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subagent-fleet 0.0.1
pip install subagent-fleet
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Latest release
Released:<br>Jun 15, 2026
Run Claude Code-style subagents across your local model fleet.
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adityak74
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License Expression: MIT
SPDX<br>License Expression
Author: Aditya Karnam
Requires: Python >=3.10
Provides-Extra:<br>dev
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Project description
subagent-fleet
Run Claude Code-style subagents across your local model fleet.
subagent-fleet is a config-first Python CLI for mapping coding subagents to the best Ollama model and machine you own, then generating LiteLLM and Claude Code-style agent configuration.
Quickstart • Configuration • Generated Files • Security • Roadmap
Overview
Local model users often have more than one useful machine: a laptop, a Mac mini, a workstation, a home server, or a spare GPU box. Most coding harnesses still point at one model endpoint.
subagent-fleet turns that setup into a private local subagent fleet:
planner -> small fast model on a lightweight node<br>implementer -> larger coding model on a bigger node<br>reviewer -> larger coding model on a bigger node<br>summarizer -> small local model on the controller
It does not replace Ollama, LiteLLM, or Claude Code. It generates the glue between them:
Claude Code / coding harness<br>LiteLLM gateway generated by subagent-fleet<br>+-- Ollama node: laptop<br>+-- Ollama node: Mac mini 64GB<br>+-- Ollama node: workstation
Features
Validate a declarative fleet.yaml.
Discover models from configured Ollama nodes via /api/tags.
Generate litellm_config.yaml with ollama_chat/ routes.
Generate Claude Code-style .claude/agents/*.md files.
Generate .env.subagent-fleet for Claude Code/LiteLLM environment variables.
Warm configured Ollama models with keep_alive.
Show node health and agent routing tables.
Keep unreachable nodes isolated so one offline machine does not crash the whole workflow.
Status
MVP CLI implemented.
Available commands:
subagent-fleet init<br>subagent-fleet validate<br>subagent-fleet discover<br>subagent-fleet generate<br>subagent-fleet warmup<br>subagent-fleet status<br>subagent-fleet doctor<br>subagent-fleet clean<br>subagent-fleet skills list<br>subagent-fleet skills install<br>subagent-fleet plugins install
Install
Choose one of the install paths below.
CLI from GitHub
Install the CLI directly from PyPI:
python -m pip install subagent-fleet
Or install it as an isolated command with pipx:
pipx install subagent-fleet
Verify:
subagent-fleet --help
Development Checkout
Use this when contributing to the project:
git clone https://github.com/adityak74/subagent-fleet.git<br>cd subagent-fleet<br>python -m pip install -e ".[dev]"
Run tests:
python -m pytest
Claude Code Plugin First
Install the plugin first from Claude Code, then let the bundled bootstrap skill install the CLI:
/plugin marketplace add https://github.com/adityak74/subagent-fleet<br>/plugin install subagent-fleet
After install, ask Claude Code:
Use the subagent-fleet bootstrap skill to install the CLI and set up this repo.
The bootstrap skill will run or recommend:
python -m pip install subagent-fleet<br>subagent-fleet skills install
Codex Plugin First
Install this repository as a local Codex marketplace:
codex plugin marketplace add .<br>codex plugin add subagent-fleet@subagent-fleet
Then ask Codex:
Use the subagent-fleet bootstrap skill to install the CLI and set up this repo.
Quickstart
Create a starter config:
subagent-fleet init
Edit fleet.yaml with your Ollama node endpoints and model names, then validate it:
subagent-fleet validate
Check which nodes are reachable:
subagent-fleet discover
Generate LiteLLM, Claude agent, and environment files:
subagent-fleet generate
Start LiteLLM:
export LITELLM_MASTER_KEY="sk-local-dev"
litellm \<br>--config ./litellm_config.yaml \<br>--host 127.0.0.1 \<br>--port 4000
Point Claude Code at the local gateway:
source .env.subagent-fleet<br>claude
Configuration
subagent-fleet is driven by fleet.yaml.
project:<br>name: local-dev<br>gateway:<br>provider: litellm<br>host: 127.0.0.1<br>port: 4000<br>master_key_env: LITELLM_MASTER_KEY
nodes:<br>m5-local:<br>endpoint: http://localhost:11434<br>tags: [controller, local, fast]
m4-mini-64gb:<br>endpoint: http://192.168.1.50:11434<br>tags: [heavy, coder, reviewer]
m4-mini-16gb:<br>endpoint: http://192.168.1.51:11434<br>tags: [small, planner, summarizer]
models:<br>heavy-coder:<br>node: m4-mini-64gb<br>ollama_model: qwen2.5-coder:32b<br>litellm_alias: claude-sonnet-local<br>context: 32768<br>timeout: 600<br>max_parallel: 1
small-coder:<br>node: m4-mini-16gb<br>ollama_model: qwen2.5-coder:7b<br>litellm_alias: claude-haiku-local<br>context:...