Local-First Software
Local-first software stores and processes data on a user’s own device, keeping core features available without a constant connection to a remote service. When devices reconnect, synchronization can share updates across them. This approach can improve privacy, resilience, and user control, while reducing dependence on service availability or account access. It also introduces design challenges, including conflict resolution, backups, and secure synchronization across devices.
Open source local-first tools include note-taking and writing apps, collaboration platforms, search utilities, and desktop software for personal workflows. When choosing one, check which features work offline, how data is stored and synchronized, and whether you can export or back up your information. Review license terms, platform requirements, integration options, and maintenance activity as well. These tools can suit people who want greater control over their data, need reliable offline access, or prefer software that remains useful without a hosted account.
62 repositories · updated October 14, 2026
plur: Share Persistent Memory Across AI Agents
PLUR gives AI agents a shared, local-first memory store of readable YAML files. It suits developers who want corrections and project conventions to carry across sessions and tools without relying on a hosted memory service.
ActiveHealth 75/100
EverOS: Give AI Agents Portable Long-Term Memory
EverOS is a Python memory runtime that stores agent conversations and context in editable Markdown, with local indexes for retrieval. It suits teams building agents that need user-owned memory to persist across apps and sessions.
ActiveHealth 65/100
lilbee: Run Local AI and Search Your Files
lilbee is a local AI model manager and search engine for files, code, and crawled websites. It offers cited answers through a terminal app, CLI, MCP server, REST API, and Python library.
ActiveHealth 50/100
agentwatch: Monitor AI Agent Activity Locally
agentwatch combines logs from multiple coding agents into a local timeline with event details, cost tracking, alerts, and search. It suits developers who run several agents and want visibility without sending data to a cloud service.
QuietHealth 21/100
agentevals: Evaluate AI Agents from OpenTelemetry Traces
agentevals scores AI agent behavior from existing OpenTelemetry traces, without rerunning agents or making extra model calls. It suits teams building instrumented agents that need local evaluation, golden-set checks, or CI quality gates.
ActiveHealth 61/100
zennotes: Manage Markdown Notes with Keyboard-First Tools
ZenNotes is a local-first Markdown notes app for people who prefer plain files, keyboard navigation, and Vim motions. It runs as an Electron desktop app or a self-hosted web app, with search, diagrams, CLI, and MCP integrations.
NewHealth 70/100
cccc: Coordinate Coding Agents in Persistent Groups
CCCC is a local-first orchestrator for coordinating coding agents across runtimes, machines, and groups. It provides durable message history, delivery tracking, and Web, CLI, MCP, and chat-platform controls for teams that need ongoing collaboration.
ActiveHealth 69/100
ai-session-search: Search Local AI Coding Sessions
ai-session-search indexes local transcripts from multiple AI coding tools and lets you search sessions, messages, and edited files. Use it from a Rust-powered CLI, MCP server, Rust library, or Python API.
ActiveHealth 39/100
Kun: Local-First AI Agent Workspace
Kun is a local-first workspace for using AI agents across coding, writing, design, research, and automation. Its desktop GUI and terminal TUI share one runtime, keeping tasks and approvals connected.
NewHealth 75/100
AgentAleph: Run a Local Coding Agent and Manage GGUF Models
Agent Aleph is a Linux desktop app for running local GGUF models and using them to inspect, edit, and build software projects. It combines model downloads and inference controls with an approval-based coding agent.
NewHealth 25/100
guaardvark: Run a Local AI Studio on Your GPU
Guaardvark combines local AI media generation, coding agents, RAG, and automation in a self-hosted studio. It suits people who want these workflows on their own hardware and can meet its Python and GPU requirements.
ActiveHealth 63/100
maka: Run and Inspect AI Agent Workflows
Apache Maka is an agent workspace that records model messages, tool calls, and permission decisions as an append-only event log. It is aimed at people building or evaluating agents who want local session data and a recoverable execution history.
NewHealth 81/100