> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex

> Enable persistent, long-term memory for your LlamaIndex chat agents

For the full source code and advanced implementation details, see the official **[LlamaIndex Integration](https://github.com/MemMachine/MemMachine/tree/main/integrations/llamaindex)** in our repository.

## Overview

Integrating MemMachine with [LlamaIndex](https://www.llamaindex.ai/) provides a persistent memory layer for chat engines. This allows agents to:

* **Recall User Profiles**: Surface user preferences and facts directly into the prompt context.
* **Maintain Context Across Sessions**: Stored episodic and semantic memories persist beyond a single execution.
* **Intelligent Injection**: MemMachine automatically injects relevant context as a system message during inference.

## Configuration

You can configure the LlamaIndex memory component using environment variables or direct constructor parameters.

| Parameter    | Environment Variable    | Default                 | Description           |
| :----------- | :---------------------- | :---------------------- | :-------------------- |
| `base_url`   | `MEMORY_BACKEND_URL`    | `http://localhost:8080` | MemMachine server URL |
| `org_id`     | `LLAMAINDEX_ORG_ID`     | `llamaindex_org`        | Organization ID       |
| `project_id` | `LLAMAINDEX_PROJECT_ID` | `llamaindex_project`    | Project ID            |
| `user_id`    | `LLAMAINDEX_USER_ID`    | `None`                  | User identifier       |
| `agent_id`   | `LLAMAINDEX_AGENT_ID`   | `None`                  | Agent identifier      |
| `session_id` | `LLAMAINDEX_SESSION_ID` | `None`                  | Session identifier    |

***

<Steps>
  <Step title="Install Dependencies">
    Install the core LlamaIndex framework and the updated MemMachine client:

    ```bash theme={null}
    pip install llama-index memmachine-client
    ```
  </Step>

  <Step title="Initialize MemMachine Memory">
    Import the `MemMachineMemory` class and configure it with your project identifiers.

    ```python theme={null}
    from mem_machine_memory import MemMachineMemory

    memory = MemMachineMemory(
        base_url="http://localhost:8080",
        org_id="my_org",
        project_id="my_project",
        user_id="user_123",
        session_id="session_456"
    )
    ```
  </Step>

  <Step title="Build the Chat Engine">
    Equip your LlamaIndex `SimpleChatEngine` with the persistent memory instance.

    ```python theme={null}
    from llama_index.core.chat_engine import SimpleChatEngine
    from llama_index.llms.openai import OpenAI

    # Ensure your API key is configured
    llm = OpenAI(api_key="your-openai-api-key")

    agent = SimpleChatEngine.from_defaults(
        llm=llm, 
        memory=memory
    )

    # First interaction stores facts
    print(agent.chat("I am Alice, I like Python programming."))

    # Subsequent interactions recall them
    print(agent.chat("What do you know about me?"))
    ```
  </Step>
</Steps>

<Note>
  **Pro Tip:** Tune the `search_msg_limit` parameter to balance the depth of recall against context window usage and latency.
</Note>

## Requirements

* **MemMachine Server**: Must be running (default: `http://localhost:8080`).
* **Python**: 3.12 or higher.
* **LLM**: An OpenAI-compatible LLM provider.