Memori v1.2: The Open-Source Memory Layer Giving AI Agents Human-Like Recall

AI agents have long suffered from a critical flaw: they forget. Without persistent memory, each interaction requires developers to re-explain context, leading to inefficiency and fractured user experiences. Enter Memori v1.2, an open-source memory layer designed to give AI agents human-like recall, transforming how multi-agent systems learn, interact, and evolve. By intelligently structuring conversations into searchable long-term and short-term memory, Memori ensures agents 'remember' user preferences, project details, and technical constraints—no repetition needed. For developers building the next generation of autonomous systems, this isn’t just a tool; it’s the missing link in creating truly contextual AI.

Breaking Down Memori’s Core Innovation

At its heart, Memori solves the context-loss problem through a dual-memory architecture inspired by human cognition. Conscious Mode acts as short-term working memory, injecting essential details like user identity or active projects at the start of a conversation. Meanwhile, Auto Mode dynamically searches the entire memory database during interactions, retrieving relevant context on-the-fly. This is powered by Pydantic-based validation, ensuring structured data extraction from conversations—entities, skills, or rules—are stored reliably in SQLite, PostgreSQL, or MySQL databases.

# Combined mode example: Leverage both memory types
from memori import Memori
from litellm import completion

memori = Memori(
    database_connect="sqlite:///project.db",
    conscious_ingest=True,  # Short-term injection
    auto_ingest=True,        # Dynamic search per query
    openai_api_key="sk-..."
)
memori.enable()  # Starts universal recording

# First call: Agent 'learns' the project context
response = completion(model="gpt-4o", messages=[{"role": "user", "content": "I'm building a FastAPI microservice"}])

# Subsequent call: Memori injects context automatically
response = completion(model="gpt-4o", messages=[{"role": "user", "content": "Add OAuth2 security"}])

Python 3.8+ support ensures seamless integration into modern AI stacks.

Why This Matters for Developers and Tech Leaders

Memori’s approach cuts development friction significantly. Traditionally, engineers had to manually manage state or rely on brittle session storage, but Memori automates this with background agents:
- Intelligent Processing: A Memory Agent extracts entities (e.g., technologies like PostgreSQL) and categorizes data (facts, skills, rules) using Pydantic models, enabling type-safe queries. A Conscious Agent then analyzes patterns—like frequently mentioned projects—to promote key details to short-term memory.
- Scalable Integration: It works out-of-the-box with LiteLLM, OpenAI, and Anthropic APIs, recording all LLM interactions automatically. For complex deployments, config files define retention policies or namespaces:

// memori.json for production
{
  "database": {
    "connection_string": "postgresql://user:pass@localhost/memori"
  },
  "agents": {
    "conscious_ingest": true
  },
  "memory": {
    "retention_policy": "30_days"
  }
}

PyPI downloads underscore its growing adoption in real-world projects.

Performance optimizations like async processing and caching ensure low latency, while tools like create_memory_tool() enable function-calling for on-demand context retrieval. This shifts focus from boilerplate to innovation—imagine agents that adapt to user habits or collaborate across sessions without resetting.

The Road Ahead: Smarter Agents, Less Grunt Work

Memori v1.2 isn’t just about storage; it’s about enabling AI that learns and grows with users. By prioritizing high-impact data—identity, preferences, and ongoing projects—it mirrors how humans retain crucial information, reducing cognitive load in multi-agent environments. As this open-source tool evolves, expect to see it power everything from personalized coding assistants to self-coordinating agent swarms, making 'forgetful AI' a relic of the past. For developers, the message is clear: stop rebuilding context and start building intelligence.

Source: Memori GitHub Repository