TigrimOS v1.1.0 and Tiger CoWork v0.5.0 released with remote agent swarms and configurable governance
TigrimOS v1.1.0 and Tiger CoWork v0.5.0 released today bring remote agent swarm communication and configurable governance protocols to the open-source multi-agent platform.
Two Editions for Different Environments
TigrimOS v1.1.0 is a standalone app for Mac and Windows with a built-in Ubuntu sandbox that requires no Docker or cloud dependencies. Tiger CoWork v0.5.0 is Linux-native with the same feature set but designed to run directly on servers without VM overhead.
Remote Agent Swarm Communication
The headline feature enables swarm-to-swarm communication across networks. Each TigrimOS instance runs its own internal agent swarm, and in v1.1.0, these swarms can communicate with each other. The orchestrator reads the persona and responsibility of each remote node, picks the right swarm for the job, and delegates the entire task. Agents on different physical machines communicate as if they're on the same box.
This allows users to attach cloud GPU nodes for heavy inference, database servers for large-scale retrieval, while keeping a laptop as coordinator, addressing the constraint of running VMs on limited desktop hardware.
Configurable Governance Protocols
Five governance topologies are available, configurable per session:
- Star/Hub: Single orchestrator with agents executing tasks. Deterministic with no negotiation. Good for well-scoped tasks requiring predictable output.
- Blackboard: Orchestrator posts tasks, agents bid based on skill and availability. Classic distributed auction system. Good for mixed-specialty teams.
- Pipeline: Sequential handoff between agents (A finishes, passes to B). Good for structured workflows like research → draft → review → deliver.
- Mesh: Fully decentralized with any agent delegating to any other directly. No central authority. Good for open-ended research or creative tasks benefiting from multiple perspectives.
- Bus: Broadcast to all agents simultaneously, with whoever can handle it picking it up. Good for parallelizable workloads.
Additional Features
- Each agent can use a different LLM backend (Claude Code, Codex, GLM, Minimax, local Ollama, etc.)
- Sandbox isolation by default — agents cannot touch the host filesystem unless folders are explicitly mounted
- Long-running sessions with checkpoint recovery and context compression
- MCP server integration for external tooling
- Minecraft-style task monitor showing live agent activity with inter-agent interactions for debugging multi-agent flows
Upgrading from v1.0.0 requires no VM rebuild — SSH in and run a few commands.
📖 Read the full source: r/openclaw
👀 See Also
re_gent: Git for AI Coding Agents – Version Control for Agent Activity
re_gent is an open-source tool that provides version control for AI agent sessions, tracking every tool call, storing prompts and file diffs, and enabling commands like `rgt log`, `rgt blame`, and `rgt rewind` (coming soon).
Kios: An iOS Reader for Self-Hosted Kobo/Calibre Libraries with Progress Sync
Kios is an iOS app that reads books from self-hosted Kobo/Calibre servers and syncs reading progress via the Kobo protocol, OPDS 1.2/2.0, and kosync. Built with Claude Code.
Merlin: Local-first LLM context dedup – measure up to 71% chunk overlap, free & open-core
Merlin is a local-first context dedup tool that measured 22-71% chunk overlap across 22M passages from real agent/RAG sessions. Ships as HTTP proxy (Ollama/vLLM/SGLang/llama.cpp), MCP server (Claude/Cursor/OpenClaw), or standalone CLI. MIT open-core with daily usage caps.
Mesh LLM: Distributed AI Computing on Iroh – Run LLMs Across Your Own GPUs
Mesh LLM pools GPUs you already have across machines and exposes them as one OpenAI-compatible API. Distributes models locally, via peer routing, or as a pipeline across several nodes using iroh's QUIC transport.