Type: GitHub Repository Original Link: https://github.com/GibsonAI/Memori?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev Publication Date: 2025-11-18
Summary #
WHAT - Memori is an open-source memory engine for Large Language Models (LLMs), AI agents, and multi-agent systems. It allows storing conversations and contexts in standard SQL databases.
WHY - It is relevant for AI business because it offers an economical and flexible way to manage the persistent and queryable memory of LLMs, reducing costs and improving data portability.
WHO - GibsonAI is the main company behind Memori. The developer community actively contributes to the project, as evidenced by the numerous stars and forks on GitHub.
WHERE - It positions itself in the market as an open-source solution for managing the memory of LLMs, competing with proprietary and expensive solutions.
WHEN - It is a relatively new but rapidly growing project, with an active community and continuous improvements. The project has already reached 4911 stars on GitHub, indicating significant interest.
BUSINESS IMPACT:
- Opportunities: Integration with our existing stack to reduce LLM memory management costs. Possibility of offering persistent memory solutions to clients without vendor lock-in.
- Risks: Competition with proprietary solutions that may offer advanced features. Need to monitor the project’s evolution to ensure it remains aligned with our needs.
- Integration: Memori can be easily integrated with frameworks such as OpenAI, Anthropic, LiteLLM, and LangChain. Example of integration:
from memori import Memori from openai import OpenAI memori = Memori(conscious_ingest=True) memori.enable() client = OpenAI() response = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "I'm building a FastAPI project"}] )
TECHNICAL SUMMARY:
- Core technology stack: Python, SQL databases (e.g., SQLite, PostgreSQL, MySQL). Memori uses an SQL-native approach for memory management, making data portable and queryable.
- Scalability and limits: Supports any SQL database, allowing horizontal scalability. The main limitations are related to the performance of the underlying database.
- Technical differentiators: Integration with a single line of code, cost reduction of up to 80-90% compared to solutions based on vector databases, and zero vendor lock-in thanks to data export in SQLite format. Memori also offers advanced features such as automatic entity extraction, relationship mapping, and context prioritization.
Use Cases #
- Private AI Stack: Integration in proprietary pipelines
- Client Solutions: Implementation for client projects
- Development Acceleration: Reduction of project time-to-market
- Strategic Intelligence: Input for technological roadmap
- Competitive Analysis: Monitoring AI ecosystem
Resources #
Original Links #
- GitHub - GibsonAI/Memori: Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems - Original Link
Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-11-18 14:09 Original source: https://github.com/GibsonAI/Memori?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev
Related Articles #
- LoRAX: Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs - Open Source, LLM, Python
- Memvid - Natural Language Processing, AI, Open Source
- ROMA: Recursive Open Meta-Agents - Python, AI Agent, Open Source