ClawStaff

Zep Alternative

The managed alternative to building a knowledge graph memory stack

Skip the temporal knowledge graph infrastructure. ClawStaff gives your AI agents scoped memory as a platform property, with no graph engine, no entity extraction pipeline, no retrieval stack to manage.

· David Schemm

Memory without graph infrastructure

Zep provides a temporal knowledge graph engine (Graphiti) that extracts entities, tracks relationships, and enables graph-based retrieval. It is sophisticated and powerful. It also requires integrating an SDK, managing a graph database, and maintaining the extraction pipeline. ClawStaff agents have memory because they run inside scoped org containers. No graph engine to configure.

Organizational scoping built in

ClawStaff's three-tier access model (private, team, and organization) doubles as knowledge boundaries. A team Claw shares context within its team. A private Claw keeps context to itself. With Zep, building equivalent organizational boundaries means custom access control logic on top of the API.

Full agent platform, not just a memory layer

Zep provides memory. You still need an agent runtime, integrations, deployment infrastructure, and orchestration. ClawStaff provides the entire stack (runtime, memory, integrations, ClawCage isolation, and multi-agent orchestration) in one platform.

No retrieval pipeline to build

Zep requires setting up entity extraction, configuring graph schemas, and building the integration between your agent's queries and Zep's retrieval engine. ClawStaff handles context retrieval within the org container. You deploy an agent with the right scope, and relevant context surfaces during interactions.

Predictable pricing with memory included

Zep's managed cloud uses usage-based pricing. Self-hosting requires Neo4j and the Zep service. ClawStaff charges a flat monthly rate per agent ($59/mo for 2, $179/mo for 10, $479/mo for 50). Memory is included in every plan, not billed separately.

Multi-agent memory without coordination

In ClawStaff, agents within the same scope share context naturally. In Zep, sharing memory across agents means each one integrates with the Zep API independently, and you coordinate what memory each agent reads and writes.

Migration Path

  1. 1 Document your current Zep integration: which agents use it, what entity types are extracted, and what retrieval patterns your agents rely on
  2. 2 Sign up for ClawStaff and create your organization
  3. 3 Map your agent roles to Claws with the appropriate scope (private, team, or organization)
  4. 4 Connect your tools (Slack, GitHub, Notion, etc.) through ClawStaff's integrations
  5. 5 Deploy your Claws and verify context accumulates within each scope
  6. 6 Decommission your Zep infrastructure once your team confirms agent performance meets expectations

Why teams look beyond Zep

Zep builds excellent technology. The Graphiti temporal knowledge graph engine is genuinely sophisticated: extracting entities from conversations, tracking how relationships change over time, and enabling graph-based retrieval that outperforms vector-only approaches on complex queries. Their 94.8% score on the DeepMemory Retrieval benchmark is real.

But sophistication comes with operational weight. Running Zep means managing a graph database (Neo4j or compatible), integrating the SDK into your agent runtime, configuring entity extraction, and maintaining the pipeline that keeps the graph current. For teams building AI as their core product, this is reasonable investment. For teams deploying AI agents as coworkers to get work done, it is infrastructure they would rather not own.

The pattern we see: a team integrates Zep to give their agents context persistence. It works, and the graph retrieval is impressive. Then the team needs to scope memory across different departments. Then they need to manage the graph database alongside their agent runtime. Then someone needs to debug why entity extraction is not catching certain relationship types. The memory layer becomes its own project, separate from the agents it serves.

What ClawStaff handles differently

Memory without a graph engine. ClawStaff agents run inside your org’s ClawCage container. Context persists within that container. There is no separate graph database to manage, no entity extraction to configure, no retrieval pipeline to build. Memory is where your agents run.

Scoping that matches your org. ClawStaff’s three-tier model (private, team, organization) controls both agent access and knowledge boundaries. A team Claw shares context within its team. A private Claw keeps context to its creator. Building this kind of organizational scoping on top of Zep requires custom access control logic that you design and maintain.

One platform for the full stack. Zep provides memory. ClawStaff provides the whole agent stack: runtime, memory, cross-tool integrations, container isolation, and multi-agent orchestration. Choosing ClawStaff means one vendor for your AI workforce, not a memory layer plus a separate runtime plus separate integrations.

BYOK with one data pathway. Your LLM calls go directly to your provider. Agent context stays within your org container. No second service sitting in the data path between your agents and their knowledge.

The honest tradeoff

ClawStaff’s retrieval is simpler than Zep’s. We are clear about that.

Zep’s graph-based retrieval (entity extraction, relationship traversal, temporal awareness) produces more sophisticated results for complex queries. If your agents need to answer “what changed in the billing system since the last incident, and who was involved in the resolution?” by traversing a knowledge graph, Zep handles that. ClawStaff does not provide graph traversal today.

ClawStaff’s advantage is that most teams do not need graph traversal for their agents to be useful. They need context persistence: the agent remembering what happened last week. They need scoped sharing: the engineering team’s context staying within engineering. They need operational simplicity: no additional infrastructure to manage alongside their agents.

The question is whether the retrieval sophistication justifies the operational complexity for your specific use case.

The cost comparison in practice

With Zep, the visible costs are the managed cloud pricing or the self-hosted infrastructure:

  • Managed cloud: Usage-based, scales with memory operations
  • Self-hosted: Neo4j hosting, Zep service infrastructure, operational maintenance
  • Integration engineering: SDK integration, entity configuration, debugging
  • Access control: Custom logic to scope memory across teams and roles

A mid-level engineer spending even 15% of their time on graph infrastructure maintenance costs more per month than a ClawStaff team plan.

ClawStaff’s Team plan runs $179/month for 10 agents with scoped memory included. No graph database to manage, no entity extraction to configure, no retrieval pipeline to debug.

When Zep still makes sense

Zep is the better choice if:

  • Your agents need graph-based retrieval with temporal awareness, not just “what happened” but “when it happened and what changed since”
  • Retrieval quality is your primary optimization target and you need benchmark-validated performance
  • You are building AI as a core product and want fine-grained control over the knowledge graph architecture
  • Your use case involves complex organizational knowledge where entity relationships and temporal context are essential to useful agent responses
  • You have the engineering capacity to integrate and maintain graph infrastructure long-term

Making the switch

Moving from Zep to ClawStaff means shifting from explicit graph-based memory to platform-native scoped context. The main conceptual change is that you stop managing a separate memory system. Instead, you deploy agents with the right scope and let the platform handle context persistence.

The agents that benefited most from Zep’s graph retrieval (the ones answering complex, relationship-heavy questions) may need adjusted expectations. ClawStaff’s retrieval is simpler. For most operational agent tasks (support, triage, reporting, coordination), scoped context persistence handles the job. For research-heavy or analysis-heavy agents, evaluate whether the loss of graph traversal affects output quality.

For a full feature-by-feature breakdown, see our ClawStaff vs Zep comparison.

Summary

ClawStaff replaces the build-a-graph-engine approach with a managed platform where agents have scoped memory by default. No knowledge graph to configure, no entity extraction pipeline, and knowledge boundaries that match your organization.

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