nfemmanuel/iranti

Overview

Iranti provides shared persistent memory for multi-agent AI systems. It allows agents to store and retrieve knowledge across sessions, preventing forgetting. Built in Python, it focuses on consistency and persistence in agent coordination.

Best for

Best for
Developers building experimental multi-agent systems in Python that require persistent shared memory.

Use cases

• Building multi-agent systems that need long-term memory across interactions
• Enabling agents to share context and state consistently over time
• Persisting agent knowledge for reuse in ongoing conversations or tasks

Notes

Iranti provides shared persistent memory for multi-agent AI systems. It allows agents to store and retrieve knowledge across sessions, preventing forgetting. Built in Python, it focuses on consistency and persistence in agent coordination.

3 stars on GitHub. Last updated 2026-04-15.

Use cases

• Building multi-agent systems that need long-term memory across interactions
• Enabling agents to share context and state consistently over time
• Persisting agent knowledge for reuse in ongoing conversations or tasks

Pros

• Lightweight Python library with a focused purpose
• Designed specifically for multi-agent memory coordination
• Open source and free to use

Cons

• Low community adoption (3 stars) suggests limited real-world validation
• May lack comprehensive documentation or production stability
• Python-only, limiting use in polyglot stacks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.