Add Memories

The add method stores conversation messages in Cognis memory. The extraction layer automatically pulls discrete facts, preferences, and context from the messages and indexes them as searchable memory records.

Method signature

cog.add(
    messages: List[Dict[str, str] | CognisMessage],
    owner_id: str | None = None,
    agent_id: str | None = None,
    session_id: str | None = None,
) -> Dict[str, Any]

At least one of owner_id, agent_id, or session_id must be provided.

Parameters

Parameter	Type	Required	Description
`messages`	`List[Dict[str, str] \| CognisMessage]`	Yes	Conversation messages. Each message must have `role` (`"user"` or `"assistant"`) and `content` keys.
`owner_id`	`str`	Conditional	User or owner identifier for memory scoping.
`agent_id`	`str`	Conditional	Agent identifier for memory scoping.
`session_id`	`str`	Conditional	Session identifier for memory scoping.

Response

{
  "success": true,
  "session_id": "sess_abc123",
  "memories_created": 2
}

Basic usage

Hosted (lyzr-adk)
Open Source (lyzr-cognis)

from lyzr import Cognis

cog = Cognis(api_key="sk-your-api-key")

result = cog.add(
    messages=[
        {"role": "user", "content": "My name is Alice and I work at Acme Corp."},
        {"role": "assistant", "content": "Nice to meet you, Alice!"},
    ],
    owner_id="user_alice",
)
print(result)
# {"success": True, "session_id": "...", "memories_created": 2}

from cognis import Cognis

m = Cognis(owner_id="user_alice")

result = m.add([
    {"role": "user", "content": "My name is Alice and I work at Acme Corp."},
    {"role": "assistant", "content": "Nice to meet you, Alice!"},
])
print(result)
# {"success": True, "message": "Extracted 2 memories from 2 messages", ...}

m.close()

Using CognisMessage objects

Use CognisMessage instead of plain dictionaries for type safety.

from lyzr import Cognis, CognisMessage

cog = Cognis(api_key="sk-your-api-key")

result = cog.add(
    messages=[
        CognisMessage(role="user", content="I prefer dark mode in all apps."),
        CognisMessage(role="assistant", content="Noted, I'll keep that preference in mind."),
    ],
    owner_id="user_alice",
)

Multi-turn conversations

Pass longer conversations as a flat list of message pairs.

result = cog.add(
    messages=[
        {"role": "user", "content": "I'm working on a Python project."},
        {"role": "assistant", "content": "What kind of project?"},
        {"role": "user", "content": "A REST API using FastAPI."},
        {"role": "assistant", "content": "FastAPI is a great choice."},
        {"role": "user", "content": "I need help with authentication."},
        {"role": "assistant", "content": "I'd recommend OAuth2 with JWT tokens."},
    ],
    owner_id="user_alice",
    session_id="project_session",
)

Scoping with multiple identifiers

# User and agent scoped
cog.add(messages=messages, owner_id="user_alice", agent_id="support_bot")

# All three identifiers (narrowest scope)
cog.add(messages=messages, owner_id="user_alice", agent_id="support_bot", session_id="sess_001")

Async usage

import asyncio
from lyzr import Cognis

async def main():
    async with Cognis(api_key="sk-your-api-key") as cog:
        result = await cog.aadd(
            messages=[
                {"role": "user", "content": "I love hiking on weekends."},
                {"role": "assistant", "content": "That sounds fun!"},
            ],
            owner_id="user_alice",
        )

asyncio.run(main())

Send messages in natural user/assistant pairs for best extraction results. Richer, multi-turn conversations produce more useful memory records.

Overview

Knowledge Base

Knowledge Graph

Cognis (Memory)

Responsible AI

Agent Simulation Engine

Voice Agents

Add Memories

Method signature

Parameters

Response

Basic usage

Using CognisMessage objects

Multi-turn conversations

Scoping with multiple identifiers

Async usage

​Method signature

​Parameters

​Response

​Basic usage

​Using CognisMessage objects

​Multi-turn conversations

​Scoping with multiple identifiers

​Async usage

Method signature

Parameters

Response

Basic usage

Using CognisMessage objects

Multi-turn conversations

Scoping with multiple identifiers

Async usage