LlamaIndex

For the full source code and advanced implementation details, see the official LlamaIndex Integration in our repository.

Overview

Integrating MemMachine with LlamaIndex 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

Install Dependencies

Install the core LlamaIndex framework and the updated MemMachine client:

pip install llama-index memmachine-client

Initialize MemMachine Memory

Import the MemMachineMemory class and configure it with your project identifiers.

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"
)

Build the Chat Engine

Equip your LlamaIndex SimpleChatEngine with the persistent memory instance.

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?"))

Pro Tip: Tune the search_msg_limit parameter to balance the depth of recall against context window usage and latency.

Requirements

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

Getting Started

Install Guides

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Core Concepts

Open Source

Overview

Configuration

Requirements

​Overview

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Overview

Configuration

Requirements