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
Prompt-caching MCP plugin automatically reduces Claude API costs by identifying stable context
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Claude Code Skill /council Runs Prompts Across 4 AI Models in Parallel
A Claude Code skill called /council sends any prompt to GPT, Claude, Gemini, and Grok simultaneously in about 7 seconds, then uses Gemini to synthesize the best response by identifying specific improvements from the other models.
AI Functions: Runtime Code Generation with Automated Verification
AI Functions is a Python library that lets you define functions with natural language specifications instead of implementation code, executes LLM-generated code at runtime, and validates outputs with post-conditions that trigger automatic retries on failure.
Open-source Specialist Dispatch adapter delegates complex tasks to Claude Code
expert-dispatch is a ~500-line bash script that lets a cheap AI assistant delegate complex coding tasks to Claude Code CLI. It uses commands like dispatch-cc run to send tasks and maintains per-project directories with CLAUDE.md for persistent context.