> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Cognis
> Memory layer for AI agents — remember facts, preferences, and context across conversations
## What is Cognis
Cognis is a memory layer for AI agents. It lets your agents remember things — user preferences, past conversations, decisions, facts — and recall them when they matter.
When a user tells your agent "I'm vegetarian" in one session, Cognis makes sure the agent knows that next time it recommends a restaurant. When a support agent resolves a billing issue, Cognis remembers the context so the next agent in the chain doesn't ask the same questions again.
## How It Works
1. **You send conversation messages** (user/assistant pairs) to Cognis
2. **Cognis extracts facts** automatically — names, preferences, decisions, context — using LLM-powered extraction with auto-categorization
3. **Your agent searches memories** before responding — Cognis returns the most relevant facts using hybrid search (vector similarity + keyword matching)
4. **Memories persist across sessions** — scoped by user, agent, and session so the right context reaches the right agent for the right user
## Architecture
## Core Capabilities
* **Hybrid search** — Matryoshka vector embeddings + BM25 keyword matching, fused with Reciprocal Rank Fusion
* **Smart extraction** — LLM automatically pulls discrete facts from conversations and categorizes them (identity, preferences, work, interests, etc.)
* **Multi-tenant scoping** — `owner_id`, `agent_id`, `session_id` — isolate memories per user, per agent, per conversation
* **Context assembly** — Combine short-term conversation history with long-term memories into a single LLM-ready context
## Quick Example
```python theme={null}
from lyzr import Cognis, CognisMessage
cog = Cognis()
cog.add(
messages=[
CognisMessage(role="user", content="My name is Alice. I'm vegetarian and love hiking."),
CognisMessage(role="assistant", content="Nice to meet you, Alice!"),
],
owner_id="user_alice",
)
results = cog.search(query="food preferences", owner_id="user_alice")
for r in results:
print(r.content)
# → "Alice is vegetarian"
```
```python theme={null}
from cognis import Cognis
m = Cognis(owner_id="user_alice")
m.add([
{"role": "user", "content": "My name is Alice. I'm vegetarian and love hiking."},
{"role": "assistant", "content": "Nice to meet you, Alice!"},
])
resp = m.search("food preferences")
for r in resp["results"]:
print(r["content"])
# → "Alice is vegetarian"
m.close()
```
Cognis is available as a **hosted API** (`pip install lyzr-adk`) and an **open-source library** (`pip install lyzr-cognis`). The core architecture is the same — [see the comparison](/cognis/comparison). There's also a [Claude Code plugin](/cognis/claude-cognis/overview) for persistent memory across coding sessions.
## Get Started
Install and run your first memory operations in 3 minutes
API keys, init params, search weight tuning
Complete method signatures and return types
CrewAI, LangChain, LangGraph, and Agno integrations