPT-4o-mini

• 71.2% with GPT-4o

The difference illustrates how the underlying language model can materially affect memory-system benchmark results.

For comparison, HydraDB reports 90.79% overall accuracy on LongMemEval-S and 97.43% accuracy on knowledge-update questions. These figures come from HydraDB's published benchmark results, so teams should consider differences in model selection, configuration, and testing methodology before treating the scores as direct product rankings.

Benchmark methodology considerations include:

• Different systems may use different LLM backends.

• Retrieval and configuration choices can materially affect results.

• LongMemEval-S measures different capabilities from benchmarks such as Deep Memory Retrieval.

• Vendor-published benchmark results should be tested against representative production workloads.

On the Deep Memory Retrieval benchmark, Zep's published research reports a 94.8% score with GPT-4-turbo, compared with 93.4% for the referenced MemGPT result.

The broader takeaway is that temporal memory performance depends on both the retrieval architecture and the models used around it. Applications that frequently encounter changing policies, preferences, account details, or technical information should test knowledge-update behavior specifically rather than relying on a single aggregate benchmark.

How HydraDB Fits Into a Zep Evaluation

HydraDB and Zep address a similar problem: giving AI agents persistent context that can evolve instead of treating every request as an isolated retrieval task. Their architectures, however, approach that problem differently.

Graph-Native Context

HydraDB is a graph database built on object storage and designed as a context infrastructure layer for AI agents. It represents entities, relationships, and historical state directly in a graph while supporting semantic search, BM25 retrieval, graph traversal, and temporal filtering.

This enables applications to retrieve context based on several signals at once:

• Semantic similarity between the request and stored information

• Explicit relationships among entities

• Exact lexical matches

• Changes in facts or relationships over time

Different Temporal Models

Zep uses a bi-temporal architecture that associates facts and relationships with validity information. This is useful when an application needs to reason about when specific information was valid.

HydraDB instead uses Git-style temporal versioning to preserve changes to graph state. That approach is intended for workloads where agents need persistent history alongside relationship-aware retrieval.

Choosing Between the Approaches

Neither architecture should be evaluated solely by benchmark scores. Engineering teams should also compare:

• Required temporal query patterns

• Retrieval latency

• Deployment control

• Multi-tenant isolation

• Operational overhead

• Pricing behavior as stored context grows

For applications where relationships and evolving context are central to agent behavior, both approaches warrant workload-specific testing before production adoption.

Zep's Role in Production AI Agents: Use Cases and Limitations

Production deployments show where memory architecture affects application behavior most directly.

Use cases suited to Zep's architecture include:

• Conversational agents requiring session context: Maintaining user information and conversational history across interactions

• Applications requiring temporal queries: Retrieving information based on when a fact or relationship was valid

• Teams preferring managed infrastructure: Reducing the amount of database infrastructure operated directly

• Organizations requiring enterprise deployment controls: Accessing additional security and deployment capabilities through enterprise plans

Considerations for production deployments:

Zep's managed approach reduces direct infrastructure administration, but organizations that require greater control over data location or infrastructure boundaries should evaluate its Enterprise deployment options against their internal requirements.

The credit model also requires teams to understand episode ingestion patterns. Credits are consumed based on the size of Episodes sent to Zep, while retrieval, storage, users, and graph storage are not separately metered under the current pricing model.

For agent memory in customer support, sales automation, coding assistants, and other persistent-context workloads, evaluation should focus on whether Zep's temporal model and consumption structure align with the application's actual memory lifecycle.

Understanding Zep's Pricing and Deployment Options in 2026

Zep currently uses a credit-based pricing model in which Episode ingestion determines credit consumption.

Current Zep pricing includes:

• Free: $0 with 10,000 credits per month

• Flex: $125 per month with 50,000 credits

• Flex Plus: $375 per month with 200,000 credits

• Enterprise: Custom pricing based on workload and deployment requirements

Flex includes additional credits at $25 per 10,000 credits, while Flex Plus charges $75 per additional 40,000 credits.

Zep states that retrieval, storage, threads, users, and graph storage are unmetered. Credits are primarily consumed according to the size of each Episode submitted for processing.

HydraDB's Storage-Based Model

HydraDB pricing follows a storage-based structure instead:

• Free: $0 per month with a 1 GB hosted sandbox

• Ship: $25 per month plus usage, with storage at $0.50 per GB-month

• Scale: $799 per month plus usage, with storage at $0.25 per GB-month on a dedicated deployment

• Enterprise: Custom pricing

The distinction matters because each model scales against a different usage dimension. Zep's paid plans center on ingestion credits, while HydraDB's pricing scales primarily with stored data.

For production planning, teams should model their own ingestion frequency, average Episode size, retained context, and expected growth instead of comparing entry prices alone.

Developer Experience: Zep Integrations and Observability Features

Developer experience affects how quickly memory infrastructure can move from evaluation into a production application.

Zep's Development Ecosystem

Zep provides SDKs and APIs for integrating its context capabilities into agent applications. Its Context Blocks functionality is designed to assemble information for downstream LLM consumption rather than requiring applications to manually reconstruct all retrieved memory.

Graphiti, Zep's open-source temporal graph framework, also gives developers a way to work with its underlying temporal graph concepts outside the managed platform. According to a third-party analysis, Graphiti passed 20,000 GitHub stars in November 2025.

Observability and Debugging

Memory infrastructure needs visibility into what gets retrieved and why, particularly when agent responses depend on historical information.

HydraDB includes an observability layer with traces, latency metrics, token-consumption tracking, and provenance for retrieved information. This can help engineering teams inspect how context was assembled and identify the sources behind retrieved facts.

Development teams comparing the platforms should test:

• Ingestion workflows

• Retrieval APIs

• Debugging visibility

• Provenance

• Framework compatibility

• Production monitoring requirements

The practical choice depends on how well the memory layer fits the surrounding agent stack rather than SDK availability alone.

Evaluating Zep's Enterprise Readiness: Security, Compliance, and Scaling

Production deployments often require security controls, deployment flexibility, auditability, and procurement documentation in addition to retrieval performance.

Zep's Enterprise Capabilities

Zep's current Enterprise offering includes features such as custom credits, negotiated rates, guaranteed rate limits, longer API log retention, audit logs, and additional support.

The company states that SOC 2 Type II compliance and HIPAA BAA support are available for applicable Enterprise deployments.

Zep also provides multiple deployment models at the Enterprise level, including managed cloud, cloud deployments using customer-controlled encryption keys, and BYOC configurations.

HydraDB Enterprise Capabilities

HydraDB supports SOC 2 and ISO 27001 requirements and offers deployment options extending from hosted infrastructure to dedicated and custom enterprise configurations.

Multi-tenant applications can also isolate context at the tenant or database level, depending on the required architecture.

Enterprise Evaluation Criteria

Teams evaluating either platform should verify:

• Data residency: Where production context is physically stored

• Backup and recovery: Recovery objectives and restoration procedures

• Audit logging: Whether access and retrieval activity can be traced

• Identity management: Compatibility with organizational authentication requirements

• Isolation: How customer or tenant data is separated

• Deployment control: Whether infrastructure can run within the organization's required trust boundary

Compliance certifications should be evaluated against the application's exact regulatory and contractual requirements rather than treated as sufficient on their own.

Zep's Market Position in the Growing AI Agent Context Space

Persistent memory has become increasingly relevant as AI applications move beyond isolated prompts toward agents operating across longer workflows and repeated interactions.

Zep approaches this category through a managed temporal context platform supplemented by the open-source Graphiti project. Graphiti has helped create developer awareness around temporal knowledge graphs and provides a separate open-source entry point into Zep's architectural approach.

HydraDB approaches the same broader infrastructure problem through a graph-native database architecture intended to combine persistent agent memory, temporal state, relationship-aware retrieval, and hybrid search.

Other memory platforms and graph databases take different approaches, ranging from managed memory APIs to general-purpose graph infrastructure.

The market is still developing, which makes long-term product evaluation difficult. Engineering teams should therefore consider more than current feature lists.

Relevant questions include:

• Is the data model compatible with the application's future memory requirements?

• Can historical context be retrieved deterministically?

• How difficult is it to migrate stored context?

• Can the architecture support increasing relationship complexity?

• How does the pricing model behave as memory volume increases?

• Does the deployment model satisfy infrastructure requirements?

These factors can matter more over time than differences in individual SDK features.

Zep vs. Legacy Solutions: When to Upgrade an AI Memory Stack

Many AI applications begin with simpler memory patterns such as raw chat history, prompt stuffing, or vector retrieval. Those approaches can work during early development but become harder to manage as agents accumulate information across users and sessions.

Signs that a memory architecture may need to change include:

• Agents contradict information from previous sessions.

• Customer context disappears after conversations end.

• Superseded information continues appearing in responses.

• Relationship-heavy retrieval requires increasingly complicated application logic.

• Context assembly consumes excessive tokens.

• Historical state becomes difficult to reconstruct.

When Zep Can Fit

Zep can be considered when a team wants a managed agent-memory platform with explicit temporal modeling and context assembly.

When HydraDB Can Fit

HydraDB is designed for applications where persistent agent memory, relationships, and changes over time need to be represented within the same graph-native context layer.

Its hybrid retrieval model combines semantic search, lexical retrieval, graph traversal, and temporal context rather than relying on similarity search alone.

Moving from a simpler memory architecture to either approach still requires more than replacing a database connection. Teams may need to define entities, relationships, ingestion behavior, retention policies, and retrieval logic around the application's actual context requirements.

The relevant question is therefore not whether every AI agent needs a dedicated memory platform. It is whether the application's current retrieval architecture can reliably preserve and retrieve the context required for future interactions.

Why HydraDB Is Worth Evaluating for Persistent Agent Context

Zep provides a managed approach to temporal agent memory, with a bi-temporal context graph, credit-based ingestion model, and enterprise deployment options. It can be a practical fit for teams that want these capabilities through a managed platform.

HydraDB takes a different infrastructure approach. Its graph-native architecture is designed to maintain relationships, temporal history, semantic relevance, and lexical signals within the same context layer. For applications whose usefulness depends on remembering users, decisions, entities, and changing facts across sessions, that architecture reduces the need to treat graph relationships and persistent memory as separate systems.

HydraDB also provides:

• Git-style temporal history for tracking how graph state changes

• Hybrid retrieval across semantic, BM25, graph, and temporal signals

• Sub-200ms retrieval for production agent workflows

• 90.79% LongMemEval-S accuracy in its published benchmark results

• Storage-based pricing that scales with retained context rather than API calls

• Dedicated and enterprise deployment options for teams requiring additional infrastructure control

The right choice ultimately depends on the application's retrieval patterns, temporal requirements, deployment constraints, and expected data growth. Testing both systems against representative production queries provides a more useful comparison than relying on a single benchmark or feature checklist.

Book a HydraDB demo to see how graph-native, persistent context can support production AI agents.

Frequently Asked Questions

How does Zep handle data deletion requests under GDPR and similar privacy regulations?

Zep provides data-management capabilities for its hosted service, while specific contractual requirements can vary by plan and deployment model. Organizations operating in privacy-sensitive jurisdictions should verify retention periods, deletion workflows, Data Processing Agreements, and data-subject request procedures against their own regulatory obligations before deployment.

Can Zep integrate with on-premises LLM deployments?

Zep can be used within architectures that involve different model providers, but infrastructure requirements depend on the deployment design. Organizations with strict network boundaries or data-residency requirements should evaluate Zep's Enterprise deployment options, including BYOC, and confirm that communication between the memory layer and model infrastructure meets internal security policies.

What happens to application data if a managed memory provider becomes unavailable?

Business continuity is relevant to any managed infrastructure service. Engineering teams should evaluate data-export capabilities, storage formats, migration procedures, backups, and contractual provisions before putting a memory service on the critical path. Open-source components can provide additional implementation options, but migration complexity still depends on how closely an application relies on provider-specific APIs and data models.

How does Zep's temporal model handle conflicting information?

Zep's temporal architecture is designed to represent how facts and relationships change over time. Applications that receive contradictory information from multiple sources may still need policies for determining authority, confidence, or precedence. Teams working with sources of varying reliability should test these conflict scenarios explicitly rather than assuming temporal tracking alone resolves source disagreement.

Is Zep suitable for multi-agent systems where several agents share context?

Zep can support architectures in which agents access shared contextual information, but implementation details such as isolation, concurrent updates, synchronization, and conflict handling depend on the surrounding application design. Teams building multi-agent systems should prototype simultaneous reads and writes under realistic workloads before deciding how shared memory should be organized.