Memori by GibsonAI: SQL-Native Memory Engine That Makes AI Agents Remember

Why memory matters for AI agents

Human intelligence relies on memory to learn, adapt, and make better decisions over time. AI agents benefit in the same way: remembering past interactions, preferences, and constraints lets them avoid repetitive calls, save tokens, and deliver consistent responses. Without persistent memory, agents repeat context, re-run tools unnecessarily, and fail to maintain simple rules like addressing a user by name.

The problem with stateless LLM workflows

Agents often break tasks into many steps — planning, searching, calling APIs, parsing results, and writing outputs. When each session starts fresh, earlier steps are forgotten. That leads to wasted time, higher token usage, and a weaker user experience. Studies show users spend roughly 23–31% of their time re-supplying context they already provided, which translates into major productivity losses for teams and enterprises.

Why SQL instead of vectors

Most modern memory solutions rely on vector databases and embeddings for similarity search. Those systems can be powerful but introduce complexity and cost: multiple services, vendor lock-in, black-box retrieval, and high storage/query costs. SQL databases, by contrast, are ubiquitous, reliable, and well understood. They offer familiar query capabilities, ACID guarantees, and a mature ecosystem of tools for backup, migration, and monitoring.

How Memori approaches memory

Memori is an open-source, SQL-native memory engine from GibsonAI that stores AI memories in standard SQL databases like SQLite, PostgreSQL, or MySQL. It uses structured entity extraction, relationship mapping, and SQL-based retrieval to make memories transparent and queryable. Memori also coordinates multiple agents to promote important long-term memories into short-term storage for fast context injection.

With a single line of code the project shows how simple enabling memory can be:

memori.enable()

Once enabled, any LLM can persist conversations, learn from interactions, and maintain context across sessions. Data remains portable and auditable because it lives in a standard SQL file or database under the user’s control.

Key differences and benefits

Memori emphasizes simplicity and ownership. Core advantages include:

• Radical simplicity: one-line enablement across frameworks like OpenAI, Anthropic, LiteLLM, and LangChain
• True data ownership: memories kept in standard SQL databases controlled by the user
• Full transparency: retrievals are explainable via SQL queries
• No vendor lock-in: exportable SQLite files for easy migration
• Cost efficiency: significantly cheaper storage and query costs compared to vector stacks
• Compliance readiness: SQL enables straightforward audit trails and data residency

Typical use cases

Memori suits a wide range of applications:

• Personalized shopping assistants that remember past purchases and preferences
• Personal AI companions that recall user context over time
• Customer support bots that avoid repetitive questions
• Adaptive educational tutors tracking student progress
• Team knowledge systems with shared memory and audit trails

Performance and impact

Early community reports suggest sizable benefits:

• 90% reduction in implementation time for memory systems
• 80–90% lower infrastructure cost versus vector database setups
• Query latency in the 10–50ms range, often faster than vector similarity searches
• Fully portable memory data and SQL-enabled auditing from day one

Technical innovations

Memori introduces a few notable ideas:

• Dual-mode memory combining working memory and intelligent search
• A universal integration layer that injects memory into any LLM without framework-specific code
• Multi-agent coordination to manage what gets stored long-term and what remains short-term

Operational backbone

GibsonAI pairs Memori with a database infrastructure that supports instant provisioning, autoscaling, branching, versioning, query optimization, and recovery. This turns SQL-based memory into a production-ready component that can scale and be managed like any other application database.

Practical takeaway

Memori reframes AI memory as a classic persistence problem solved with proven SQL technology. By making memories portable, debuggable, and auditable, it removes many hidden costs of vector-first architectures and offers a pragmatic path to smarter, more reliable AI agents.

Check the GibsonAI GitHub page for more details and resources.