Memory of users, the business, and work done.
Managed, governed, and served at scale.
Create memories from any source. Zep constructs the graph. Retrieve relevant, token-efficient context.
Any Source
People, things, and how they change.
Automated Context Assembly
Emily prefers cycling to jogging
(Valid: 2024-11-14 — present)
# Observations
Emily's blood pressure ranges 5%
lower after cycling workouts.Millions of context graphs, governed and served as one system. The data-lake pattern, applied to agent context. Powered by Zep’s proprietary Context Graph Engine.
Retrieval stays under 200 milliseconds, regardless of graph size or count.
Authorization, retention, and audit live in the substrate, not bolted on. Policy applies across every graph, every query, every layer.
Learn moreRobbie strongly favors Adidas shoes.
“I only wear Adidas shoes. I love them!”
Every fact in the graph traces back to the source episode that produced it. Audit any answer back to where it came from.
We can easily see Zep becoming a de facto partner in this layer of the enterprise agent stack.
When new information contradicts what’s in the graph, Zep invalidates the old fact. Your agent reasons with the latest decisions, traits, and behaviors.
Old facts stay as history. Ask what’s true now, or what was true on any past date.
Reason for return
Additional comments
Zep analyses the structure of the graph to surface Observations: patterns, recurrences, and co-occurrences in memory. Your agent gains a global perspective, beyond facts and summaries.
Add memory to your agent in minutes. Works with any agent framework, or none.
# Add messages and get context in one callresponse = client.thread.add_messages( thread_id=thread_id, messages=[Message(name="Jane", role="user", content="I'd like to upgrade my plan...")], return_context=True,) # Add business data to the user's graphclient.graph.add( user_id=user_id, type="json", data=json.dumps({"event": "plan_upgrade", "to": "pro", "mrr": 49}),) # Get relevant contextuser_context = client.thread.get_user_context(thread_id=thread_id)// Add messages and get context in one callconst response = await client.thread.addMessages(threadId, { messages: [{ name: "Jane", role: "user", content: "I'd like to upgrade my plan..." }], returnContext: true,}); // Add business data to the user's graphawait client.graph.add({ userId, type: "json", data: JSON.stringify({ event: "plan_upgrade", to: "pro", mrr: 49 }),}); // Get relevant contextconst userContext = await client.thread.getUserContext(threadId);// Add messages and get context in one callresp, _ := client.Thread.AddMessages(context.TODO(), threadID, &v3.AddThreadMessagesRequest{ Messages: []*v3.Message{ {Name: v3.String("Jane"), Role: "user", Content: "I'd like to upgrade my plan..."}, }, ReturnContext: v3.Bool(true), },) // Add business data to the user's graphevent, _ := json.Marshal(map[string]interface{}{"event": "plan_upgrade", "to": "pro", "mrr": 49})client.Graph.Add(context.TODO(), &v3.AddDataRequest{ UserID: &userID, Type: v3.GraphDataTypeJSON, Data: string(event),}) // Get relevant contextuserContext, _ := client.Thread.GetUserContext(context.TODO(), threadID, nil)Govern context across thousands of agents, users, and context sources.
Control what context agents can access and what they can do with it.
Retention is policy-driven. Data expires on the schedule you set. Legal hold blocks deletion when compliance requires it.
Detailed logs of every request and policy decision, ready for audit.
Agent memory systems often trade one for another. Zep leads on all three.
Latency, error rate, retrieval activity, and ingest throughput across every project.
Usage, latency, and reliability for your account.
New graphs over time.
New users over time.
New episodes over time.
Context retrieval and graph search requests.
The trust boundary moves with your deployment. Choose where compute, data, and keys live. Learn more.
Zep's managed service. No infrastructure to run. Start in minutes.
Zep's managed service with your own encryption keys. You control the keys; data at rest is encrypted with them.
Zep deployed inside your VPC. Your network, your perimeter, your compliance boundary.
Voices from the teams running Zep in production.
Zep is one of the most exciting things I've seen for real-world agent use cases in a long time.
Unlike other systems that only retrieve static documents, Zep uses a temporal knowledge graph to combine conversations and structured business data, keeping track of how things change over time.
Zep AI was instrumental in enabling the Sidekick's personalized experience through dynamic memory retrieval.
By organizing memories into structured episodes and extracting key insights, it builds smarter, more intuitive AI agents that revolutionize how businesses harness intelligence.
Zep is one of the most exciting things I've seen for real-world agent use cases in a long time.
Unlike other systems that only retrieve static documents, Zep uses a temporal knowledge graph to combine conversations and structured business data, keeping track of how things change over time.
Zep AI was instrumental in enabling the Sidekick's personalized experience through dynamic memory retrieval.
By organizing memories into structured episodes and extracting key insights, it builds smarter, more intuitive AI agents that revolutionize how businesses harness intelligence.
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