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Production-Ready Agent Memory

Build Personalized AI Agents That Learn From Users & Business Data

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100%

Improved Agent Accuracy

Improve your agent's accuracy on complex recall tasks by up to 100%.

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98%

Lower Inference Costs

Decrease token usage and costs for long-term recall.

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90%

Lower Response Latency

Reduce your agent's response latency for long-term recall tasks.

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Powered by Zep's Temporal Knowledge Graph

Memory That Integrates With Your Business  

Send Zep a stream of chat messages and business data, and it'll intelligently fuse them into a Knowledge Graph—giving your agent accurate, relevant user facts in a single API call.

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Easily Drop into LangChain

Zep's ChatHistory class makes production-ready personalization simple for any LangChain Expression Language or LangGraph app.

Enable Agent Temporal Reasoning

Build Agents That Continuously Learn & Adapt

As users interact with your agent and business, Zep marks outdated facts as invalid, tracks changes, and retains history—enabling agents to reason about evolving user states.

See it in action
Easily Drop into LangChain

Zep's ChatHistory class makes production-ready personalization simple for any LangChain Expression Language or LangGraph app.

Ready for Enterprise Production and Scale

Effortlessly Scale to Millions of Users and Facts

Zep retrieves user memories instantly, even as your user count and knowledge graph grows.

SOC 2 Type 2 and Privacy Compliance

Be assured with SOC 2 Type II compliance. Zep also offers privacy controls to support CCPA and GDPR compliance.

Cloud Service or BYOC Deployment

Sign up for Zep's cloud service, or deploy on your own infrastructure to meet your security and privacy needs.

Add Agent Memory in Minutes

With minimal code changes, enable your agent with rich, relevant context from chat and business data.

Python
TypeScript
Go
 
# Add a chat message to the graph
await zep_client.memory.add(session_id, messages=[
	Message(role="Jane", role_type="user", content="I can't log in!")
])
# Add business data to the graph
await zep_client.graph.add(user_id=user_id, type="json", data={"account_status": "suspended"})

# get memory relevant to the current conversation
memory = zep_client.memory.get(session_id="session_id")
print(memory.context)

"""FACTS and ENTITIES represent relevant context to the current conversation.
# These are the most relevant facts and their valid date ranges
# format: FACT (Date range: from - to)
<FACTS>
  - Emily is experiencing issues with logging in. (2024-11-14 02:13:19+00:00 - present) 
  - Emily's account has a suspended status due to payment failure. (2024-11-14 02:03:58+00:00 - present) 
...
</FACTS>"""
 

 
// Add a chat message to the graph
await zepClient.memory.add("session_id", [
    { role: "Jane", roleType: "user", content: "I can't log in!" }
]);

// Add business data to the graph
await zepClient.graph.add(userId, "json", { account_status: "suspended" });

// Get memory relevant to the current conversation
const memory = await zepClient.memory.get("session_id");
console.log(memory.context);

```
FACTS and ENTITIES represent relevant context to the current conversation.
# These are the most relevant facts and their valid date ranges
# format: FACT (Date range: from - to)
<FACTS>
  - Emily is experiencing issues with logging in. (2024-11-14 02:13:19+00:00 - present) 
  - Emily's account has a suspended status due to payment failure. (2024-11-14 02:03:58+00:00 - present) 
...
</FACTS>
```
 

 
client.Memory.Add(context.TODO(), "sessionId", &v2.AddMemoryRequest{
	Messages: []*v2.Message{{Content: "I can't log in!", RoleType: v2.RoleTypeUser}},
})

client.Graph.Add(context.TODO(), &v2.AddDataRequest{
	UserId: userID, Type: "json", Data: map[string]interface{}{"account_status": "suspended"},
})

memory, _ := zepClient.Memory.Get(context.TODO(), "session_id")
fmt.Println(memory.Context)

```
FACTS and ENTITIES represent relevant context to the current conversation.
# These are the most relevant facts and their valid date ranges
# format: FACT (Date range: from - to)
<FACTS>
  - Emily is experiencing issues with logging in. (2024-11-14 02:13:19+00:00 - present) 
  - Emily's account has a suspended status due to payment failure. (2024-11-14 02:03:58+00:00 - present) 
...
</FACTS>
```
300ms P95 Retrieval Latency

Speed

Lightning-Fast Memory Retrieval

Zep keeps your user experience fast, recalling memories in milliseconds—perfect for building voice or text agents.

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Graphiti Graph Framework

Powered by Open-Source

Zep's open-source temporal knowledge graph library, Graphiti, serves as the core of our capability to swiftly integrate new data streams and provide comprehensive historical context regarding user states.

Open Source

Framework and Platform Agnostic

Develop agents in Python, TypeScript, or Go - with any framework or none at all.

Python

TypeScript

Go

LangChain

AutoGen

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