by mlhher
Stop degrading your model's reasoning. A minimal, zero-config AI coding agent. Enforced ephemeral subagents keep context pure. From tiny local models up to Sol, Fable and Kimi K3.
# Add to your Claude Code skills
git clone https://github.com/mlhher/late-cliLast scanned: 5/29/2026
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}late-cli is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by mlhher. Stop degrading your model's reasoning. A minimal, zero-config AI coding agent. Enforced ephemeral subagents keep context pure. From tiny local models up to Sol, Fable and Kimi K3. It has 397 GitHub stars.
Yes. late-cli passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/mlhher/late-cli" and add it to your Claude Code skills directory (see the Installation section above).
late-cli is primarily written in Go. It is open-source under mlhher on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh late-cli against similar tools.
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Outperforming Claude Code and Codex for Local LLM Workflows — Agent Native
"Late-CLI is mindblowing... I'm shocked that the token usage is so minimal, I keep expecting a big bill from DeepSeek's API." — GitHub Discussions
"The same model feels smarter with Late." — Reddit
Built with Late: Late is primarily developed inside Late itself.
A single, statically compiled binary. Zero dependencies. No Python venvs, no NodeJS.
# Linux / macOS (Homebrew)
brew tap mlhher/late && brew install late
# Universal Fallback (Linux / macOS / Windows WSL)
curl -sfL https://raw.githubusercontent.com/mlhher/late-cli/main/install.sh | bash
# Run instantly in any project
cd your-project
late
(Arch Linux: yay -S late-cli-bin | Manual Binaries (incl. native Windows))
The Problem: Standard coding agents try to do everything inside a single, shared context window. Every compile error, lint failure, and even file write piles up in the KV cache. As the context fills with garbage, the model's intelligence actively degrades. You blame the model, but it's an architecture failure.
The Late Solution: Late splits the brain. It enforces a strict boundary between planning and execution and actively compartmentalizes agents identities and objectives.
[ User Prompt ]
│
▼
┌──────────────────────────────────────────────────────────────────┐
│ MAIN ORCHESTRATOR (~1,000 Token System Prompt) │
│ Always planning. Analyzes intent, maps layout, forms master plan.│
│ Context window remains 100% pure (Signal Only). │
└──────┬────────────────────────────────────────────────────┬──────┘
│ (Spawns) │ (Spawns)
▼ ▼
┌────────────────────────────────┐ ┌────────────────────────────────┐
│ EPHEMERAL SUBAGENT: Coding │ │ EPHEMERAL SUBAGENT: Research │
│ - Executes exact-match diffs │ │ - Scrapes & synthesizes data │
│ - Absorbs lint/write/retry │ │ - Absorbs raw data bloat │
└──────────────┬─────────────────┘ └────────────────┬───────────────┘
│ │
▼ ▼
[ CONTEXT DESTROYED ] [ CONTEXT DESTROYED ]
│ │
└───────────► [ Returns Outcomes ] ◄───────────┘
│
▼
( 🔄 Orchestrator plans & continues )
The orchestrator’s context grows only from what actually matters: your exact instructions and the definitive results. Everything the subagent did to get there is wiped from memory. The same model feels smarter in Late because it reasons purely from signal, never noise.
| Late | Claude Code | OpenCode | The Weekly Clone | |
|---|---|---|---|---|
| Workflow | Autonomous Orchestration | Manual toggling | Manual toggling | Blind execution/Manual toggling |
| Implementations | Ephemeral coder subagents (Wiped) | Floods main context | Floods main context | Floods main context |
| Explorations | Ephemeral researcher subagents (Wiped) | Floods main context | Floods main context | Floods main context |
| KV-Cache | Ruthless KV-cache management (No prompt-reprocessing) | Brute-force dumping | Brute-force dumping | Brute-force dumping |
| System Prompt | ~1,000 tokens (Always planning) | 10,000+ tokens | 10,000+ tokens | ~300-1000+ tokens (No workflow) |
| Dependencies | Zero-dependency static binary | Node.js | Node.js | Python / Node.js |
| Setup Required | None (OOTB llama-server support) |
Anthropic OAuth | Mandatory JSON tweaks | Endless YAML/TOML/JSON configs |
| Built For | Builders wanting 10x throughput | Enterprise expense accounts | Tinkering with settings | Chasing GitHub stars |
Late is model-agnostic.
Local Models (Zero Config):
Works out-of-the-box. Late targets llama.cpp on port :8080 (the default for llama-server) with zero configuration required.
Cloud Providers (DeepSeek, Claude, GPT, Kimi, GLM, OpenRouter):
export OPENAI_BASE_URL="your-api-url"
export OPENAI_API_KEY="your-api-key"
export OPENAI_MODEL="model-name"
📖 Read the Quickstart Guide to find out how to persist these settings and for MCP setup, Agent Skills, Git Worktrees, Keybindings and more.
search/replace blocks with autonomous self-healing on mismatch. Edits fail loud. We never silently corrupt your files..gitignore and .llmignore to prevent flooding the context window with irrelevant files.[y/N]. Features Session, Project, and Global trust scopes with TTL decay.Built to create engineering leverage, not to supply free infrastructure for AI startups.