Alternatives
Products that do what Runigent: Open Agent service network does
The open agent network: expose services, discover others
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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Hi HN — I’m building an interoperability layer for AI agents that lets local and remote agents run inside the same network and coordinate with each other. Here is a demo: https://youtu.be/2_1U-Jr8wf4 • OpenClaw runs locally on-device • it connects to remote agents through Hybro Hub • both participate in the same workflow execution The goal is to make agent-to-agent coordination work across environments (local machines, cloud agents, MCP servers, etc). Right now most agent systems operate inside isolated runtimes. Hybro is an attempt to make them composable across boundaries.…
Apr 2026 · github.com
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We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…
Mar 2026 · github.com
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Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…
Feb 2026 · mnemom.ai
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RunAgent eliminates the complexity of AI agent deployment across different frameworks and languages. Today's developers face deployment nightmares with fragmented frameworks (LlamaIndex, LangChain, LangGraph, CrewAI, Letta, Agno, etc.) each requiring different deployment processes, creating unnecessary friction. The Solution: Like MCP (Model Context Protocol), RunAgent provides a standardized approach to agent deployment. Developers simply provide a config file and their agent code - RunAgent handles the rest with REST API and WebSocket (Streaming and non streaming). Our open-source platform…
2025 · github.com
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Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…
2025 · okteto.com
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Zero-config hosting to launch specialized AI teams instantly
Feb 2026 · yourclaw.cloud
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