A distributed framework for LLM agents

AI agents in Go — Temporal for durable, crash-resilient execution or run in-process with zero setup. OpenAI, Anthropic, Gemini, tools, MCP, A2A, RAG, memory, conversations, AG-UI, streaming, sub-agents & human-in-the-loop.

Peer-to-peer communication for AI coding agents. 8 MCP tools, full CLI, Python client. Part of the Qualixar research initiative by Varun Pratap Bhardwaj.

Composable primitives for goal-directed coordination in Go. Synchronization, resource allocation, and collective intelligence that adapt through multiple strategies. Built with capacity for systems with 1000+ independent agents.

A framework for building multi-agent AI systems. Enables LLMs to collaborate through hierarchical organization, parallel task execution, and extensible tools.

Multi-agent systems framework for the BEAM platform - build distributed autonomous agents with OTP supervision and fault tolerance

A minimal protocol for task exchange between autonomous AI agents (TASK → RESULT | ERROR).

Distributed Multi-Agent AI Orchestration with Ollama - Advanced collaborative workflow with AST quality voting, TrustCall validation, and adaptive strategy selection

두레클로 — 분산 디바이스 AI 에이전트 협력 오케스트레이션 | Distributed AI agent crew orchestration

LangGraph agents that coordinate across machines with durable execution, retries, and human approval.

Multi-agent coordination layer for the Digi-Office ecosystem: task queues, worker pools, A2A messaging, and self-healing agent swarms. Extracted from LISA_FTM.

LangGraph coordinates agents in one process. AXME coordinates them across machines - with durable state and automatic recovery.