{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Database Engines/Servers","url":"https://skillfed.io/packages/category/database-database-engines-servers"}],"enrichment":{"capability":"Memori is a Python SDK that automatically captures and recalls structured memory from LLM interactions and agent conversations, persisting context across sessions without requiring code changes to existing LLM calls.","skillfed_tags":["agent-memory","llm-integration","context-persistence"],"use_cases":["Build support agents that remember customer history and preferences across sessions without manual context management","Enable multi-step AI workflows to persist decisions and tool outputs so agents can reason over their own execution history","Reduce LLM token costs by storing structured memory instead of repeating full conversation history in every prompt","Implement team-wide agent memory so new engineers inherit shared context and project conventions without tribal knowledge transfer","Connect developer tools via MCP so your coding assistant learns project conventions and coding style over time"],"what_it_does":"Memori is a Python SDK that intercepts LLM interactions and automatically extracts, stores, and recalls structured memory across conversations and sessions. It works by registering with supported LLM clients, then transparently capturing conversation history, tool calls, and agent decisions without requiring code changes to your existing LLM calls.\n\nThe package is designed for AI agents and multi-turn applications that need persistent context without inflating prompt sizes. It attributes memories to entities (users, systems) and processes (agents, workflows), allowing fine-grained recall and scoping. Memori can run against its cloud API (zero-config) or your own database via BYODB mode. It integrates with frameworks and supports MCP clients for developer-focused memory.","worth_installing":"Yes, with conditions. Memori is worth installing if you need persistent agent memory and are willing to adopt its API key model and attribution pattern. The package is actively maintained with no known vulnerabilities and offers genuine efficiency gains. The main friction is the 8 runtime dependencies and the requirement to sign up for Memori Cloud or manage your own database. If you're building stateless LLM applications or don't need cross-session memory, it adds unnecessary overhead."},"id":"memori","links":{"html":"https://skillfed.io/packages/memori","md":"https://skillfed.io/packages/memori.md","pypi":"https://pypi.org/project/memori/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-28","license_spdx":null,"license_treatment":"permissive","name":"memori","python_support":"supports_current","summary":"Memori Python SDK"},"popularity":{"monthly_downloads":299041,"position":7863,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.3.6"}