Introducing Memori: The Open-Source Memory Engine for AI Agents

Most AI apps today work without memory. Each time you start a new app session, you have to repeat the same context to models. For example, who you are, your goals, what you’re working on, and what matters to you.

For AI agents, memory gaps are even more noticeable:

• Agents break work into steps (plan → search → call API → parse → write). Without memory, they lose track of multiple steps.

• They often repeat tool calls or fetch the same data again.

• They forget preferences (“always use 24-hour time”) or rules (“always write tests”).

• If something fails, they can’t recover. They just start over.

This means agents waste model tokens, take longer, and lead to inconsistent results.

We built Memori to change that. An open-source memory engine that gives your AI agents human-like memory so they can stay consistent, recall past work, and improve over time.

Why Memory Matters

Think of how humans remember. When you meet someone again, they recall details: where you live, what you’re working on, or your last conversation.

With Memori, your AI apps and agents:

• Automatically remember context (like tools you use, projects you’re on, people you work with)

• Reduce token usage and costs by skipping repeated backstory

• Give consistent, personal answers instead of starting from zero every time

Before and After Memori

Without memory, you repeat context every single time:

response = completion(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a Python expert. I use FastAPI, PostgreSQL, prefer clean code..."},
        {"role": "user", "content": "Remember, I work on microservices, use Docker, my teammate is Mike..."},
        {"role": "user", "content": "Help me with authentication"}
    ]
)

That’s not only expensive but also frustrating.

With Memori, memory is automatic:

from memori import Memori

memori = Memori(conscious_ingest=True)
memori.enable()  # Records all conversations

response = completion(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Help me with authentication"}
    ]
)

# Memori already knows:
# FastAPI, PostgreSQL, microservices, Docker, teammate Mike

Now, the model goes straight to the solution, recommending FastAPI auth with JWT + OAuth2 password flow without you restating everything.

How Memori Works

Memori decides which long-term memories are important enough to “promote” into short-term memory, so the agent always has the right context. It supports the following modes:

• Conscious Mode (short-term working memory): Keeps recent and essential context ready for immediate use.

• Auto Mode (dynamic intelligent search): Finds relevant context from the entire database every time.

• Combined Mode: A layered approach that balances quick recall with deep retrieval.

Under the hood, Memori uses a multi-agent architecture. Three agents work together to capture conversations, analyze them, and select the most relevant memory for injection back into your LLM. It also comes with a SQL-first design, meaning you can use SQLite, PostgreSQL, or MySQL to store memory with full-text search, versioning, and optimization out of the box.

Real Use Cases

Memori can transform how AI is used across industries:

• Sales & CRM: Agents that remember every client interaction, track deal progress, and provide insights to close faster.

• E-Commerce: Personalized shopping with smart recommendations that adapt to each customer.

• Customer Support: Context-aware support with memory of past issues, creating smoother customer experiences.

We’ve also built demo applications to show what’s possible:

• A Personal Diary Assistant that tracks your reflections over time.

• A Research Agent that builds knowledge instead of starting fresh.

• A Travel Planner Agent that remembers your preferences and past trips.

Seamless Integrations with AI Agent Frameworks

Memori connects smoothly with popular AI Agent frameworks, so you can bring memory wherever you build:

• LangChain → integrate long-term memory into enterprise-grade agents with custom tools, executors, and error handling.

• Agno → add persistent conversations and memory search to simple chat agents.

• CrewAI → enable shared memory across multiple agents for better coordination and task workflows.

Getting Started

Adding memory to your AI agent is simple.

pip install memorisdk

Install the SDK and enable memory in one line:

from memori import Memori

memori = Memori(conscious_ingest=True)
memori.enable()

From that moment, every conversation is remembered and intelligently recalled when needed.

Built on a Strong Database Infrastructure

In addition to this, AI agents need not only memory but also a database backbone to make that memory usable and scalable. Think of AI agents that can run queries safely in an isolated database sandbox, optimize queries over time, and autoscale on demand, such as initiating a new database for a user to keep their relevant data separate.

A robust database infrastructure from GibsonAI backs Memori. This makes memory reliable and production-ready with:

• Instant provisioning

• Autoscale on demand

• Database branching

• Database versioning

• Query optimization

• Point of recovery

Join Us

Memori is fully open source. We’d love you to try it, give feedback, and even contribute.

👉 Get Started on GitHub

👉 Join our Discord

👉 Read the Docs

This is just the beginning. With Memori, we’re bringing memory to AI agents — so they can coordinate better, recover faster, and feel more human every time you use them.