NevaMind-AI/memUPublic

Personal memory across agents

AI summary: An agent-driven memory system that curates a shared LLM wiki across different sessions, coding agents, and devices.

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PythonOtherCreated Jul 29, 2025Last push 2d agoLatest release v1.5.1+60 stars this week+153 this month

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since Aug 3, 2025
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14.3K stars as of Aug 7, 2026, tracked back to Aug 3, 2025. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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Signals and awards

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  • Widely adopted

    14,267 stars

  • Continuous integration

    Automated checks passing

  • Repeat trending

    3 trending appearances

What memU does

memU serves as a lightweight, personal memory backend for AI agents, allowing them to persistently store and retrieve knowledge. It automatically distills reusable skills from your agent history, persisting specifications, tasks, and learned workflows into a Markdown-based Wiki. Built with a compact 500-line core logic module, it operates as a background process to bridge context across multiple tools like Cursor, Claude Code, and Codex. It essentially gives your AI assistants a persistent memory, ensuring they don't start from scratch every session.

memU is for software engineers and power users who heavily utilize LLM-based coding agents and want to retain context across different platforms. It is compatible with macOS, Windows, and Linux environments.

  • Cross-Session Persistence: Maintains a continuous wiki of memory that spans different agent sessions and environments.
  • Automatic Skill Extraction: Analyzes agent history to distill useful tool calls and messages into reusable Markdown skills.
  • Multi-Agent Support: Integrates seamlessly with popular agents like Claude Code, Cursor, OpenClaw, and Hermes.
  • Job Slicing: Segments agent histories into self-contained evolution jobs with relevant paths and context.
  • Markdown Indexing: Writes readable skill documentation containing workflow steps, edge cases, and pitfalls.
  • Lightweight Core Logic: Implements its memory processing in under 500 lines of code for easy auditing and adaptation.

Where teams use it

Preserving engineering standards

Developers can instruct their AI agents once, and memU will ensure the agent follows those specific conventions in all future sessions.

Cross-device workflow synchronization

Remote workers can share context between their desktop at work and their laptop at home using the shared LLM wiki.

Agent skill bootstrapping

Power users can automatically extract complex, multi-step operations performed by an agent into a reusable skill file.

Mitigating context window limits

Engineers can rely on memU to surface only the most relevant historical memory, preventing token bloat in long conversations.

Getting started: Get your API key from memu.so, then prompt your agent to read https://memu.pro/SKILL.md

README

main branch

memU Banner

memU

Personal memory, stored as Wiki

Across Sessions. Across Agents. Across Devices.

PyPI version License: Apache 2.0 Python 3.11+ Discord Twitter

NevaMind-AI%2FmemU | Trendshift


memU is a lightweight, agent-driven memory system that gives users a shared LLM wiki across sessions, agents, and devices. It automatically distills your own reusable skills from your agent history. Its core memory logic is only 500 lines — compact enough to inspect, understand, and adapt.

Quick start

memU works with Codex, Claude Code, Cursor, OpenClaw, Hermes, WorkBuddy, Cola, and more. See Host adapters.

Cross-device · Free · Unlimited · View online

Get your API key from memu.so, then send this message to your agent:

Read https://memu.pro/SKILL.md, follow its instructions to install and configure memU, API Key is memu_•••••••••(get Api Key from memu.so).

Agent support

This matrix lists the currently tested memU integrations by operating system.

  • Memorize — capture useful session knowledge through a scheduled background task and turn it into reusable memory.
  • Retrieve — bring relevant memory into a future task.
  • ⚠️ — supported with an important limitation; see the user note.

macOS

Agent Mode Memorize Retrieve User note
ChatGPT ChatGPT(Work mode), codex and VS Code extension
ChatGPT Chat Chat mode is not currently supported. Please use Work mode.
Claude Code Desktop and CLI If the selected model declines the setup steps, retry with Opus or another model. Sonnet 5 can occasionally do this.
Claude Chat and Cowork
Cursor
OpenClaw Retrieve support has not yet been verified.
Hermes Agent
WorkBuddy

Windows

Agent Mode Memorize Retrieve User note
ChatGPT ChatGPT(Work mode), codex and VS Code extension
ChatGPT Chat Chat mode is not currently supported. Please use Work mode.
Claude Code Desktop and CLI If the selected model declines the setup steps, retry with Opus or another model. Sonnet 5 can occasionally do this.
Claude Chat and Cowork
Cursor
OpenClaw
Hermes Agent ⚠️ Use a memU version with Windows HERMES_HOME support; older versions may retrieve from the wrong files.
WorkBuddy With Hy3, retrieval may fail. Retry with another model if this happens.

Linux

Agent Mode Memorize Retrieve User note
Codex VS Code extension
Claude Code CLI
OpenClaw 4.23 / 7.1

Support status reflects the current release and may change as host integrations evolve.

How it works

memU memory system architecture

Automatic skill extraction

Once the scheduled bridging task is installed, memU can turn useful agent history into reusable Markdown skills automatically.

How memU turns agent history into reusable skills

  1. Capture new sessions. The host adapter reads new session history, including messages and tool calls.
  2. Prepare self-evolve jobs. prepare slices each session into a self-contained job with the paths and context the agent needs.
  3. Let the agent decide. The agent reads related existing skills, then chooses to do nothing, patch an existing skill, or create a new one.
  4. Write readable skill Markdown. Each skill has a name, description, and reusable workflow, including useful branches, edge cases, and pitfalls.
  5. Commit and index. commit submits changed skill files through commit_results; memU embeds the skill name and description and stores it under the skill track.
  6. Retrieve it later. On a similar future task, memU returns the relevant skill so any connected agent can use the learned workflow.

The judgment and synthesis stay inside the agent. MemoryService makes no LLM or chat calls; it stores, embeds, and retrieves the skill Markdown the agent prepared.

Self-hosted

Private · Single-device · Embedding key required

To run memU locally with your own storage and embedding provider, send this message to your agent:

Read https://raw.githubusercontent.com/NevaMind-AI/MemU/main/SKILL.md and follow it to install memU.

Uninstall

To uninstall memU, send this message to your agent:

Read https://memu.pro/SKILL.md and follow its instructions to uninstall memU.

By default, uninstalling removes the host integration and tooling while keeping your memory store and ~/.memu/config.env, so a later reinstall can resume where you left off. Memory is erased only when you explicitly ask for it.

Host adapters: memory for desktop coding agents

memU runs as a sidecar to a desktop agent, one binary per host. Each binds two seams:

  • record — a scheduled bridging task slices new session logs into self-contained job files; the agent itself distills them into memory/skill Markdown; commit submits whatever the agent left on disk back through commit_results.
  • inject — a standing instruction in the host's instruction file tells the agent to run <binary> retrieve (→ progressive_retrieve) before answering.
Host Binary Session log it mines Instruction file it patches
Codex memu-codex ~/.codex/sessions/**/*.jsonl ~/.codex/AGENTS.md
Claude Code memu-claude-code ~/.claude/projects/<project>/<session>.jsonl ~/.claude/CLAUDE.md
Cursor (Agent/CLI) memu-cursor ~/.cursor/projects/<project>/agent-transcripts/**.jsonl ./AGENTS.md (per project)
OpenClaw memu-openclaw ~/.openclaw/agents/<agentId>/agent/openclaw-agent.sqlite (SQLite, read-only) + legacy <agentId>/sessions/*.jsonl ~/.openclaw/workspace/AGENTS.md
Hermes Agent memu-hermes ~/.hermes/state.db (SQLite, read-only) ~/.hermes/SOUL.md
WorkBuddy memu-workbuddy ~/.workbuddy/projects/<project>/<session>.jsonl ~/.workbuddy/SOUL.md
Cola memu-cola ~/.cola/sessions/<scope>/<session>.jsonl ~/.cola/memory-bank/MEMORY.md
any other agent memu-agent found by memu-agent detect (JSONL dialect sniffed) found by detect (AGENTS.md / CLAUDE.md / SOUL.md / …)

For agents without a dedicated binary, memu-agent detect probes the machine and reports per agent whether memorization works (a recognizable session log exists) and whether retrieval works (an instruction file exists to patch) — then the same verbs run against what it found.

All hosts share one configured memory backend via ~/.memu/config.env — local or MemU Cloud. What one host's sessions taught memU, another host retrieves.

Installation is the one-message setup in Quick start or Self-hosted. SKILL.md is the routing skill it hands your agent: install the package, identify which host you are (falling back to memu-agent detect for anything without a dedicated adapter), print that host's packaged install guide (<binary> docs install), and follow it — configure the memory backend, register the scheduled bridging task, patch the instruction file, each step behind a verify gate — then report which seams (memorization / retrieval) are now active.

Afterwards <binary> doctor proves the whole loop resolves: config, selected mode, and a live retrieval.

Adding another host means implementing one TranscriptSource (where its session logs live, how its records are shaped) plus a HostSpec-sized CLI — the pipeline, verbs, and instruction text are shared.

CLI

With memU Cloud, sign in at memu.so to view your memory files. With a local installation, memory lives in the shared store configured by MEMU_DB in ~/.memu/config.env — typically ~/.memu/memu.sqlite3 for local SQLite, or a Postgres DSN.

Once installed, your agent retrieves relevant memory automatically before answering. To retrieve manually, run the adapter for your host:

memu-codex retrieve "What should I remember about this project?"
# or: memu-claude-code / memu-cursor / memu-openclaw / memu-hermes / memu-workbuddy / memu-agent

Install or invoke the CLI directly:

pip install memu-cli         # library + memu + memu-codex CLIs
npx memu-cli --help          # CLI via npm launcher (engine: PyPI package memu-cli)
uvx --from memu-cli memu     # CLI via uv, no install

Configuration

Values resolve in order: process env → ~/.memu/config.env → default. memU supports Local and Cloud memory backends, selected by MEMU_MEMORY_MODE; an unset mode remains Local for backward compatibility.

For Local / self-hosted installations, every CLI flag has a matching variable:

Setting Env var Default
Store MEMU_DB ./data/memu.sqlite3 (CLI); required for host adapters
Embedding provider MEMU_EMBED_PROVIDER openai (also: jina, voyage, doubao, openrouter); legacy MEMU_LLM_PROVIDER still read
API key MEMU_API_KEY the provider's env var, e.g. OPENAI_API_KEY
Embedding model MEMU_EMBED_MODEL the provider's default
Base URL MEMU_BASE_URL the provider's default

Run <binary> doctor to display the resolved mode and verify the same retrieval path the host uses.

Storage backends

Provider DSN Vector search Use for
inmemory brute-force cosine tests, throwaway sessions
sqlite sqlite:///path.sqlite3 brute-force cosine local/default, single writer
postgres postgresql://... pgvector concurrent access, large stores (pip install "memu-cli[postgres]")
service = MemoryService(
    database_config={"metadata_store": {"provider": "postgres", "dsn": "postgresql://..."}},
    embedding_profiles={"default": {"provider": "jina"}},
)

License

Apache-2.0

Partnership Community: LINUX DO

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Recent activity

commits and pull requests

Releases and announcements

25 total
  1. v2.0.0-beta.0v2.0.0-beta.0Jul 23, 2026pre-release9 downloads

    ## [2.0.0-beta.0](https://github.com/NevaMind-AI/memU/compare/v1.5.1...v2.0.0-beta.0) (2026-07-23) ### Features * **blob:** ingest rich documents (PDF/Office/HTML) via MarkItDown ([#447](https://github.com/NevaMind-AI/memU/issues/447)) ([1e16341](https://github.com/NevaMind-AI/memU/commit/1e1634131f84ceb023d03cd87bcf062317abd5ba)) * Codex session-bridging pipeline and scheduled-task skill ([#477](https://github.com/NevaMind-AI/memU/issues/477)) ([1a8edb5](https://github.com/NevaMind-AI/memU/commit/1a8edb57110ebfd208b2fe1f52e3998058fed408)) * **doctor:** diagnose proxy hijacking instead of printing a bare 502 ([7c41eec](https://github.com/NevaMind-AI/memU/commit/7c41eecd53e4c13b1a5c25b01b70f484147295fb)) * **env:** config.env carries NO_PROXY; MEMU_HTTP_PROXY works from the file ([5265945](https://github.com/NevaMind-AI/memU/commit/5265945e3063529819415194e79c46b7614d6081)) * from memory item/category to tracked entry/file for workspace memorization (ADR 0006) ([#456](https://github.com/NevaMind-AI/memU/issues/456)) ([68877da](https://github.com/NevaMind-AI/memU/commit/68877da10a1a18be5c708ec1ab7eb6c45b80ae50)) * **hermes:** install retrieval as a skill ([#536](https://github.com

  2. v1.5.1v1.5.1Mar 23, 2026227 downloads

    ## [1.5.1](https://github.com/NevaMind-AI/memU/compare/v1.5.0...v1.5.1) (2026-03-23) ### Bug Fixes * use correct interpolation in alembic url ([#390](https://github.com/NevaMind-AI/memU/issues/390)) ([fd87ceb](https://github.com/NevaMind-AI/memU/commit/fd87ceb558eaa800aeb694b045847971958f23a3))

  3. v1.5.0v1.5.0Mar 17, 202659 downloads

    ## [1.5.0](https://github.com/NevaMind-AI/memU/compare/v1.4.0...v1.5.0) (2026-03-17) ### Features * add non-propagate option for memory patch ([#386](https://github.com/NevaMind-AI/memU/issues/386)) ([3b67458](https://github.com/NevaMind-AI/memU/commit/3b67458c6325b4fcdaccbc34f6e0fea491b7ba96)) * **http:** add HTTP proxy support for LLM & embedding clients ([#310](https://github.com/NevaMind-AI/memU/issues/310)) ([a3d45e2](https://github.com/NevaMind-AI/memU/commit/a3d45e2f4174702fd8fd83cd13862285cd47cad9)) ### Bug Fixes * httpx base_url path discarded when endpoint starts with / ([#328](https://github.com/NevaMind-AI/memU/issues/328), [#336](https://github.com/NevaMind-AI/memU/issues/336), [#341](https://github.com/NevaMind-AI/memU/issues/341), [#329](https://github.com/NevaMind-AI/memU/issues/329)) ([#344](https://github.com/NevaMind-AI/memU/issues/344)) ([9e31ef2](https://github.com/NevaMind-AI/memU/commit/9e31ef235d46317f383613825090c52943cb55a2)) ### Documentation * add architecture explanation and adrs ([#353](https://github.com/NevaMind-AI/memU/issues/353)) ([8676a27](https://github.com/NevaMind-AI/memU/commit/8676a2721917f4e0baee6d01a9d6b95bd642be97)) * add pull re

  4. v1.4.0v1.4.0Feb 6, 2026523 downloads

    ## [1.4.0](https://github.com/NevaMind-AI/memU/compare/v1.3.0...v1.4.0) (2026-02-06) ### Features * Add inline memory item references in category summaries ([#202](https://github.com/NevaMind-AI/memU/issues/202)) ([#205](https://github.com/NevaMind-AI/memU/issues/205)) ([5213571](https://github.com/NevaMind-AI/memU/commit/5213571b218d85784e0771f7a721eafd7da1c1ff)) * Add salience-aware memory with reinforcement tracking ([#186](https://github.com/NevaMind-AI/memU/issues/186)) ([#206](https://github.com/NevaMind-AI/memU/issues/206)) ([2bdbcce](https://github.com/NevaMind-AI/memU/commit/2bdbcce1a87ae1017d5930901fb0ae8d2924dcee)) * Add Tool Memory type with specialized metadata and statistics ([#247](https://github.com/NevaMind-AI/memU/issues/247)) ([4e8a035](https://github.com/NevaMind-AI/memU/commit/4e8a03578641afd0e07f9700629dff8d91d2b3fb)) ### Bug Fixes * add chat() API and stop misusing summarize() ([#208](https://github.com/NevaMind-AI/memU/issues/208)) ([be0a5c7](https://github.com/NevaMind-AI/memU/commit/be0a5c73250f0a21ad5e0d39b0e66e3018809e0f)) * remove unused type: ignore comment and add lazyllm mypy override ([#275](https://github.com/NevaMind-AI/memU/issues/275)) ([0

  5. v1.3.0v1.3.0Jan 29, 2026218 downloads

    ## [1.3.0](https://github.com/NevaMind-AI/memU/compare/v1.2.0...v1.3.0) (2026-01-29) ### Features * add happened at and extra fields to memory item ([#262](https://github.com/NevaMind-AI/memU/issues/262)) ([77938e9](https://github.com/NevaMind-AI/memU/commit/77938e9c282e1c0eda11088675c35975d85c4ff0)) * add proactive example ([#268](https://github.com/NevaMind-AI/memU/issues/268)) ([d3d1de1](https://github.com/NevaMind-AI/memU/commit/d3d1de1d9b0f45d9b14479cbaa4462458b172005)) * add Sealos support agent use case (Track G) ([#255](https://github.com/NevaMind-AI/memU/issues/255)) ([8fbdf3c](https://github.com/NevaMind-AI/memU/commit/8fbdf3c301f74f2aa85604837e00bb305b8801ec)) * integrate LazyLLM to provide more llm services ([#265](https://github.com/NevaMind-AI/memU/issues/265)) ([c03f639](https://github.com/NevaMind-AI/memU/commit/c03f639677d6c897b75dfe28d0cd92d5b5270957)) * **integrations:** Add LangGraph Adapter for MemU (Track A) ([#258](https://github.com/NevaMind-AI/memU/issues/258)) ([50b5502](https://github.com/NevaMind-AI/memU/commit/50b5502ebcacd86401f98b1bb7e5a6577fab7126)) * **llm:** add Grok (xAI) integration ([#179](https://github.com/NevaMind-AI/memU/issues/179)) ([#2

Code frequency

additions and deletions
+36K-36KWeek of 2025-08-10: +28,020 linesWeek of 2025-08-10: -36,012 linesWeek of 2025-08-17: +1,173 linesWeek of 2025-08-17: -79 linesWeek of 2025-08-24: +0 linesWeek of 2025-08-24: -0 linesWeek of 2025-08-31: +69 linesWeek of 2025-08-31: -63 linesWeek of 2025-09-07: +44 linesWeek of 2025-09-07: -90 linesWeek of 2025-09-14: +4,947 linesWeek of 2025-09-14: -7,188 linesWeek of 2025-09-21: +405 linesWeek of 2025-09-21: -76 linesWeek of 2025-09-28: +118 linesWeek of 2025-09-28: -152 linesWeek of 2025-10-05: +572 linesWeek of 2025-10-05: -622 linesWeek of 2025-10-12: +1,012 linesWeek of 2025-10-12: -327 linesWeek of 2025-10-19: +0 linesWeek of 2025-10-19: -0 linesWeek of 2025-10-26: +0 linesWeek of 2025-10-26: -0 linesWeek of 2025-11-02: +1 linesWeek of 2025-11-02: -0 linesWeek of 2025-11-09: +0 linesWeek of 2025-11-09: -0 linesWeek of 2025-11-16: +8,604 linesWeek of 2025-11-16: -32,524 linesWeek of 2025-11-23: +1 linesWeek of 2025-11-23: -1 linesWeek of 2025-11-30: +936 linesWeek of 2025-11-30: -295 linesWeek of 2025-12-07: +73 linesWeek of 2025-12-07: -26 linesWeek of 2025-12-14: +122 linesWeek of 2025-12-14: -5 linesWeek of 2025-12-21: +6,929 linesWeek of 2025-12-21: -2,526 linesWeek of 2025-12-28: +4,877 linesWeek of 2025-12-28: -884 linesWeek of 2026-01-04: +1,107 linesWeek of 2026-01-04: -1,441 linesWeek of 2026-01-11: +4,404 linesWeek of 2026-01-11: -593 linesWeek of 2026-01-18: +1,976 linesWeek of 2026-01-18: -575 linesWeek of 2026-01-25: +6,336 linesWeek of 2026-01-25: -1,526 linesWeek of 2026-02-01: +4,860 linesWeek of 2026-02-01: -84 linesWeek of 2026-02-08: +0 linesWeek of 2026-02-08: -0 linesWeek of 2026-02-15: +1 linesWeek of 2026-02-15: -0 linesWeek of 2026-02-22: +449 linesWeek of 2026-02-22: -12 linesWeek of 2026-03-01: +7 linesWeek of 2026-03-01: -5 linesWeek of 2026-03-08: +2 linesWeek of 2026-03-08: -2 linesWeek of 2026-03-15: +61 linesWeek of 2026-03-15: -14 linesWeek of 2026-03-22: +49 linesWeek of 2026-03-22: -8 linesWeek of 2026-03-29: +0 linesWeek of 2026-03-29: -0 linesWeek of 2026-04-05: +0 linesWeek of 2026-04-05: -0 linesWeek of 2026-04-12: +0 linesWeek of 2026-04-12: -0 linesWeek of 2026-04-19: +4 linesWeek of 2026-04-19: -4 linesWeek of 2026-04-26: +0 linesWeek of 2026-04-26: -0 linesWeek of 2026-05-03: +0 linesWeek of 2026-05-03: -0 linesWeek of 2026-05-10: +0 linesWeek of 2026-05-10: -0 linesWeek of 2026-05-17: +0 linesWeek of 2026-05-17: -0 linesWeek of 2026-05-24: +0 linesWeek of 2026-05-24: -0 linesWeek of 2026-05-31: +127 linesWeek of 2026-05-31: -322 linesWeek of 2026-06-07: +303 linesWeek of 2026-06-07: -232 linesWeek of 2026-06-14: +1,996 linesWeek of 2026-06-14: -2,716 linesWeek of 2026-06-21: +7,947 linesWeek of 2026-06-21: -2,427 linesWeek of 2026-06-28: +8,065 linesWeek of 2026-06-28: -4,693 linesWeek of 2026-07-05: +1,075 linesWeek of 2026-07-05: -266 linesWeek of 2026-07-12: +7,594 linesWeek of 2026-07-12: -32,843 linesWeek of 2026-07-19: +5,601 linesWeek of 2026-07-19: -1,525 linesWeek of 2026-07-26: +4,002 linesWeek of 2026-07-26: -781 linesWeek of 2026-08-02: +5,576 linesWeek of 2026-08-02: -452 linesAug 10, 2025Aug 2, 2026
+119.4K lines added, -131.4K removed over the last year.

Commits per week

last 52 weeks
540Week of 2025-08-10: 54 commitsWeek of 2025-08-17: 18 commitsWeek of 2025-08-24: 0 commitsWeek of 2025-08-31: 3 commitsWeek of 2025-09-07: 3 commitsWeek of 2025-09-14: 6 commitsWeek of 2025-09-21: 1 commitsWeek of 2025-09-28: 2 commitsWeek of 2025-10-05: 7 commitsWeek of 2025-10-12: 21 commitsWeek of 2025-10-19: 0 commitsWeek of 2025-10-26: 1 commitsWeek of 2025-11-02: 2 commitsWeek of 2025-11-09: 0 commitsWeek of 2025-11-16: 23 commitsWeek of 2025-11-23: 1 commitsWeek of 2025-11-30: 4 commitsWeek of 2025-12-07: 4 commitsWeek of 2025-12-14: 1 commitsWeek of 2025-12-21: 6 commitsWeek of 2025-12-28: 9 commitsWeek of 2026-01-04: 33 commitsWeek of 2026-01-11: 13 commitsWeek of 2026-01-18: 4 commitsWeek of 2026-01-25: 21 commitsWeek of 2026-02-01: 9 commitsWeek of 2026-02-08: 0 commitsWeek of 2026-02-15: 2 commitsWeek of 2026-02-22: 4 commitsWeek of 2026-03-01: 1 commitsWeek of 2026-03-08: 1 commitsWeek of 2026-03-15: 2 commitsWeek of 2026-03-22: 2 commitsWeek of 2026-03-29: 0 commitsWeek of 2026-04-05: 0 commitsWeek of 2026-04-12: 0 commitsWeek of 2026-04-19: 1 commitsWeek of 2026-04-26: 0 commitsWeek of 2026-05-03: 0 commitsWeek of 2026-05-10: 0 commitsWeek of 2026-05-17: 0 commitsWeek of 2026-05-24: 0 commitsWeek of 2026-05-31: 1 commitsWeek of 2026-06-07: 9 commitsWeek of 2026-06-14: 4 commitsWeek of 2026-06-21: 11 commitsWeek of 2026-06-28: 12 commitsWeek of 2026-07-05: 8 commitsWeek of 2026-07-12: 26 commitsWeek of 2026-07-19: 37 commitsWeek of 2026-07-26: 23 commitsWeek of 2026-08-02: 4 commitsAug 10, 2025Aug 2, 2026
394 commits in the last 52 weeks.

When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 3 commitsSun 1:00 — 1 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 1 commitsSun 8:00 — 0 commitsSun 9:00 — 1 commitsSun 10:00 — 0 commitsSun 11:00 — 1 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 3 commitsSun 15:00 — 3 commitsSun 16:00 — 3 commitsSun 17:00 — 2 commitsSun 18:00 — 4 commitsSun 19:00 — 1 commitsSun 20:00 — 2 commitsSun 21:00 — 1 commitsSun 22:00 — 0 commitsSun 23:00 — 2 commitsMon 0:00 — 1 commitsMon 1:00 — 3 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 1 commitsMon 6:00 — 2 commitsMon 7:00 — 1 commitsMon 8:00 — 0 commitsMon 9:00 — 1 commitsMon 10:00 — 4 commitsMon 11:00 — 3 commitsMon 12:00 — 1 commitsMon 13:00 — 4 commitsMon 14:00 — 4 commitsMon 15:00 — 5 commitsMon 16:00 — 4 commitsMon 17:00 — 6 commitsMon 18:00 — 5 commitsMon 19:00 — 2 commitsMon 20:00 — 3 commitsMon 21:00 — 4 commitsMon 22:00 — 1 commitsMon 23:00 — 0 commitsTue 0:00 — 5 commitsTue 1:00 — 5 commitsTue 2:00 — 1 commitsTue 3:00 — 3 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 1 commitsTue 7:00 — 1 commitsTue 8:00 — 0 commitsTue 9:00 — 4 commitsTue 10:00 — 9 commitsTue 11:00 — 3 commitsTue 12:00 — 3 commitsTue 13:00 — 2 commitsTue 14:00 — 7 commitsTue 15:00 — 1 commitsTue 16:00 — 4 commitsTue 17:00 — 1 commitsTue 18:00 — 0 commitsTue 19:00 — 3 commitsTue 20:00 — 1 commitsTue 21:00 — 3 commitsTue 22:00 — 5 commitsTue 23:00 — 1 commitsWed 0:00 — 4 commitsWed 1:00 — 1 commitsWed 2:00 — 2 commitsWed 3:00 — 1 commitsWed 4:00 — 0 commitsWed 5:00 — 2 commitsWed 6:00 — 3 commitsWed 7:00 — 2 commitsWed 8:00 — 2 commitsWed 9:00 — 1 commitsWed 10:00 — 2 commitsWed 11:00 — 3 commitsWed 12:00 — 4 commitsWed 13:00 — 5 commitsWed 14:00 — 1 commitsWed 15:00 — 2 commitsWed 16:00 — 6 commitsWed 17:00 — 2 commitsWed 18:00 — 3 commitsWed 19:00 — 3 commitsWed 20:00 — 3 commitsWed 21:00 — 5 commitsWed 22:00 — 6 commitsWed 23:00 — 14 commitsThu 0:00 — 8 commitsThu 1:00 — 4 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 1 commitsThu 6:00 — 2 commitsThu 7:00 — 1 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 4 commitsThu 11:00 — 3 commitsThu 12:00 — 7 commitsThu 13:00 — 7 commitsThu 14:00 — 12 commitsThu 15:00 — 3 commitsThu 16:00 — 4 commitsThu 17:00 — 4 commitsThu 18:00 — 4 commitsThu 19:00 — 1 commitsThu 20:00 — 0 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 2 commitsFri 0:00 — 6 commitsFri 1:00 — 0 commitsFri 2:00 — 1 commitsFri 3:00 — 6 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 2 commitsFri 9:00 — 7 commitsFri 10:00 — 2 commitsFri 11:00 — 6 commitsFri 12:00 — 4 commitsFri 13:00 — 4 commitsFri 14:00 — 5 commitsFri 15:00 — 6 commitsFri 16:00 — 11 commitsFri 17:00 — 9 commitsFri 18:00 — 3 commitsFri 19:00 — 5 commitsFri 20:00 — 3 commitsFri 21:00 — 4 commitsFri 22:00 — 3 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 1 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 2 commitsSat 10:00 — 2 commitsSat 11:00 — 0 commitsSat 12:00 — 3 commitsSat 13:00 — 5 commitsSat 14:00 — 2 commitsSat 15:00 — 4 commitsSat 16:00 — 2 commitsSat 17:00 — 2 commitsSat 18:00 — 2 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 3 commitsSat 23:00 — 3 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Feb 4, 2026daily#25+82
Jan 30, 2026daily#17+148
Jan 29, 2026daily#15+160
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