> ## 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.

# Your First Memory

> Add, search, and retrieve memories in 3 minutes — both hosted and open source

This cookbook gets you from zero to working memory in 3 minutes. Choose your version and follow along.

## Install

<Tabs>
  <Tab title="Hosted (lyzr-adk)">
    ```bash theme={null}
    pip install lyzr-adk
    ```

    ```bash theme={null}
    export LYZR_API_KEY="your-lyzr-api-key"
    ```
  </Tab>

  <Tab title="Open Source (lyzr-cognis)">
    ```bash theme={null}
    pip install lyzr-cognis
    ```

    ```bash theme={null}
    export GEMINI_API_KEY="your-gemini-key"    # For embeddings
    export OPENAI_API_KEY="your-openai-key"    # For fact extraction
    ```
  </Tab>
</Tabs>

## Step 1: Initialize and Add Memories

<Tabs>
  <Tab title="Hosted (lyzr-adk)">
    ```python theme={null}
    from lyzr import Cognis, CognisMessage

    cog = Cognis()

    # Store a conversation
    cog.add(
        messages=[
            CognisMessage(role="user", content="My name is Alice and I love hiking."),
            CognisMessage(role="assistant", content="Nice to meet you, Alice!"),
            CognisMessage(role="user", content="I work at Google as a data scientist."),
            CognisMessage(role="assistant", content="That's a great role!"),
        ],
        owner_id="user_alice",
    )
    ```
  </Tab>

  <Tab title="Open Source (lyzr-cognis)">
    ```python theme={null}
    from cognis import Cognis

    m = Cognis(owner_id="user_alice")

    # Store a conversation
    result = m.add([
        {"role": "user", "content": "My name is Alice and I love hiking."},
        {"role": "assistant", "content": "Nice to meet you, Alice!"},
        {"role": "user", "content": "I work at Google as a data scientist."},
        {"role": "assistant", "content": "That's a great role!"},
    ])
    print(result["message"])
    # "Extracted 3 memories from 4 messages"
    ```
  </Tab>
</Tabs>

Cognis automatically extracts discrete facts from the conversation and stores them as searchable memory records.

## Step 2: Search Memories

<Tabs>
  <Tab title="Hosted (lyzr-adk)">
    ```python theme={null}
    results = cog.search(query="What does Alice do for work?", owner_id="user_alice", limit=5)
    for r in results:
        print(f"  {r.content}  (score: {r.score})")

    # Output:
    #   Alice works at Google as a data scientist  (score: 0.89)
    ```
  </Tab>

  <Tab title="Open Source (lyzr-cognis)">
    ```python theme={null}
    resp = m.search("What does Alice do for work?", limit=5)
    for r in resp["results"]:
        print(f"  {r['content']}  (score: {r['score']})")

    # Output:
    #   Alice works at Google as a data scientist  (score: 0.8712)
    ```
  </Tab>
</Tabs>

## Step 3: Retrieve All Memories

<Tabs>
  <Tab title="Hosted (lyzr-adk)">
    ```python theme={null}
    memories = cog.get(owner_id="user_alice")
    for mem in memories.memories:
        print(f"  [{mem.id[:8]}] {mem.content}")

    # Output:
    #   [a1b2c3d4] Alice's name is Alice
    #   [e5f6g7h8] Alice works at Google as a data scientist
    #   [i9j0k1l2] Alice loves hiking
    ```
  </Tab>

  <Tab title="Open Source (lyzr-cognis)">
    ```python theme={null}
    resp = m.get_all()
    for mem in resp["memories"]:
        cat = mem["metadata"]["category"]
        print(f"  [{cat}] {mem['content']}")

    # Output:
    #   [identity] Alice's name is Alice
    #   [work_career] Alice works at Google as a data scientist
    #   [interests] Alice loves hiking
    ```
  </Tab>
</Tabs>

## Step 4: Use Context in Your LLM

<Tabs>
  <Tab title="Hosted (lyzr-adk)">
    ```python theme={null}
    context = cog.context(
        current_messages=[
            CognisMessage(role="user", content="What should I do this weekend?"),
        ],
        owner_id="user_alice",
    )
    # Use context in your LLM system prompt
    print(context)
    ```
  </Tab>

  <Tab title="Open Source (lyzr-cognis)">
    ```python theme={null}
    ctx = m.get_context(
        messages=[{"role": "user", "content": "What should I do this weekend?"}]
    )
    # Use context_string in your LLM system prompt
    print(ctx["context_string"])
    # "Relevant memories:\n- Alice loves hiking\n- ..."

    m.close()  # Don't forget to close!
    ```
  </Tab>
</Tabs>

## What's Different Between Hosted and Open Source?

| Aspect        | Hosted                         | Open Source                        |
| ------------- | ------------------------------ | ---------------------------------- |
| Data storage  | Lyzr cloud                     | Local (`~/.cognis/`)               |
| Async support | Full (`aadd`, `asearch`, etc.) | Sync only                          |
| Summaries     | Available                      | Not available                      |
| Return types  | Objects (`.content`, `.score`) | Dicts (`["content"]`, `["score"]`) |
| Cleanup       | Optional                       | Required (`m.close()`)             |

For a full comparison, see [Hosted vs Open Source](/cognis/comparison).

## Next Steps

<CardGroup cols={2}>
  <Card title="CrewAI Integration" icon="users" href="/cognis/cookbooks/cognis-crewai">
    Add memory to multi-agent crews
  </Card>

  <Card title="LangGraph Integration" icon="diagram-project" href="/cognis/cookbooks/cognis-langgraph">
    Memory as graph nodes
  </Card>
</CardGroup>