AiRembr SDK Documentation

Overview

AiRembr SDK is a software development kit that enables developers to easily store, retrieve, and manage data within the AiRembr memory system — a distributed infrastructure for building AI Based Systems. It provides a seamless interface for integrating AiRembr’s real-time memory into any application, allowing AI agents, enterprise systems, and intelligent apps to capture observations, query contextual memories, and evolve knowledge structures with minimal latency.

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What is AiRembr?

AiRembr is a neuroplastic, neurosymbolic distributed memory system designed for real-time AI agents. It captures, synthesizes, and evolves data, enabling large language models to access stored information. It can store both semantic and knowledge-graph-like data. By applying background processes such as entity extraction and identification, AiRembr can further decompose and structure stored facts.

Currently, these processes must be implemented by the developer using the SDK. AiRembr is designed to be open and extensible — we do not limit how you process data or extract knowledge. Future versions will introduce optional built-in background processes, but you’ll always be free to use your own implementations.

The vision behind AiRembr is to provide a framework for anyone to build their own AI memory infrastructure.

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✨ Key Features

• Open Interface – Build modern AI Memory systems, independent of any LLM or architecture
• Real-Time Processing – Sub-20ms latency with horizontally scalable distributed services
• Neuroplastic Design – Memories that continuously learn and restructure themselves
• API-First Architecture – Seamless integration into existing infrastructures
• Enterprise-Grade – Built for production-scale workloads
• Neurosymbolic Approach – Combines machine learning with symbolic reasoning for knowledge mining

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🧩 Use Cases

• AI agents with persistent memory
• Customer data and personalization platforms
• Healthcare or enterprise knowledge systems
• Conversational AI with contextual recall
• Intelligent assistants with memory continuity

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⚙️ Installation

Prerequisites

AiRembr requires both the service infrastructure and the SDK library.

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Install AiRembr Service

1. Clone the repository and get the docker-compose.yml file
2. Run the service:

docker compose up

The service will be available at:

http://localhost:14002

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Install AiRembr SDK

pip install airembr-sdk

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🚀 Quick Start

Note: Currently, AiRembr supports conversation-scoped memory, but all stored facts are retained for future processing and retrieval.

1. Initialize the Client

from airembr.sdk.client import AiRembrChatClient

client = AiRembrChatClient(
    api="http://localhost:4002",
    source_id="8351737-a9ad-4c29-a01b-2f3180bec592",
    person_instance="person #1",
    person_traits={"name": "Adam", "surname": "Nowak"},
    agent_traits={"name": "ChatGPT", "model": "openai-5"},
    chat_id="chat-1"
)

2. Send Messages

# Person sends a message. This should be somewhere in your chat code. It does not query LLM just saves the message.
client.chat("Hi, how are you?", "person")

# Agent responds
client.chat("I'm fine.", "agent")

3. Retrieve Conversation Memory

# Save Facts (messages)  and retrieve conversation memory for this chat
memory = client.remember(realtime='collect,store,destination')

The remember() method retrieves conversation memory for the specific chat_id. It includes messages, summaries, entities, and contextual metadata — all compressed and indexed for low-latency recall.

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🧠 Core Concepts

Observations

Observations are the fundamental data units in AiRembr. Each observation contains:

• Actor – The entity performing the action (e.g., person or agent)
• Event – The type of action (e.g., "message")
• Objects – Data associated with the event

Actors and objects are treated as entities that can be identified and merged. Over time, repeated interactions enrich entities with additional traits and relationships.

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Conversation Memory vs. Long-Term Memory

Type	Description
Conversation Memory	Stores and retrieves messages within a specific chat session. Provides contextual recall and automatic compression when limits are reached. Indexed by chat_id.
Long-Term Memory	(Coming Soon) Enables cross-session memory retrieval and semantic search across historical data. Currently requires a custom implementation.

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Memory Structure

Each retrieved conversation memory includes:

• Summary – Compressed representation of previous chat context
• Entities – Identified actors with their traits
• Messages – Recent conversation history
• Context – Temporal and environmental metadata

Example Response

{
    "chat-1": """
        Summary of previous chat:
        Adam and the agent discussed LLM history...

        Entities:
          person -> (name: Adam, surname: Nowak)
          agent -> (name: ChatGPT, model: openai-5)

        Current messages:
          [date] person: Hi, how are you?
          [date] agent: I’m fine.

        Context:
          Now: 2025-11-03 09:39:23 (Monday)
    """
}

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🧰 API Reference

AiRembrChatClient

Constructor Parameters

Parameter	Type	Required	Description
api	str	✅	AiRembr service endpoint URL
source_id	str	✅	Unique identifier for the data source
person_instance	str	✅	Identifier for the person instance (entity #ID)
person_traits	dict	✅	Attributes of the person (e.g., name, email)
agent_traits	dict	✅	Attributes of the agent (e.g., model, version)
chat_id	str	✅	Unique identifier for the conversation

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chat(message, actor)

Sends a message to the AiRembr system and stores it as an observation.

client.chat("Hello!", "person")

Parameters

• message (str) – Message text
• actor (str) – "person" or "agent"

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remember(realtime)

Retrieves the stored conversation memory for the active chat session.

memory = client.remember(realtime='collect,store,destination')

Parameters

• realtime (str) – Specifies which parts of the ingestion pipeline run in real time

Returns

• A dictionary containing memories indexed by chat_id

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⚡ Features

• Automatic Context Compression – Keeps context within window limits while maintaining continuity
• Multi-Chat Support – Each chat_id maintains its own memory scope
• Entity Tracking – Identifies and merges entities automatically, evolving over time

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🧩 Advanced Usage

Building Long-Term Memory Systems

To extend AiRembr beyond conversation-scoped memory:

1. Build a Retrieval System

   • Query stored data across sessions
   • Use vector or symbolic search

2. Implement an Embedding Pipeline

   • Process incoming facts and store embeddings in a database

3. Design a Retrieval Strategy

   • Combine symbolic and semantic search methods

AiRembr provides the infrastructure foundation — you control how long-term memory and retrieval logic evolve.

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Extensibility

AiRembr is designed to be your experimental memory foundry — a sandbox for developing different approaches to AI memory systems. Future versions will include built-in long-term retrieval APIs, but the current release empowers developers to build their own.

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🗺️ Roadmap

Planned features for upcoming releases:

• Built-in long-term memory retrieval across sessions
• Internal reasoning and reflection mechanisms
• Memory model training capabilities
• Pre-built retrieval and embedding add-ons
• Semantic and hybrid search integrations

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📜 SDK License

MIT License © 2025 AiRembr