Open Source Manus Alternatives

OpenClaw is a personal AI assistant built to take action. It doesn't just answer questions. It clears your inbox, sends emails, manages your calendar, and handles tasks like flight check-ins, all triggered through chat apps you already use every day.

The core idea is that you shouldn't need a new app to talk to your assistant. OpenClaw works through WhatsApp, Telegram, Discord, Slack, Signal, and iMessage, so you interact with it wherever you already spend time. No dashboard to open, no new habit to build.

It connects to a wide range of services out of the box:

• Email and calendar via Gmail and similar tools, handling real actions, not just summaries
• AI models including Claude and GPT, so you can route requests through your preferred backend
• Productivity tools like Obsidian and GitHub for note-taking and code workflows
• Smart home and media through Philips Hue and Spotify
• Social and browser via Twitter and direct browser control

What sets it apart from most chat-based AI tools is the action layer. Many assistants can tell you what to do. OpenClaw does it. The integrations aren't read-only; it can send, create, update, and automate across connected services.

The project is open source and self-hostable, which matters if you're handing an assistant access to your email and calendar. You control where it runs and what it can reach. A companion menubar app is available for macOS users who want quick access without switching windows.

It's an independent project, not affiliated with Anthropic or any of the AI providers it connects to. That independence shows in the breadth of integrations rather than a walled ecosystem.

Hermes Agent is a desktop AI agent built by Nous Research that runs natively on macOS, Windows, and Linux. It's designed for people who want a single agent that works across their communication tools, handles recurring tasks, and actually remembers what it's done before.

The core idea is persistence. Most agents start fresh every session. Hermes maintains memory across conversations, auto-generates skills from past interactions, and builds up context about your projects over time. You don't have to re-explain things.

Where it connects:

• Telegram, Discord, Slack, WhatsApp, Signal, and email are all supported natively, so the agent meets you where you already work
• A CLI interface is also available for terminal-first workflows

What it can do:

• Natural-language scheduling for reports, backups, and briefings that run unattended
• Web search and browser automation with vision, image generation, and text-to-speech
• Subagents that run in isolated conversations with their own terminals and Python RPC scripts, useful for parallel pipelines without ballooning context costs
• Multi-model reasoning with access to 300+ models through the Nous Portal

Sandboxing is a genuine differentiator. Five execution backends (local, Docker, SSH, Singularity, and Modal) give you control over where code actually runs, with container hardening and namespace isolation. That matters if you're running automated tasks or untrusted scripts.

For teams or individuals already using LobeChat or similar multi-surface chat interfaces, Hermes takes a different angle: it's less about chat UI and more about an agent that operates autonomously across surfaces. Think of it as closer to an AgentOS approach, where the agent itself is the persistent layer.

It's free to use under the MIT license, with paid tiers through the Nous Portal for higher model access and monthly credits.

AutoGPT is an AI agent platform for people who want to hand off recurring work entirely, not just get help with a single task. You describe the outcome you want, and an agent plans and executes every step: pulling data, calling AI models, updating spreadsheets, sending messages, and looping back on a schedule. It's built for the kind of work that comes back every day or every week.

There are four ways to interact with the platform:

• AutoPilot lets you describe a job in plain English and builds the agent for you inside the conversation, no flowcharts or prompt engineering required.
• Visual Builder gives you a drag-and-drop canvas of blocks when you need exact control over branching, looping, and routing logic.
• Dashboard shows every agent's status, run history, and spend in one place, with alerts when an agent needs a decision from you.
• Marketplace offers ready-made agents from AutoGPT and the community, browsable without an account and deployable in one click.

On the integration side, the platform ships with dozens of AI models already connected (no API keys needed) and supports services like Gmail, Slack, Google Sheets, Notion, HubSpot, GitHub, and Airtable. Agents can handle chat, image generation, video, transcription, OCR, and more across 45+ platforms.

Common uses include morning briefings assembled from earnings and headlines, prospect research before sales calls, overnight campaign drafts, support ticket triage, and competitor monitoring that only pings you when something actually changes.

AutoGPT is fully open source. Self-hosting is free: you bring your own model API keys and infrastructure and get the same builder and agent runtime as the cloud. The hosted cloud removes the ops burden and covers model usage through a subscription. Teams often prototype in the cloud and move sensitive workloads on-prem later.

For teams that want a visual workflow automation approach rather than agent-based logic, tools like ByteChef or Automa take a different angle. If you're looking for an agent-first experience with a different focus, Eigent is worth comparing. AutoGPT's sweet spot is repeatable, multi-step work you can describe clearly enough to hand to a capable assistant.

LobeHub is a collaborative agent platform where AI agents join your team chats and take on work. You add an agent to Slack, Discord or Telegram, and it reads the channel history, docs and code before it does anything. It's built for teams that want routine requests handled without someone assigning every task.

The agent can act on its own. With the "speak up on its own" setting on, it spots a feature request that several people have +1'd, creates a task, and starts work without being tagged.

• Coding handoff: it passes the build to Claude Code or Codex and tracks the task
• Acceptance checks: each delivery comes with screenshots or recordings, and you can send back any check that's off
• Pull requests: the PR merges only after you accept
• Thread replies: it tells the people who asked once the work is live
• Permissions: you decide what it can do, for example opening PRs freely while deploys need your OK
• Daily brief: a home page lists what finished, what's running, and what needs your decision

The agent also learns. A Self-evolving page shows each area it practices, the lessons you've taught it, and how often it gets things right. Mistakes become lessons, and a rule it keeps breaking is flagged so you can teach it again.

You can start with one agent and add more later, such as separate agents for design, releases and docs. Each teammate can have their own too. Agents remember team preferences, like sending pricing copy to a specific person for sign-off.

nanobot is a minimal, self-hosted AI agent designed to run wherever you work: a web UI, your terminal, or a chat interface. It's built for people who want a personal AI agent they control, without the overhead of bloated frameworks or cloud-locked platforms.

The core design philosophy is lean execution. Rather than consuming tokens without restraint, nanobot applies sensible context management and explicit token budgets, so your costs stay predictable. That matters especially for long-horizon tasks, where the agent needs to sustain coherent reasoning across tens or even hundreds of steps without losing the thread.

Key capabilities:

• Multi-surface operation: runs in a browser-based web UI, a terminal, or directly inside chat apps, so you're not locked into one interface
• Long-running workflows: maintains steady execution across complex, multi-step tasks without context drift
• Token budgeting: built-in controls keep spending predictable, unlike agents that leave cost management entirely to the user
• Embeddable runtime kernel: the agent core is portable, so you can drop it into a business workflow or a personal automation setup without rewiring everything around it
• Self-hosted: your data stays on your infrastructure; no third-party service holds your conversations or task history

Compared to heavier agent frameworks or fully managed platforms like Agenta, nanobot stays deliberately small. The codebase is compact, the runtime is portable, and the architecture doesn't assume you want a full orchestration platform. It suits developers and technically minded individuals who want an agent they can embed, extend, or just run locally without a lot of ceremony.

It supports automation tasks, developer integrations, and conversational use cases through the same core runtime. The MIT license means you can adapt it freely for personal or commercial use.

Goose is a general-purpose AI agent that runs natively on your machine. It's not narrowly focused on coding: you can use it for research, writing, data analysis, browser automation, or any multi-step task you'd otherwise piece together manually. It ships as a desktop app for macOS, Linux, and Windows, a full CLI for terminal-first workflows, and an API for embedding it in other tools.

The LLM support is broad. Goose works with 15+ providers, including Anthropic, OpenAI, Google, Ollama, LocalAI, OpenRouter, Azure, and Bedrock. You can use API keys or connect through existing Claude, ChatGPT, or Gemini subscriptions, so you're not locked into a new billing relationship just to try it.

Extensibility is central to how goose works:

• 70+ MCP extensions connect it to databases, APIs, browsers, GitHub, Google Drive, and more via the open Model Context Protocol standard
• MCP Apps let extensions render interactive UIs inside the desktop app, including buttons, forms, and visualizations
• Recipes let you capture repeatable workflows as portable YAML configs, shareable with a team or runnable in CI
• Subagents can be spawned to handle tasks in parallel, keeping the main conversation focused
• Security controls include prompt injection detection, tool permission controls, and a sandbox mode

Goose also implements the Agent Client Protocol, which means it works as an ACP server you can connect from editors like Zed, JetBrains, or VS Code. It can use Codex and similar agents as providers through the same standard.

The project sits under the Agentic AI Foundation at the Linux Foundation, which keeps it vendor-neutral and community-governed. Built in Rust for performance, it has attracted 500+ contributors and 45k+ GitHub stars. If you want a self-hostable, provider-agnostic agent that works across interfaces and integrates deeply with the MCP ecosystem, goose covers a lot of ground.

Most AI assistants are a seat you rent on someone else's infrastructure. ZeroClaw runs as a single native binary on your own machine, using your own API keys or fully local models like Ollama. No cloud seat, no subscription, no ZeroClaw server in the middle.

The binary is built in Rust and starts in under 20ms. It uses less memory than a browser tab and runs on everything from a workstation to a Raspberry Pi, including ARM and x86. There's no Node, JVM, or Python environment to install.

What it connects to:

• 70+ LLM providers: local options like LM Studio, llama.cpp, vLLM, and LocalAI, plus hosted providers including Anthropic, OpenAI, Google Gemini, Amazon Bedrock, Mistral, Groq, and any OpenAI-compatible endpoint
• 30+ channels: Telegram, Discord, WhatsApp, Slack, Signal, iMessage, Matrix, email, IRC, Bluesky, Reddit, Nostr, Notion, webhooks, and more
• Hardware: GPIO on Raspberry Pi, STM32, Arduino, and ESP32

Security is the default posture, not an add-on.

• Supervised autonomy: medium-risk actions require your approval before they run; high-risk ones are blocked outright
• OS-level sandboxes: Landlock, Bubblewrap, Seatbelt, or Docker contain what the agent can touch, enforced by the kernel rather than a prompt
• Command allowlists: explicit workspace scoping decides what runs and where
• Tool receipts: every successful tool call can be stamped with an HMAC-SHA256 tag the model cannot forge, making fabricated runs or invented results detectable

YOLO mode exists for trusted dev environments and is strictly opt-in.

The agent supports multiple named agents running from a single daemon, each isolated. Skills package repeatable tasks with their tools. Scheduled jobs, webhook triggers, and channel events all run through the same agent loop inside the same sandbox and allowlists you've scoped. A2A discovery lets agents describe and find one another through the gateway.

With a local model, nothing leaves your machine at all. With a hosted provider, only your prompts go to the provider you chose, using your own key. Dual-licensed MIT OR Apache-2.0.

OpenWork is a desktop app (and browser-based workspace) that lets individuals and teams run AI agents against their own files, tools, and data. It's built on OpenCode and positions itself as an open alternative to Claude Cowork, supporting 50+ LLMs from providers like OpenAI, Anthropic, Google, Mistral, and local models, rather than locking you to a single vendor.

The core idea: bring your own API keys, wire in the tools your team already uses, and package everything into reusable setups that anyone can import in one click. No terminal required.

Key capabilities:

• Multi-model support – switch between OpenAI, Anthropic, Gemini, Bedrock, Azure AI Foundry, Mistral, OpenRouter, or local models. Connect your own keys or use a managed provider on cloud plans.
• Browser automation – describe a task in plain language and the agent executes it in a real browser. Like replies, extract data, fill forms, and save results to your machine.
• OpenWork Connect (MCP gateway) – add an MCP server or skill once, and every teammate gets it instantly across desktop, web, and any MCP-compatible client like Claude Code or Cursor. One URL covers your whole org, with auth, roles, and policies applied in transit.
• Skill and plugin sharing – Anthropic-compatible plugins and SKILL.md files work as-is. Teams package skills, configs, and MCP servers into a single shareable link.
• Local-first privacy – in desktop mode, files stay on your machine. Prompts go directly to the LLM provider you choose. Cloud workers are optional and sandboxed.
• Self-host or managed – run it on your own infrastructure or use OpenWork Cloud for central seat management, SSO, and bring-your-own inference.

For teams migrating from Claude Cowork, existing SKILL.md files and MCP servers import without modification. The free desktop app needs no account to start. Team plans add API access and the Extension Marketplace at $10 per seat per month after the first five free seats.

MausBot is a local-first Grok Bot alternative that lets you run a team of AI agents from a familiar chat interface. Each bot gets its own model, its own computer, and access to your connected apps. They work in parallel, come back when something needs your attention, and never proxy your credentials through a third party.

It runs on Mac, Windows, and Linux under Apache 2.0. Your transcripts, keys, and events stay in a local folder on your own disk. Local-first isn't a setting; it's the default.

Key capabilities:

• Model per bot. Assign Claude, Codex, or other models to individual bots and switch mid-conversation.
• Computer use. Each bot gets a desktop it can see and click, either a cloud desktop from a provider you connect or your local machine directly.
• Scheduled tasks. Inbox sweeps, weekly reports, competitor watches, PR reviews, and support triage all run on a calendar you define.
• Connected apps. Gmail, Slack, GitHub, Notion, Linear, and hundreds more via Composio. Authenticate once and every bot on the team can use them.
• Voice. Hit call and the bot hears you, narrates its work, and asks for approvals out loud.
• Playbooks. A whole team, its routines, and its app connections ship as a single Markdown file. Connections stay off until you approve them.
• Manage bots like contacts. Pin, duplicate, mark unread, hide, or delete. The interface is a messaging app, not a dashboard.

For teams that want bots running around the clock without leaving a laptop on, Cloud plans add always-on workspaces with scheduled tasks and voice included. You still bring your own Claude or ChatGPT account; MausBot never sits between you and the model provider.

Compared to other AI chat interfaces, MausBot is built specifically for multi-agent workflows where each agent has persistent tasks, tool access, and its own compute rather than just a shared conversation window.

Kortix is a platform for running AI agents across an entire company, where the whole setup lives in a single git repo you own. Agents, skills, memory, connectors, triggers, and permissions are all plain files. You can grep the company, diff any change, and roll anything back. No settings locked in someone else's database.

It fits teams that want AI agents doing real, multi-step work across sales, engineering, finance, marketing, and ops, not just answering questions. Agents start from Slack, MS Teams, the web, a CLI, a cron schedule, or a signed webhook. Work lands back in the repo as a change request you read as a diff before it merges.

The platform is built in six layers that work together:

• Git repo as the source of truth. kortix.yaml declares the machine image, connectors, and triggers. Agents and skills are markdown files. Memory accumulates as files over time.
• Connectors. 3,000+ app integrations plus MCP, OpenAPI, GraphQL, and raw HTTP. Credentials are brokered server-side and never reach the agent's machine. Each tool call can be set to allow, ask, or block, down to the specific arguments passed.
• Model-agnostic runtime. Pick any model per agent, per session, or per message. Bring your own API keys, use a ChatGPT subscription you already pay for, or point it at any OpenAI-compatible endpoint.
• Agent harness powered by OpenCode. Turns a model into an agent that plans, uses tools, and finishes multi-step runs. Permissions are set per tool in the agent file, so a git push can be blocked until a human reviews the change request.
• Isolated sandboxes. Each session boots its own Linux machine with the repo and tools already loaded. Thousands run in parallel with no crossover. Only commits survive.
• Unified control plane. Web, Slack, mobile, CLI, and API all start the same session type. Cron and webhooks start sessions with no human involved.

Practical examples from the source: an engineering agent reads error logs each morning, reproduces the top failure on its own machine, patches it, and opens a pull request. A finance agent reconciles the ledger, chases missing receipts, and closes the month with the variance explained. A data science agent queries the warehouse on a schedule and posts the chart to Slack before the team wakes up.

Kortix is self-hostable, VPC-deployable, or available as managed cloud. It sits in the AI coding agent orchestrators space but extends well beyond code, covering any business function that can be described as a repeatable job.

Agent Zero is an agentic AI framework built for people who want autonomous AI that actually does work, not just answers questions. It runs inside a sandboxed Docker environment with its own terminal, file system, and browser, so agents can execute real tasks end-to-end without touching your local machine unless you explicitly connect them.

The core idea is that agents shouldn't need pre-built tools for every situation. Agent Zero creates tools on the fly as tasks demand them, learns from past runs, and self-corrects when something goes wrong. Workflows are fully transparent: you can see what the agent is doing, why, and what it's executing at every step.

Key capabilities:

• Multi-provider support: Connect any LLM provider without exposing API keys to the agent itself.
• Dynamic tool creation: Agents write and reuse tools as needed rather than relying on a fixed library.
• Agentic memory and RAG: Persistent knowledge management lets agents recall context across sessions and build on prior work.
• Subordinate agents: Spawn specialized sub-agents to handle parallel or delegated tasks within a single workflow.
• Plugin Hub: Browse, install, and update community plugins directly from the UI, with built-in AI-driven security scanning before deployment.
• A0 CLI Connector: Bridge the sandboxed agent to your local terminal and project files when you need it.
• Context engineering: Prompt structure is tuned to stay efficient on local models and scale to larger ones without bloat.

Agent Zero suits developers and power users who want a self-hostable AI personal assistant they can extend, audit, and fully control. Unlike closed systems such as ChatGPT or Manus, every layer is inspectable and modifiable.

The project also has a community governance layer backed by the A0T token on Ethereum BASE L2, letting token holders vote on feature priorities and development allocation. Venice AI integration gives community members access to private AI API keys at no cost.

Eigent is a desktop application that puts a multi-agent AI workforce on your machine. You describe a task, and a pool of specialized agents divides it up, works in parallel, and delivers results. It's built for people who want AI to actually finish work, not just assist with it.

The core idea is "cowork": agents operate alongside you in your desktop environment with access to your local files, browser, and terminal. You can run a single focused agent for direct tasks or spin up a full workforce where agents collaborate across steps. A Browser Agent handles web research, a Document Agent reads and writes files, a Terminal Agent runs shell commands, and a Multi-modal Agent processes images and other media.

Key capabilities include:

• Scheduled automation: set recurring workflows so agents run tasks on a timer, even when you're not at your desk
• Local-first execution: files, credentials, and context stay on your machine; nothing has to leave it
• Model agnostic: connect cloud APIs, enterprise model gateways, or local inference. No vendor lock-in
• MCP and skill support: track which tools, skills, and referenced files each agent uses during a task
• Agent folder: every file an agent creates or modifies during a task is surfaced in one place for review

The open source version is fully self-hostable with your own API keys or local models. It's built on CAMEL-AI, a multi-agent framework, and runs on Mac, Windows, and Linux.

For teams doing research-heavy work, competitive analysis, report generation, or any task that involves pulling from multiple sources and producing structured output, Eigent is closer to OpenHands than a simple chat interface. It's designed to complete multi-step work end-to-end rather than hand control back to you at every turn.

Rakazo is a self-hosted platform for running AI agents that actually do things. Each bot gets a sandboxed browser and shell, signs into your tools the way you would, and completes tasks while you're away. It's a direct Grok Bot alternative built around one principle: you own everything.

Bots run in Docker on your own machine, behind your firewall. Sessions and credentials never leave your infrastructure. You point each bot at whatever model you want (Claude, GPT, Grok, or a local model), and you can mix and match per bot. The cheap model triages; the smart one writes.

Setup starts with an interview. A new bot asks you a few questions about the work, your writing style, and which tools it should use. Then it gets going.

What bots can handle out of the box:

• Inbox Manager archives noise, replies to routine threads, and parks drafts for your review
• Sales Outbound researches accounts overnight, scores intent, and drafts outreach in your voice
• Talent Scout reads applicants, shortlists against your criteria, and writes intro emails
• Bug Triage reproduces reports in a real browser and attaches reproduction steps to the issue
• Expense Manager matches receipts to charges and files reports, asking before guessing
• Chief of Staff runs briefings, bookings, and handoffs between your other bots

Routines are saved as plain Markdown. Show a bot a workflow once and it writes a routine you can read, edit, and commit to version control. Approvals are configurable: set what a bot may do alone and what it must ask you about first. Every action lands in an audit log you own.

Rakazo is Apache-2.0 licensed with no seats, no pricing tiers, and nothing gated. Unlimited bots, no limits on usage. A managed cloud option (where you bring your own keys and they run the sandboxes) is planned, with no migration required when it arrives.

OpenBot turns AI models into a team of persistent agents that work together on your computer. Instead of one chatbot answering questions, you get named agents with individual instructions, workspaces, and conversation histories. They message each other, hand off tasks, and review each other's work, so a bug found by one agent can be fixed by another and merged by a third, without you orchestrating every step.

The app is free for personal use and runs on macOS, Windows, and Linux. Workspaces, conversations, attachments, and browser data live on your machine, though agents still connect to the model providers you choose.

What agents can do:

• Use plans you already pay for. Connect Codex, Claude Code, Gemini, Grok, OpenCode, Cline, Cursor, or any OpenAI-compatible endpoint, including local models via Ollama or LM Studio.
• Built-in browser. Agents open, read, and control web pages inside a browser that's part of the app, so they can interact with live sites without extra setup.
• Task queue. Send more work while an agent is busy. Messages wait in a queue you can pause, resume, or cancel.
• Automatic failover. When a plan hits its usage limit or a provider goes down, an agent switches to a backup model and keeps its chat and workspace intact.
• Agent persistence. An agent keeps its full context when you restart the app or switch it to a different provider.

Each agent has its own name and instruction set. You describe what you want in a prompt, review the generated instructions, and the agent is ready. Teams grow naturally: add more agents and they coordinate around whatever work is in flight.

For teams that need always-on availability, a managed server option runs OpenBot outside any single laptop, with shared or per-employee environments and custom integrations into existing tools and APIs. The source code is available under the PolyForm Noncommercial license, so you can audit what the app does, but commercial use requires a paid license.