Eigent: Free Open-Source Multi-Agent Desktop AI (2026)
Eigent is a free open-source multi-agent desktop: 4 parallel AI workers run locally on CAMEL-AI. Paid managed plans start at $19.90/month with 2,000 credits.
Eigent is an open-source, local-first multi-agent AI desktop built by the CAMEL-AI team. It runs 4 specialized AI agents in parallel on your machine, keeping data private. The core is free (Apache 2.0); cloud-managed Plus plans start at $19.90/month. Best for developers and teams that want automation control without giving data to a cloud provider. In public beta as of 2026.
Eigent is a free, open-source multi-agent AI desktop built on CAMEL-AI by EIGENT UK LTD (London, 2023). It deploys 4 parallel specialized AI workers (Developer, Browser, Document, Multi-Modal) that run entirely on your machine. The open-source core is free; managed plans start at $19.90/month. Eigent supports 10+ LLM providers including GPT, Claude, Gemini, and local models via Ollama.
Maker: Eigent AI · Autonomy: semi autonomous · Maturity: BETA
Underlying models: GPT-4o, Claude Sonnet, Claude Opus, Gemini 3 Pro, GLM-4.7 (Z.AI), MiniMax M2.5, Local models via Ollama, Local models via vLLM
About Eigent
Eigent is an open-source multi-agent AI desktop built by EIGENT UK LTD, a London-based startup founded in 2023 by the team behind CAMEL-AI, the academic multi-agent framework with over 35,000 GitHub stars and 200+ contributors. Rather than replacing a single assistant with a faster one, Eigent deploys a team of 4 specialized AI workers in parallel on your own machine: a Developer Agent that writes and runs code, a Browser Agent that navigates the web, a Document Agent that processes files, and a Multi-Modal Agent that handles images and audio. Each worker in Eigent is backed by the CAMEL-AI orchestration framework, which routes subtasks to the appropriate specialist and coordinates results into a coherent output. Eigent is model-agnostic: you can connect GPT-4o, Claude Sonnet or Opus, Gemini Pro, GLM-4.7 from Z.AI, MiniMax M2.1, or a local model running via Ollama or vLLM, all from the same desktop interface. The platform ships with more than 200 built-in Model Context Protocol (MCP) tools covering browser automation, code execution, Google Workspace, Notion, Slack, and mobile phone control via the Mobile MCP, plus you can install additional MCP servers for custom integrations. Eigent fits developers and technical teams who need to automate long, multi-step workflows without sending sensitive data to a cloud provider. A legal team can have the Document Agent parse contracts while the Browser Agent fetches jurisdiction-specific case law, all without any file leaving the local machine. A sales team can use the Browser Agent for lead capture and the Developer Agent to build a custom report, coordinated in a single prompt. The platform suits startups, regulated industries, and anyone who has hit the limits of a single-threaded AI assistant. Pricing starts at $0 for self-hosting the open-source Apache 2.0 core via GitHub. For users who prefer a managed experience, the Plus plan costs $19.90/month billed yearly (or $24.99/month billed monthly) and includes 2,000 monthly task credits with a 7-day free trial. The Pro plan is $99.99/month yearly or $129.99/month monthly with 10,000 monthly credits. A Teams plan with centralized billing and seat management is in development, and Enterprise pricing is available on request for organizations that need custom SSO, dedicated deployment, and compliance tooling. Eigent reached v0.0.90 as of April 2026 and ships updates at high frequency, having added support for Gemini 3 Pro, MiniMax M2.5, Z.AI GLM-4.7, and mobile phone automation since launch. The project passed $250,000 in revenue within three months of its product launch. 10% of every subscription goes back to funding CAMEL-AI.org research into the scaling laws of multi-agent systems, making it one of the faster-moving open-source agent platforms in 2026.
Pricing
Open-source self-hosted core is free (Apache 2.0, GitHub). Managed cloud plans: Plus $19.90/month (yearly) or $24.99/month (monthly), 2,000 task credits/month, 7-day free trial. Pro $99.99/month (yearly) or $129.99/month (monthly), 10,000 task credits/month. Enterprise: custom pricing with local deployment and SSO.
Key Features
- 4-Agent Parallel Workforce: Eigent deploys Developer, Browser, Document, and Multi-Modal agents simultaneously on a single task, completing multi-step workflows in parallel rather than sequentially.
- 200+ Built-in MCP Tools: Ships with over 200 Model Context Protocol tools covering browser automation, terminal commands, Google Workspace, Notion, Slack, and mobile phone control via Mobile MCP, with support for adding custom MCP servers.
- Model-Agnostic BYOK: Connects to 10+ LLM providers including OpenAI, Anthropic, Google Gemini, Z.AI, MiniMax, and local models via Ollama or vLLM, so you are not locked into a single model vendor.
- Local-First Privacy: The entire agent workforce runs on your own machine or self-hosted server; files, credentials, and workflow data never leave your environment unless you explicitly connect a cloud API.
- Apache 2.0 Open Source: The full codebase is publicly available on GitHub with 35,000+ stars and 200+ contributors; you can inspect, fork, and modify the orchestration logic and MCP integrations.
- Human-in-the-Loop Checkpoints: When an agent hits uncertainty or a step requiring a judgment call, Eigent pauses and asks the user rather than guessing, reducing costly automated mistakes in long workflows.
Strengths
- Free open-source core under Apache 2.0 means $0 in software costs; you pay only for LLM API calls or local compute, which can be significantly cheaper than managed agents at scale.
- Runs 4 specialized agents in parallel rather than sequentially, cutting wall-clock time on multi-step workflows that involve web research, coding, and document processing in a single task.
- Model-agnostic design lets you swap GPT-4o for a local Llama 3 model in one settings change, giving control over cost, latency, and data residency that closed platforms cannot match.
Weaknesses
- Self-hosted setup requires Docker, PostgreSQL, and Node.js configuration plus an LLM API key or local Ollama install; non-technical users will find this barrier too high compared to a managed agent like Manus.
- No published SWE-bench or GAIA benchmark scores as of June 2026, making direct performance comparisons with specialized coding or research agents difficult to verify.
- Managed cloud Plus plan's 2,000 monthly task credits may run out quickly on large or frequent workflows, and overage costs are not clearly listed, creating budget unpredictability.
Frequently Asked Questions
What is Eigent and what does it do?
Eigent is an open-source multi-agent AI desktop application created by EIGENT UK LTD, a London startup founded in 2023 by the team behind the CAMEL-AI framework, which has over 35,000 GitHub stars. Instead of a single AI assistant, Eigent deploys a coordinated team of 4 specialized AI workers: a Developer Agent, a Browser Agent, a Document Agent, and a Multi-Modal Agent. These workers run in parallel on your own machine, handling different parts of a complex task simultaneously rather than sequentially. The platform positions itself as a free, local-first alternative to managed services like Claude Cowork, released under the Apache 2.0 open-source license. You can run the core entirely offline with local models via Ollama, or connect cloud APIs from OpenAI, Anthropic, Google, and others. Eigent reached v0.0.90 in April 2026 and is in public beta, with a managed cloud tier available alongside the free self-hosted option.
How much does Eigent cost?
Eigent's open-source core is free to self-host under the Apache 2.0 license; your only costs are your own hardware and any external API keys you connect for cloud LLMs. For users who want a managed cloud experience, Eigent offers two paid individual tiers. The Plus plan is $19.90/month billed annually (or $24.99/month billed monthly) and includes 2,000 monthly task credits plus a 7-day free trial with up to 1,000 credits total and a 300 credit daily cap. The Pro plan is $99.99/month billed annually (or $129.99/month monthly) and includes 10,000 monthly task credits with the same 7-day trial. A Teams plan with shared libraries and centralized billing is listed as coming soon, with no announced price. Enterprise pricing is custom and covers local deployment, enterprise-grade security, custom integrations, and dedicated support. Eigent also donates 10% of every subscription to open-source CAMEL-AI research.
Is Eigent fully autonomous?
Eigent is semi-autonomous: it can plan and execute complex, multi-step workflows end-to-end, but it is designed to pause and ask for human input when it encounters uncertainty or a step that requires a judgment call it cannot resolve. In practice, you describe a goal in plain language and Eigent's orchestrator breaks it into subtasks, assigns each to the right specialized worker, and runs them in parallel without further instructions from you for the bulk of the task. However, if the Browser Agent encounters an unexpected CAPTCHA or the Developer Agent is unsure which of two code approaches to take, it will surface the question to the user rather than guess. This behavior is intentional: the CAMEL-AI framework behind Eigent was built for research into safe multi-agent coordination, not just raw throughput. For well-defined, repetitive workflows that you have already run and validated, Eigent operates with minimal interruption. Compared to fully autonomous cloud agents, Eigent's checkpoint design trades a small amount of speed for a meaningful reduction in costly automated mistakes.
What AI model powers Eigent?
Eigent is model-agnostic and does not ship with a fixed underlying LLM; instead, you connect whatever model fits your needs and budget via your own API key. Officially supported cloud providers include OpenAI (GPT-4o and later), Anthropic (Claude Sonnet and Opus), Google (Gemini Pro and Gemini 3 Pro), Z.AI (GLM-4.7 and GLM-5), and MiniMax (M2.1 and M2.5). For local inference, Eigent connects to Ollama, vLLM, SGLang, and LLaMA.cpp, which means models like Llama 3, Mistral, and Qwen run entirely on your machine with no data leaving it. The CAMEL-AI orchestration layer handles routing tasks between models, so you can mix a fast local model for simple subtasks and a more capable cloud model for reasoning-heavy steps in the same workflow. This bring-your-own-key (BYOK) approach lets organizations avoid vendor lock-in and optimize cost per task. Users can switch providers from the settings panel without changing any workflow definitions.
What are the best alternatives to Eigent?
The closest direct competitor is OpenClaw, another open-source multi-agent platform; OpenClaw takes a chat-first approach integrated with messaging apps like Slack and WhatsApp, while Eigent provides a dedicated desktop UI with stronger parallel-agent execution and a visual workflow builder. Manus is a fully managed cloud alternative with no install or configuration required; you describe a task and Manus handles research, coding, and file creation end-to-end, but you have no control over data privacy or model choice. Claude Cowork is Anthropic's proprietary multi-agent workspace offering the tightest integration with Claude models and a polished interface, but it is a paid cloud subscription with no self-hosted option. Microsoft AutoGen and CrewAI are developer SDKs rather than desktop applications; they suit teams that want to build a custom multi-agent pipeline in code rather than drive one through a GUI. Eigent is the best option when local execution, model flexibility, and cost efficiency matter more than out-of-the-box polish.
Who is Eigent best for?
Eigent is best for developers, data engineers, and technical product teams at startups or in regulated industries who need to automate complex workflows without sending sensitive data to a cloud provider. Legal and compliance analysts can use it to parse large document batches locally without any file leaving their environment. Sales teams building outbound sequences can combine the Browser Agent for prospect research and the Document Agent for email drafts in a single coordinated workflow. Researchers can have the Browser Agent pull papers, the Document Agent summarize them, and the Developer Agent run analysis scripts, all in parallel. Eigent is NOT a good fit for non-technical users who want a plug-and-play experience: the self-hosted setup requires Docker, a PostgreSQL database, and familiarity with API keys. Users who want a managed, no-install experience will find Manus or Claude Cowork easier to start with, even if those cost more or offer less control.
How does Eigent compare on benchmarks?
Eigent has not published results on standard agent benchmarks such as SWE-bench Verified, WebArena, or GAIA as of June 2026. Because Eigent is model-agnostic, its task performance depends entirely on which underlying LLM you connect; using GPT-4o or Claude Opus 4.8 (which score around 48-55% on SWE-bench Verified as of mid-2026) produces very different results than using a 7B local model. The platform's own signal for real-world adoption is that it generated over $250,000 in revenue within three months of launch. The open-source codebase on GitHub has accumulated 35,000+ stars and contributions from 200+ developers, indicating meaningful community validation. For multi-step desktop automation, Eigent's parallel-agent architecture is categorically different from single-agent benchmarks, so direct score comparisons do not fully represent its value. We will update this section when Eigent publishes official benchmark results.
How do you get started with Eigent?
To use the self-hosted version, clone the repository at github.com/eigent-ai/eigent, which requires Docker (for the PostgreSQL backend), Node.js, and at least one LLM API key or a local Ollama installation; expect around 20-30 minutes for initial setup. The managed cloud version is faster: visit eigent.ai, create an account, and receive 500 registration credits plus a 7-day free trial of up to 1,000 total credits (300 per day maximum), with no local install required. Once signed in or set up locally, open the Eigent desktop application, connect your preferred LLM provider in Settings, and type a goal into the task prompt. Eigent's orchestrator automatically breaks your goal into subtasks and assigns each to the right specialized worker, so you do not need to write a workflow yourself. First-time users typically start with a single-agent task such as web research (Browser Agent) or document summarization (Document Agent) before running combined multi-agent workflows. The platform also supports installing additional MCP servers from the community to extend what each worker can do.