Late isolates execution steps to keep your model's context clean during long workflows.
Drop it into any project. Works with any cloud provider or local model.
Outperforming Claude Code and Codex for Local LLM Workflows — Agent Native
"You solved local AI coding for me." — Reddit
"Late-CLI is mindblowing... it's a true hidden gem." — GitHub Discussions
"The same model feels smarter with Late." — Reddit
Built with Late: Late is primarily developed inside Late itself.
Late autonomously planning, delegating, and resolving a complex multi-step merge conflict.
10-Second Quickstart
A single, statically compiled binary. Zero dependencies. No Python venvs, no Node.js.
# 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
# Launch interactively in any project
cd your-project
late
Manual Binaries: Linux, macOS, native Windows
Pre-release Notice: This README documents features in v2.0.0-rc.1 (pre-release). The latest stable release is v1.5.1. Binaries for both versions are available on GitHub Releases.
One binary. Zero configuration. If llama-server is already running, Late finds it automatically.
📖 Read the Quickstart Guide for setup details on persistent settings, fully autonomous containerized workflows, MCP and Skills setup, Git worktrees, keybindings, and more.
The Architectural Bottleneck
The Problem: Standard coding agents still let the primary agent directly absorb codebase scans, compiler errors, file reads, failed diffs, and retries into one growing trajectory. As that execution noise accumulates in the KV cache, model reasoning quality degrades severely. You blame the model, but it's an architectural failure.
1. The 40% Collapse: Long-context LLMs suffer up to a ~45% collapse in reasoning accuracy once context utilization crosses 40–50%, even when all tokens are technically relevant (Weiwei Wang et al., arXiv 2026: Intelligence Degradation in Long-Context LLMs).
2. The Overthinking Tax: Reasoning models waste 27%–51% of their trajectory on redundant self-reflection loops ("Wait...", "Hmm") without accuracy gains (Chenlong Wang et al., EMNLP 2025: Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency).
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