openhuman

Open source alternative to Muse, Vellum AI, Sai by Simular, Manus AI, Alfred and Grok Bot

42.2K stars
GPL-3.0
Active this week
Last commit 1 day ago

Last verified Oct 20, 2026

Orchestrate AI agents from your desktop with a local-first memory engine, visual workflow canvas, and 100+ app integrations. GPL-3.0.

Visit website GitHub42.2K 1-click deploy on Hostinger

Read the full breakdownWho it is for, how it works, trade-offs and the FAQ

who it's for

Who openhuman is for#

Knowledge workers who need persistent AI context across apps

OpenHuman pulls your inbox, calendar, documents, and messaging into a single local memory, so the AI already knows your current projects, recurring contacts, and open threads. Unlike starting a chat session cold, the agent can draft a reply that accounts for a conversation from earlier in the week.

Skip if:

Skip if you primarily work in a single app and do not need cross-app context. A simpler AI assistant with fewer integrations will have less setup friction and fewer beta-stability risks.

Developers building multi-step agent automations

The visual workflow canvas lets you define trigger-driven automations across GitHub, Slack, Gmail, and other connected apps. Agent-proposed workflows reduce the time from idea to running automation; the approval gate keeps side effects controlled. Workflows run as durable checkpointed graphs, so a restart does not lose progress.

Skip if:

Skip if your automation needs fit a single-tool Zapier or n8n flow. OpenHuman's orchestrator is most valuable when agents need to coordinate across multiple apps with persistent memory.

Privacy-conscious users who need local-only AI

Privacy Mode enforces that no inference leaves the device, running against a local Ollama model. All memory is stored encrypted on-device and secrets are held in the OS keyring. This makes OpenHuman one of few agent harnesses that can operate without sending any data to a cloud AI provider.

Skip if:

Skip if you need frontier model capability and are comfortable with cloud providers processing your prompts. Local models currently lag frontier models on complex reasoning tasks.

Researchers managing large personal knowledge bases

OpenHuman's Memory Tree compresses and scores documents into a local Obsidian-compatible wiki. The auto-fetch loop keeps the knowledge base current from connected sources. For researchers who ingest information heavily, the agent surfaces relevant prior notes before you finish asking, rather than requiring a manual search.

Skip if:

Skip if your research material is already well-organized in a dedicated PKM tool with strong search. OpenHuman adds most value when context is scattered across multiple unconnected apps.

the problem

The problem it solves#

Most AI assistants start cold with each conversation, forgetting everything from prior sessions. They have no knowledge of your inbox, your current projects, your recurring contacts, or how you prefer to work. Paid AI agent platforms like Manus AI and Vellum do store some context, but that knowledge lives on their servers under their pricing and privacy policies. A subscription change or a service shutdown takes it with them.

The result is an AI that can answer questions but cannot act meaningfully on your world. It cannot draft an email that accounts for a conversation from last week, build a workflow that triggers from your calendar, or coordinate multiple agents on a task without you re-supplying context manually. Every useful cross-app action requires explaining what you already know.

how openhuman solves it

How it solves it#

Local memory tree with Obsidian export

OpenHuman compresses your emails, documents, chats, and calendar data into scored Markdown files stored in SQLite on your device. These files are mirrored as an Obsidian vault you can open, edit, and version directly. An auto-fetch loop refreshes connected apps every 20 minutes, keeping the agent's context current without manual updates.

Agent-proposed visual workflow canvas

Ask for an automation and OpenHuman proposes a workflow graph you review on a visual canvas before saving. Workflows are durable and trigger-driven: they fire on schedules, webhooks, or channel events, survive restarts, and gate side effects behind approval steps. The underlying engine is the open source tinyflows library.

100+ OAuth integrations and 5,000+ MCP servers

One-click connections to Gmail, Notion, GitHub, Slack, Excel, Teams, and 100+ other apps via OAuth. Beyond OAuth, OpenHuman connects to over 5,000 MCP servers and 90,000 Skills, giving agents a broad toolkit without requiring per-connection configuration.

Checkpointed agent graph orchestrator

Agent runs execute as checkpointed graphs on tinyagents, OpenHuman's open source harness. Graphs pause for human approval, survive application restarts, and resume mid-run. Stuck agents produce root-cause reports rather than silent failures, and sub-agent fleets spawn three levels deep.

Privacy Mode: no inference leaves your device

A single toggle switches OpenHuman to Privacy Mode, where all inference runs locally via Ollama or another local model. This is enforced at the Rust core level, not just a preference setting. On-device encryption and OS-keyring secret storage protect all data at rest in all modes.

17 messaging channels including native email

OpenHuman connects to Telegram, Discord, Slack, WhatsApp, Signal, iMessage, and 11 other channels. Native email support uses IMAP IDLE and SMTP directly, so your agent can read, respond to, and send email without an intermediary API or third-party service.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Full working context in one sync passAfter connecting your accounts, OpenHuman's auto-fetch pulls data from your inbox, calendar, repos, and docs into local Markdown memory files. A single sync pass gives the agent compressed context across your working life, compared to cloud agent platforms that hold no persistent memory between sessions or require weeks of use before becoming useful.
GPL-3.0 with no per-seat fees for the desktop appThe desktop application is GPL-3.0 licensed and free to run. Unlike paid agent platforms that charge per-seat or per-API-call, there are no recurring fees to run OpenHuman on your own hardware. A managed subscription is optional and covers web search and media generation, but the core agent, memory, and workflow features are included.
TokenJuice cuts token costs by up to 80%TokenJuice compresses tool output before it reaches the model, preserving the same information in fewer tokens. OpenHuman's documentation cites up to 80% token reduction on tool output. This directly reduces API costs for users running their own provider keys, making a memory-rich agent harness affordable to operate.
Local model and BYOK support with built-in routingModel routing selects the right LLM per workload from a single OpenHuman subscription. You can also point any workload at your own provider API key or a fully local Ollama model, mixing the three approaches however fits your privacy or cost requirements. No single-provider lock-in.

Trade-offs

-Early beta with 300+ open issuesThe README explicitly badges OpenHuman as Early Beta and warns to expect rough edges. The GitHub repository carries over 300 open issues. Users who need production stability for critical workflows should verify whether the current release quality meets their requirements before committing to OpenHuman.
-GPL-3.0 restricts proprietary redistributionGPL-3.0 means that if you modify OpenHuman and distribute it or run it as a service for others, you must publish your modifications under the same license. Teams who want to build a proprietary product on top of OpenHuman should review the license implications before doing so.
-Building from source requires a complex toolchainContributing from source requires Node.js 24+, pnpm 10.10.0, Rust 1.93.0 with rustfmt and clippy, CMake, Ninja, and ripgrep, plus platform-specific desktop build prerequisites. Binary installers are available for end users, but the contributor setup cost is well above that of a typical web application.

versus alternatives

openhuman vs alternatives#

OpenHuman vs Manus AI

Both OpenHuman and Manus AI are autonomous AI agent platforms that execute multi-step tasks across connected apps. The key difference is where your data and workflow logic live.

Manus AI is a proprietary cloud service: your tasks, integrations, and agent context are managed on Manus servers under a paid subscription. OpenHuman is GPL-3.0 and installs as a native desktop application, keeping all memory and workflow definitions local on your device. For users with privacy requirements or who need to avoid per-run billing, OpenHuman provides a self-operated path.

Manus AI is the better choice when you need a production-stable service with managed reliability and vendor support. OpenHuman is early beta software with over 300 open issues, making it less suitable for teams who cannot tolerate instability in a critical workflow.

OpenHuman vs Alfred

Alfred is a macOS productivity launcher with automated workflows (Alfred Workflows) available via the Alfred Powerpack, a one-time purchase. OpenHuman is a cross-platform desktop AI agent with persistent memory and LLM-driven orchestration.

Alfred excels at fast, keyboard-driven macOS automation: launching apps, running scripts, and triggering predefined workflows. It does not include an AI reasoning layer, persistent cross-app memory, or support for Windows and Linux.

OpenHuman adds an AI reasoning layer: instead of triggering a predefined workflow, you describe a goal and the agent proposes and executes the steps. It also runs on Windows and Linux. Alfred wins when you need fast, reliable, stable workflow automation on macOS without an AI layer or beta instability. OpenHuman wins when you need an agent that reasons across your data and coordinates multi-app tasks with persistent memory.

tech stack · detected from GitHub

What it's built on#

Languages: JavaScriptRustTypeScript
Frameworks: React

frequently asked

FAQ#

Is OpenHuman free to use?

The desktop application is free to download and run under GPL-3.0. A managed subscription is available that adds web search (powered by Exa) and media generation, but the core agent, memory, and workflow features work without it. Running your own API keys or a local Ollama model keeps all inference costs under your control.

Does OpenHuman send my data to the cloud?

By default, inference calls go to whichever AI provider you configure. Enabling Privacy Mode switches all inference to a local model and, per the README, enforces that no data leaves your machine at the Rust core level. Memory files and secrets are stored encrypted on-device in all modes.

What operating systems does OpenHuman support?

OpenHuman provides native desktop installers for macOS, Windows, and Linux. Linux users can also install via Homebrew, a Debian/Ubuntu .deb package, or AUR. The README references platform-specific build prerequisites for contributors building from source.

How is OpenHuman different from workflow tools like n8n or Zapier?

n8n and Zapier are workflow automation tools where you define each step manually. OpenHuman's workflow engine (tinyflows) generates the workflow for you based on what you ask for, which you then review and approve on a visual canvas. The agent also has persistent memory of your data, enabling context-aware automations rather than simple trigger-action flows.

What does the GPL-3.0 license mean in practice?

You can use, modify, and self-host OpenHuman freely. If you distribute a modified version or run it as a service for others, GPL-3.0 requires you to publish your modifications under the same license. Teams building proprietary products on top of OpenHuman should review the license with a lawyer before doing so.

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OpenHuman vs Manus AI

Both OpenHuman and Manus AI are autonomous AI agent platforms that execute multi-step tasks across connected apps. The key difference is where your data and workflow logic live.

Who openhuman is for#

Knowledge workers who need persistent AI context across apps

Skip if:

Developers building multi-step agent automations

Skip if:

Privacy-conscious users who need local-only AI

Skip if:

Researchers managing large personal knowledge bases

Skip if:

The problem it solves#

How it solves it#

Local memory tree with Obsidian export

Agent-proposed visual workflow canvas

100+ OAuth integrations and 5,000+ MCP servers

Checkpointed agent graph orchestrator

Privacy Mode: no inference leaves your device

17 messaging channels including native email

Strengths and trade-offs#

Strengths

Trade-offs

openhuman vs alternatives#

OpenHuman vs Manus AI

OpenHuman vs Alfred

What it's built on#

FAQ#

Other ways to replace Muse, Vellum AI, Sai by Simular, Manus AI, Alfred and Grok Bot#

Open source tools in these categories#

AI & Machine Learning

Product & Project Management

Communication & Collaboration

Find open source alternatives to paid software

Who openhuman is for#

Knowledge workers who need persistent AI context across apps

Skip if:

Developers building multi-step agent automations

Skip if:

Privacy-conscious users who need local-only AI

Skip if:

Researchers managing large personal knowledge bases

Skip if:

The problem it solves#

How it solves it#

Local memory tree with Obsidian export

Agent-proposed visual workflow canvas

100+ OAuth integrations and 5,000+ MCP servers

Checkpointed agent graph orchestrator

Privacy Mode: no inference leaves your device

17 messaging channels including native email

Strengths and trade-offs#

Strengths

Trade-offs

openhuman vs alternatives#

OpenHuman vs Manus AI

OpenHuman vs Alfred

What it's built on#

FAQ#

Other ways to replace Muse, Vellum AI, Sai by Simular, Manus AI, Alfred and Grok Bot#

Open source tools in these categories#

AI & Machine Learning

Product & Project Management

Communication & Collaboration

Find open source alternatives to paid software