paperclip
AI agent orchestration server for autonomous companies — manages teams of bots, goals, budgets, and governance
When one agent isn’t enough. Orchestration frameworks let you wire multiple agents into a graph, a team, or a swarm — with stateful execution, durable workflows, and inter-agent messaging. Most are LLM-agnostic.
AI agent orchestration server for autonomous companies — manages teams of bots, goals, budgets, and governance
Swarm intelligence engine for future prediction — simulates multi-agent interactions to deduce outcomes
Multi-agent orchestration framework for collaborative AI agents, independent of LangChain
Lightweight RAG framework — builds simple, fast, and scalable retrieval-augmented generation systems for LLMs
Agent orchestration framework for stateful LLM applications — builds resilient, long-running agents with durable execution
Agent-native learning platform for personalized tutoring, multi-agent problem solving, and knowledge management
Agent orchestrator for parallel coding agents — plans tasks, fixes CI, handles merge conflicts, manages PRs
Autonomous agent hedge fund application — automates market analysis, risk management, and trade execution on Solana
Visual AI agent studio — build and run multi-model workflows, automate GitHub operations, and generate documents
Agent orchestration is the layer above a single agent — frameworks that coordinate multiple agents through a graph, team, or swarm. They handle stateful execution, message passing, retries, and durable workflows.
When one agent isn’t enough: long-running workflows, parallel agents specializing in different roles, or systems that need to survive restarts. For a single linear task, a CLI agent or agent SDK is simpler.
Most are LLM-agnostic — LangGraph, CrewAI, AutoGen all support multiple providers. The orchestration layer is structural; the LLM is a swappable backend.