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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.

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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.

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Parameters

ParameterTypeRequiredDescription
messagesList[Dict[str, str] | CognisMessage]YesConversation messages. Each message must have role ("user" or "assistant") and content keys.
owner_idstrConditionalUser or owner identifier for memory scoping.
agent_idstrConditionalAgent identifier for memory scoping.
session_idstrConditionalSession identifier for memory scoping.

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Response

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

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Basic usage

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}

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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",
)

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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",
)

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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")

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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.