Skip to main content
📢 Announcing our research paper: Zentry achieves 26% higher accuracy than OpenAI Memory, 91% lower latency, and 90% token savings! Read the paper to learn how we're revolutionizing AI agent memory.
Zentry can be easily integrated into chat applications to enhance conversational agents with structured memory. Zentry’s APIs are designed to be compatible with OpenAI’s, with the goal of making it easy to leverage Zentry in applications you may have already built. If you have a Zentry API key, you can use it to initialize the client. Alternatively, you can initialize Zentry without an API key if you’re using it locally. Zentry supports several language models (LLMs) through integration with various providers.

​
Use Zentry Platform

from zentry.proxy.main import Zentry

client = Zentry(api_key="m0-xxx")

# First interaction: Storing user preferences
messages = [
    {
        "role": "user",
        "content": "I love indian food but I cannot eat pizza since allergic to cheese."
    },
]
user_id = "alice"
chat_completion = client.chat.completions.create(
    messages=messages,
    model="gpt-4o-mini",
    user_id=user_id
)
# Memory saved after this will look like: "Loves Indian food. Allergic to cheese and cannot eat pizza."

# Second interaction: Leveraging stored memory
messages = [
    {
        "role": "user",
        "content": "Suggest restaurants in San Francisco to eat.",
    }
]

chat_completion = client.chat.completions.create(
    messages=messages,
    model="gpt-4o-mini",
    user_id=user_id
)
print(chat_completion.choices[0].message.content)
# Answer: You might enjoy Indian restaurants in San Francisco, such as Amber India, Dosa, or Curry Up Now, which offer delicious options without cheese.
In this example, you can see how the second response is tailored based on the information provided in the first interaction. Zentry remembers the user’s preference for Indian food and their cheese allergy, using this information to provide more relevant and personalized restaurant suggestions in San Francisco.

​
Use Zentry OSS

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333,
        }
    },
}

client = Zentry(config=config)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "What's the capital of France?",
        }
    ],
    model="gpt-4o",
)

​
Zentry Params for Chat Completion

  • user_id (Optional[str]): Identifier for the user.
  • agent_id (Optional[str]): Identifier for the agent.
  • run_id (Optional[str]): Identifier for the run.
  • metadata (Optional[dict]): Additional metadata to be stored with the memory.
  • filters (Optional[dict]): Filters to apply when searching for relevant memories.
  • limit (Optional[int]): Maximum number of relevant memories to retrieve. Default is 10.
Other parameters are similar to OpenAI’s API, making it easy to integrate Zentry into your existing applications.