Adding Memory

You can add both messages and business data to User Graphs.

Adding Messages

Add your chat history to Zep using the thread.add_messages method. thread.add_messages is thread-specific and expects data in chat message format, including a name (e.g., user’s real name), role (AI, human, tool), and message content. Zep stores the chat history and builds a user-level knowledge graph from the messages.

The example below adds messages to Zep’s memory for the user in the given thread:

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from zep_cloud.client import AsyncZep
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from zep_cloud.types import Message
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zep_client = AsyncZep(
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    api_key=API_KEY,
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)
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messages = [
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    Message(
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        name="Jane",
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        role="user",
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        content="Who was Octavia Butler?",
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    )
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]
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await zep_client.thread.add_messages(thread_id, messages=messages)

You can find additional arguments to thread.add_messages in the SDK reference. Notably, for latency sensitive applications, you can set return_context to true which will make thread.add_messages return a context block in the way that thread.get_user_context does (discussed below).

Ignore Assistant Messages

You can also pass in a list of roles to ignore when adding messages to a User Graph using the ignore_roles argument. For example, you may not want assistant messages to be added to the user graph; providing the assistant messages in the thread.add_messages call while setting ignore_roles to include “assistant” will make it so that only the user messages are ingested into the graph, but the assistant messages are still used to contextualize the user messages. This is important in case the user message itself does not have enough context, such as the message “Yes.” Additionally, the assistant messages will still be added to the thread’s message history.

Adding Business Data

You can also add JSON or unstructured text as memory to a User Graph using our Graph API.

Customizing Memory Creation

Zep offers two ways to customize how memory is created. You can read more about these features at their guide pages:

• Custom entity and edge types: Feature allowing use of Pydantic-like classes to customize creation/retrieval of entities and relations in the knowledge graph.
• Fact ratings: Feature for rating and filtering facts by relevance to your use case.