meshclaw 0.5.3

AI workers, anywhere — run AI agents on any machine, no cloud required

meshclaw

Run AI agent workflows across your infrastructure. Build → test → deploy, in one command.

pip install meshclaw

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What meshclaw does

# Parallel — build and test simultaneously on different servers
meshclaw parallel worker1:"make build" worker2:"make test"

# Pipeline — sequential, stops on first failure
meshclaw pipeline worker1:"make build" worker2:"make test" relay1:"./deploy.sh"

# Broadcast — same command on every node
meshclaw broadcast "pip install --upgrade meshpop"

meshclaw turns your servers, containers, and local runtimes into a unified execution surface. One command to run distributed workflows.

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Key features

Parallel execution — different tasks on different nodes simultaneously. A build on worker1 and tests on worker2 finish in the time of the slower one.

Pipelines — chain tasks with dependency ordering. Build must succeed before tests run; tests must pass before deploy.

Broadcast — same command across all discovered nodes. Fleet-wide updates, health checks, config pushes.

Signal coordination — agents wait for signals from other agents before proceeding, enabling complex multi-step workflows.

Single-machine mode — works locally with Docker, LXC, or rtlinux containers as nodes. Same API whether you have one machine or twenty.

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Quick Start

Discover nodes

meshclaw discover

Finds all nodes on the wire network (or from config).

Run commands

# Single node
meshclaw exec "uptime" -s worker1

# Parallel
meshclaw parallel worker1:"make build" worker2:"make test"

# Pipeline (stops on failure)
meshclaw pipeline worker1:"make build" worker2:"make test" relay1:"./deploy.sh"

# Broadcast to all nodes
meshclaw broadcast "systemctl status nginx"

# History
meshclaw history

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Python API

from meshclaw import Orchestrator, Task

with Orchestrator() as orch:
    orch.discover()

    # Parallel
    orch.parallel("build-and-test", [
        Task("build", command="make build", server="worker1"),
        Task("test",  command="make test",  server="worker2"),
    ])

    # Pipeline
    orch.pipeline("release", [
        {"server": "worker1", "command": "make build"},
        {"server": "worker2", "command": "make test"},
        {"server": "relay1",  "command": "./deploy.sh"},
    ])

    # Broadcast
    orch.broadcast("systemctl reload nginx")

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Single-Machine Mode

Works without a network — local runtimes treated as nodes:

meshclaw parallel \
  docker:trainer1:"python train.py --shard 0" \
  docker:trainer2:"python train.py --shard 1" \
  docker:trainer3:"python train.py --shard 2"

Supports Docker containers, LXC containers, rtlinux instances, and the host machine.

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MCP Integration

{
  "mcpServers": {
    "meshclaw": { "command": "meshclaw-mcp" }
  }
}

"Run the build on worker1 and tests on worker2 in parallel" "Deploy to all production nodes" "What's the history of deployments this week?" "Run a health check broadcast across the fleet"

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CLI Reference

meshclaw discover                     # Find available nodes
meshclaw exec "<cmd>" -s <node>       # Run on specific node
meshclaw parallel <node>:<cmd> ...    # Parallel execution
meshclaw pipeline <node>:<cmd> ...    # Sequential pipeline
meshclaw broadcast "<cmd>"            # All nodes
meshclaw history [count] [filter]     # Command history
meshclaw version                      # Show version

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Architecture

meshclaw uses only what's available — components are optional:

meshclaw (Orchestrator)
   ├── vssh      command execution
   ├── wire      node discovery (optional)
   ├── mpop      scheduling (optional)
   └── meshdb    state and search (optional)

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MeshPOP Stack

mpop      Fleet orchestration
vssh      Authenticated transport
wire      Encrypted mesh VPN
meshclaw  Agent workflows  ← this
meshdb    Distributed search

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License

MIT — MeshPOP