Start with the invoice
Your agent can know that Account X changed owners last Tuesday. Beautiful. Your bill can still look like a hockey stick because someone dumped half the graph neighborhood into the prompt on turn eighteen.
That is the Zep conversation in one paragraph. Temporal context lakes and Graphiti-style graphs are real engineering. They solve structure. They do not automatically decide what lands in Claude Code, Cursor, support bots, and internal services on every call. That path problem is what Cortyxia is for.
Two jobs, not one product category: rich temporal structure, and memory that actually rides the model call across your tools. Zep is strong at the first. Cortyxia is built for the second.
zep / graphiti path
- 01Call the LLM provider
- 02Query the context lake / graph
- 03Stuff neighborhoods into the prompt
- 04Each team handles budgets and traces in its own way
cortyxia
- 01App or agent points at Cortyxia (any major model provider)
- 02Cortyxia API key carries provider credentials
- 03Memory retrieved and assembled into a bounded budget
- 04Request routed to your model; facts can flow back into memory
Respect the graph
Entity resolution. Time-aware edges. Long-horizon agents that need more than a flat memory list. If that is your product's differentiator, Zep or Graphiti can be the right specialized bet. We are not here to pretend embeddings alone beat a well-built temporal graph for every workload.
The organizational question is different from the graph question. Platform teams often want one motion across tools. Legal wants to know what the model saw. A context lake can feed those systems beautifully. Cortyxia focuses on the hop itself: retrieval, assembly, and visibility where the model is called.
Graphiti's open-source path and Zep's managed offering give builders a serious route from experiment to hosted graph memory. Keep that credit. Then separate storage brilliance from inference ownership.
The enforcement layer
Cortyxia sits on the inference path. Tools talk to a Cortyxia endpoint with a Cortyxia API key. Memory is assembled with a budget in mind. Traces show what fired. Knowledge health shows where coverage is thin. Providers stay interchangeable: OpenAI, Anthropic, Gemini, and the rest.
That is complementary to a strong temporal graph, not a replacement for it. Many teams will want Graphiti-class structure for specialized agents and a simple path layer for everything that already calls a model.
What ops and security usually want
Security and platform leads often ask who can see which memories, what fired on a given request, and whether empty retrievals are becoming a backlog. Cortyxia ships traces and knowledge health with the same proxy motion so those answers are easier to find.
Zep's enterprise story centers on richer agent context. Cortyxia's centers on institutional memory wherever models are called, with ops attached. If your RFP lists VPC, SSO, audit logs, and cross-team knowledge, Cortyxia is built to make those properties true on the inference hop.
| Dimension | Zep | Cortyxia |
|---|---|---|
| Core strength | Temporal graph / context lake | Budgeted memory on the model path |
| Token control | Your assembly code | Enforced at the proxy |
| Fleet coverage | Per-agent wiring | Endpoint + API key |
| Ops visibility | Context quality focus | Traces and knowledge health |
Developer experience across the fleet
Developers already juggle providers, frameworks, and IDE agents. Cortyxia is meant to feel like changing a base URL. Coding agents keep working. Namespaces isolate projects. Memory persists. Platform teams can teach one integration story while specialized teams still use Graphiti where temporal depth is the product.
Fleet drop-in
94
IDEs + services
Audit readiness
92
traces + knowledge health
Budget-aware assembly
93
context stays bounded
Temporal graph depth
90
Zep/Graphiti specialty
When Zep is still the right buy
Long-horizon agent. Differentiation is temporal reasoning. Your team wants to own prompt assembly around a graph. Buy Zep or Graphiti. That is a sharp specialized bet.
If you also need memory to show up across coding agents, support bots, and internal services without making every surface a graph client, Cortyxia is worth trying. Hybrid setups are common and honest: Graphiti where structure is the product, Cortyxia on the fleet path for everything else.
Request access at cortyxia.com, put one production-shaped workflow on the path for a week, and see whether the fit is real for your stack.
Key Takeaways
- Zep and Graphiti excel at temporal knowledge structure.
- Cortyxia focuses on memory on the inference path across your tools.
- Those jobs can complement each other in a real enterprise stack.
- Pick the specialized graph when the graph is the product. Try Cortyxia when the fleet needs one path.