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Zurybr
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Letta Conversations API

Manage isolated message threads on agents via Python SDK and REST API.

Updated Feb 24, 2026
Search Tags:#api#agent#session management#chat#conversations#multi-user
One-click install
npx skills add https://github.com/Zurybr/lefarma-skills --skill letta-conversations-api
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Letta Conversations API
Source: https://github.com/Zurybr/lefarma-skills/tree/main/letta/conversations
Command: npx skills add https://github.com/Zurybr/lefarma-skills --skill letta-conversations-api

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires letta_client, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing multiple, independent conversation contexts within a single AI agent, enabling more complex and organized interactions.

Core Features & Use Cases

  • Conversation Management: Create, list, retrieve, and manage distinct conversation threads.
  • Message Handling: Send messages within a conversation and stream responses.
  • Isolation: Each conversation maintains its own message history, preventing context bleed.
  • Shared State: All conversations on an agent share access to memory blocks and tools.
  • Use Case: Building a multi-user chat application where each user interacts with the agent in their own private conversation thread.

Quick Start

Use the letta_client to create a new conversation for agent 'agent-xxx'.

Frequently Asked Questions about Letta Conversations API

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage isolated chat threads for multi-user sessions on an AI agent?▼

To manage isolated chat threads, you create distinct conversation contexts using the letta_client. This prevents context bleed, allowing each user to interact with the agent in their own private thread while sharing agent memory and tools.

What is the best way to build a multi-user chat application with shared agent memory?▼

The best way to build a multi-user chat application with shared agent memory is to use isolated conversation threads. This maintains separate message histories per user while ensuring all conversations share access to the same memory blocks and tools.

How do I send messages and stream responses within a specific conversation context?▼

To send messages and stream responses within a specific conversation context, use the Python SDK and REST API endpoints. This allows you to facilitate chat interactions strictly within the designated, isolated thread.

Does the letta_client support preventing context bleed between different users?▼

Yes, letta_client supports preventing context bleed by maintaining isolated message histories for each conversation thread. Each thread operates independently, ensuring user interactions remain private and separated.

Can I use Python SDK endpoints to create and list distinct conversation threads?▼

Yes, you can use the Python SDK and REST API endpoints to create and list distinct conversation threads. This functionality facilitates organized session management and multi-user chat applications on a single agent.

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