Memory sits at the heart of human intelligence. It lets people learn from experience, adapt to changing situations, and make better decisions over time. AI agents show similar gains when they retain context: an agent that remembers past purchases, budgets, and preferences can recommend gifts or products based on prior conversations and behavior.
Most agents break tasks into a sequence such as plan → search → call API → parse → write. Without persistent memory, those agents can lose track of what happened earlier in the sequence. They may repeat tool calls, fetch the same records again, or fail to follow simple rules like "always refer to the user by their name." Repeating the same context consumes more tokens, slows responses, and produces inconsistent outputs. To address this, many companies poured billions into vector databases and embedding infrastructure, treating the problem as if it were only about search. Those systems often act as opaque layers where developers cannot inspect, query, or explain why particular memories surfaced.
The GibsonAI team built Memori to change that dynamic. Memori is an open-source memory engine that delivers persistent, intelligent memory for any LLM using standard SQL databases (PostgreSQL/MySQL). The project rethinks agent memory as application data that must be queryable, portable, and auditable. Below is a look at the memory challenge and what Memori brings to the table.
Research shows users spend roughly 23–31% of their time restating context they have shared before. For software teams that rely on AI assistants, that becomes measurable waste:
- Individual developer: ~2 hours/week repeating context
- 10-person team: ~20 hours/week of duplicated effort
- Enterprise (1,000 developers): ~2,000 hours/week, roughly $4M/year in redundant communication
Those inefficiencies have practical effects on productivity and perceived intelligence. Common symptoms include:
- No learning from interactions: mistakes repeat and preferences must be re-stated each session
- Broken workflows: multi-session projects force constant context rebuilding
- No personalization: the assistant cannot adapt to individual users or teams
- Lost insights: recurring patterns in conversations are never captured
- Compliance challenges: missing audit trails for AI-driven decisions
What AI needs is persistent, queryable memory, similar to the databases every application depends on. Application databases, though, are not tuned for context selection, relevance ranking, or injecting retrieved knowledge back into an agent’s reasoning. A dedicated memory layer can handle those functions and act as a bridge between raw conversation data and the agent’s runtime context.
SQL databases have been foundational for decades. They power banking systems, social networks, and countless business applications because of their familiarity and operational guarantees:
- Universally known: developers already understand SQL; no new query language to learn
- Proven reliability: SQL systems run mission-critical workloads around the world
- Rich querying: filtering, joining, and aggregating data is straightforward
- Strong guarantees: ACID transactions keep data consistent and safe
- Mature tooling: migration, backup, monitoring, and observability tools are widely available
Building memory on top of SQL leverages existing, battle-tested infrastructure instead of adding proprietary layers.
Many modern AI memory solutions favor vector databases and similarity search. On paper those systems are attractive because they store embeddings and return semantically similar items. In practice they introduce trade-offs:
- Multiple moving parts: setups often require a vector DB, a cache layer, and a relational store
- Vendor lock-in: data can end up trapped in proprietary systems
- Opaque retrieval: developers cannot always trace why a particular memory matched
- Costly at scale: infrastructure and query costs grow quickly
- Difficult to debug: embeddings are not human readable, so results are hard to validate with simple queries
Memori pursues a different path with a SQL-first design. Critics who doubt SQL’s scalability can look to SQLite for perspective. SQLite is one of the most widely deployed databases in history:
- Over 4 billion deployments
- Present on every iPhone, Android device, and in modern browsers
- Executes trillions of queries each day
If SQLite supports that scale on tiny devices and browsers, teams can avoid expensive, distributed vector clusters for many memory workloads.
Memori combines entity extraction, relationship mapping, and SQL-based retrieval to make memories transparent, portable, and easy to query. It coordinates multiple agents that promote important long-term items into short-term working memory for fast injection into a running context. With a single line of code—memori.enable()—any LLM framework can gain session-spanning recall, learn from prior interactions, and maintain continuity across conversations. The memory store lives in a standard SQLite file for lightweight deployments, with PostgreSQL or MySQL options for enterprise needs. That architecture aims to keep memory data fully user-owned and auditable.
Core promises include:
- Radical simplicity: one line of code to add memory for frameworks like OpenAI, Anthropic, LiteLLM, and LangChain
- True data ownership: memories stored in standard SQL databases under user control
- Complete transparency: every retrieval and decision can be queried with SQL and explained
- Zero vendor lock-in: export the memory as a SQLite file and move it anywhere
- Cost efficiency: claims of 80–90% lower costs compared with typical vector database setups at scale
- Compliance-ready: SQL storage supports audit trails, data residency rules, and regulatory controls
Practical applications span multiple domains:
- Shopping agents that remember customer preferences and past behavior
- Personal AI assistants that retain user-specific context and choices
- Support bots that avoid repeating the same clarifying questions
- Educational tutors that adapt to a student’s progress over time
- Team knowledge systems that share persistent context across members
- Compliance-oriented tools that require verifiable decision histories
Early community implementations report measurable gains. Reported outcomes include:
- Development time: roughly 90% reduction in hours required to implement a memory system
- Infrastructure costs: 80–90% lower costs than vector-based solutions
- Query performance: 10–50 ms response times, often 2–4× faster than vector similarity searches
- Memory portability: 100% of memory data can be exported, versus zero portability for many cloud vector services
- Compliance readiness: SQL audit capability available from day one
- Maintenance overhead: a single relational database beats the complexity of distributed vector clusters
Memori’s runtime model mixes a "conscious" working memory layer with an "auto" search tier that surface relevant items when needed. That dual-mode approach is intended to reflect how short-term facts and longer-term knowledge interact during reasoning. The platform offers an integration layer that injects memory into any LLM without framework-specific plumbing, and a multi-agent architecture where specialized agents cooperate to manage and refine what gets remembered.
The broader market shows several alternative strategies for agent memory:
- Mem0: a feature-rich stack that pairs Redis, vector search, and orchestration to manage distributed memory
- LangChain Memory: developer-focused abstractions tailored to the LangChain ecosystem
- Vector databases (Pinecone, Weaviate, Chroma): specialized platforms built for semantic similarity via embeddings
- Custom solutions: in-house designs that fit specific business needs but require ongoing upkeep
Each approach has trade-offs around complexity, cost, portability, and auditability. Memori positions itself as a SQL-native, open-source option aimed at simplicity, transparency, and production readiness.
Beyond the memory layer itself, effective agent deployments often need a database backbone that supports safe queries, per-user isolation, autoscaling, and lifecycle management. GibsonAI pairs Memori with database features designed for production use:
- Instant provisioning for new memory instances
- Autoscale on demand to handle peaks
- Database branching to isolate experiments or users
- Database versioning for reproducible states
- Query optimization to improve performance over time
- Point-of-recovery for robust operations
Competitors have trended toward complex distributed architectures and proprietary embedding formats. Memori takes a different route, relying on the long-proven strengths of SQL databases that already run many of the world’s core applications.
The project frames its goal as practical rather than merely sophisticated: store AI memory in the same kinds of databases developers already operate, and make that memory as portable, queryable, and manageable as any other application dataset.