AI Memory & Search🔴Developer

Agent Cloud

Name: Agent Cloud
Brand: Agent Cloud
Availability: InStock

Open-source platform for building private AI apps with RAG pipelines, multi-agent automation, and 260+ data source integrations — fully self-hosted for complete data sovereignty.

Starting at$0 (free under AGPL 3.0)

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In Plain English

Agent Cloud is like having your own private ChatGPT that you can train on your company's data. It's an open-source platform that lets you build AI chatbots and automated workflows that can access information from your databases, documents, and other business systems, all while keeping your data completely private and secure on your own servers.

Overview

Agent Cloud represents a fundamental shift in how organizations approach enterprise AI application development, providing a complete self-hosted alternative to proprietary AI platforms while delivering the sophisticated features modern businesses require. In 2026, as data privacy regulations tighten globally and organizations face increasing scrutiny over how they handle sensitive information, Agent Cloud offers a compelling path to AI adoption without sacrificing data sovereignty.

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Key Features

End-to-end RAG pipeline with native Airbyte integration supporting 260+ data sources including PostgreSQL, Snowflake, BigQuery, Confluence, Notion, and direct file uploads (PDF, DOCX, CSV, XLSX) — far exceeding the 20-30 connectors typical of competing platforms like Langflow or Flowise+

Multi-agent automation powered by CrewAI enabling sophisticated workflow orchestration where specialized agents collaborate on complex tasks like customer service triage, content creation pipelines, and automated research — capabilities that single-agent platforms like Botpress cannot match+

Complete data sovereignty with fully self-hosted deployment supporting air-gapped environments through local LLM support via LM Studio and Ollama, ensuring zero data leaves the organization's infrastructure+

Intuitive Next.js GUI for conversational app creation, agent configuration, tool integration, data source management, and team permissions — no command-line expertise required for day-to-day operations+

Flexible LLM provider support spanning local models (LM Studio, Ollama) and cloud services (OpenAI, Azure OpenAI), enabling hybrid deployment strategies that balance performance with data privacy requirements+

High-performance Rust vector proxy communicating with Qdrant for fast similarity search across large document collections, with additional support for Pinecone vector database+

Enterprise-grade team and user management with granular permissions controlling access to specific agents, data sources, and workflows — a critical capability missing from many open-source alternatives like PrivateGPT+

Automated data synchronization with configurable refresh schedules (manual, scheduled, or cron-based) ensuring AI agents always work with current information rather than stale data snapshots+

Docker-based deployment with single-command installation scripts for Mac and Linux, reducing setup time from weeks (typical of enterprise platforms) to under an hour+

Open-source transparency under AGPL 3.0 license enabling community security audits, custom modifications, and integration of proprietary extensions for specialized use cases+

Pricing Plans

Plan 1

$0 (free under AGPL 3.0)

Plan 2

Usage-based; estimated $500–$2,000+/month (contact sales for exact quote)

Plan 3

Annual contract; estimated $25,000–$100,000+/year (custom quote required)

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with Agent Cloud

1Clone the Agent Cloud repository from GitHub (git clone https://github.com/rnadigital/agentcloud.git) and ensure Docker is installed with at least 16 GB RAM allocated to the Docker engine
2Run the automated installer with 'chmod +x install.sh && ./install.sh' on Mac or Linux, then follow the interactive prompts to configure your LLM provider (local via Ollama/LM Studio or cloud via OpenAI API key)
3Open the Agent Cloud web interface at localhost:3000, create your first agent by selecting an LLM model and defining its role and instructions through the visual agent builder
4Connect your first data source using the Data Sources panel — choose from 260+ connectors (e.g., upload a PDF, connect a PostgreSQL database, or link a Confluence workspace) and configure the sync schedule
5Start a new chat session with your configured agent to test RAG responses against your connected data, then iterate on chunking settings and agent instructions to optimize answer quality

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Best Use Cases

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Regulated enterprises (healthcare, finance, public sector) that need RAG over internal documents but cannot send data to hosted SaaS LLM platforms.

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Internal knowledge assistants spanning Confluence, Jira, Salesforce, Notion, and shared drives, where the 260+ connectors remove the need for custom ingestion code.

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Multi-agent automation projects where role-based agents need to call internal APIs and collaborate on multi-step tasks rather than just answering questions.

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Teams standardizing on open-source LLMs (Llama, Mistral) who want a complete on-premise stack — ingestion, vector retrieval, and agent orchestration — under their own control.

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Organizations that want department-scoped AI assistants with permissioning so finance, legal, and engineering teams each see only the data they are authorized for.

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Platform engineering teams building a shared internal AI platform that other product teams can deploy agents on top of, without each team rebuilding RAG infrastructure.

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Agent Cloud doesn't handle well:

⚠Requires minimum 16 GB RAM for Docker-based deployment, making it unsuitable for lightweight hardware or standard consumer laptops with 8 GB RAM
⚠No native mobile application or mobile-optimized interface — platform management and monitoring requires desktop browser access
⚠AGPL 3.0 copyleft license requires organizations to share source code of any modifications if distributing the software, potentially conflicting with proprietary development workflows
⚠Self-hosted architecture means organizations must manage their own infrastructure including security patches, backups, database maintenance, and scaling — no managed option for the free tier
⚠Limited built-in observability and monitoring tools — production deployments benefit from adding external logging and alerting systems like Grafana or Datadog
⚠No built-in fine-tuning capabilities for LLMs — organizations needing custom model training must use external tools and import the resulting models
⚠Windows deployment requires WSL2 (Windows Subsystem for Linux) as native Windows installation is not directly supported

Pros & Cons

✓ Pros

✓Fully open-source under AGPL 3.0 with a self-hosted community edition that includes the entire platform — no feature gating between free and paid tiers for core RAG and agent capabilities.
✓260+ pre-built data connectors out of the box, covering relational databases, document stores, SaaS apps, and file formats, eliminating the need to write custom ETL for most enterprise sources.
✓LLM-agnostic architecture supports OpenAI, Anthropic, and locally hosted open-source models (Llama, Mistral), so sensitive workloads can stay entirely on-premise.
✓Built-in multi-agent orchestration with CrewAI-style role-based agents that can call third-party APIs and collaborate on multi-step tasks, rather than just single-turn chat.
✓Strong data sovereignty story with VPC deployment, SSO/SAML, and audit logging in the Enterprise tier — well-suited to regulated industries that cannot use hosted RAG services.
✓Permissioning model lets admins scope specific agents to specific user groups, preventing accidental cross-team data exposure inside a single deployment.

✗ Cons

✗Self-hosting assumes Kubernetes and DevOps expertise — not a fit for teams that want a one-click hosted chatbot with minimal infrastructure work.
✗AGPL 3.0 licensing is more restrictive than MIT/Apache and can complicate embedding Agent Cloud into proprietary commercial products without a commercial license.
✗Smaller ecosystem and community compared to Langflow, Flowise, or Dify, which means fewer third-party tutorials, templates, and Stack Overflow answers.
✗Managed Cloud and Enterprise pricing is sales-gated rather than published, making upfront cost comparison difficult for procurement teams — expect to budget $500–$2,000+/month for Managed Cloud and $25,000–$100,000+/year for Enterprise based on comparable platforms.
✗The platform is broad in scope (ingestion + vector + agents + UI), so debugging issues that span multiple layers can require deeper system understanding than narrower tools.

Frequently Asked Questions

What are the minimum hardware requirements to run Agent Cloud?+

Agent Cloud requires a machine with at least 16 GB of RAM for Docker-based deployment. A base MacBook Air M1/M2 with 8 GB RAM is insufficient as the Airbyte integration requires significant resources. If running local LLMs via Ollama or LM Studio alongside Agent Cloud, additional RAM is recommended. Non-Docker deployments may work with 8 GB RAM but are harder to configure.

Can Agent Cloud run completely offline without internet access?+

Yes. By using local LLM providers like Ollama or LM Studio and connecting only to on-premises data sources, Agent Cloud can operate in a fully air-gapped environment with zero external API calls. This makes it suitable for classified or highly regulated environments where internet connectivity is restricted.

What is the AGPL 3.0 license and how does it affect usage?+

AGPL 3.0 is a copyleft open-source license that allows free use, modification, and deployment. However, if you modify the source code and distribute the software or provide it as a network service to others, you must make your modifications available under the same license. Internal use within your organization does not trigger this requirement.

How does Agent Cloud compare to using OpenAI's custom GPTs?+

Agent Cloud provides complete data sovereignty (your data never leaves your servers), supports 260+ data source integrations vs GPTs' limited file upload approach, enables multi-agent orchestration for complex workflows, and has no per-token usage fees beyond your own infrastructure costs. The trade-off is that Agent Cloud requires self-hosting and technical setup, while custom GPTs are instantly available but route all data through OpenAI's servers.

Which vector databases does Agent Cloud support?+

Agent Cloud natively supports Qdrant (included in the Docker deployment) and Pinecone. The platform's Rust-based vector proxy provides high-performance communication with these databases for fast similarity search across large document collections.

Can non-technical users operate Agent Cloud after initial setup?+

Yes. While initial deployment requires Docker and DevOps knowledge, the day-to-day operation of Agent Cloud uses an intuitive web-based GUI. Non-technical team members can create agents, connect data sources, manage conversations, and configure workflows through the visual interface without touching the command line.

How much does Agent Cloud actually cost, including infrastructure?+

The community edition is free to download and run. Your real costs are infrastructure and LLM API fees. A typical small-team deployment on AWS (m5.xlarge instance, EBS storage, and OpenAI API usage for ~50 users) runs roughly $200–$500/month all-in. Managed Cloud pricing is usage-based and starts in the $500–$2,000/month range depending on cluster size and connector volume — contact RNA Digital's sales team for an exact quote. Enterprise contracts are annual and typically range from $25,000 to $100,000+ per year based on deployment model, seat count, and support tier. For budget planning, the self-hosted path is significantly cheaper than comparable managed platforms like Dify Cloud or Botpress Enterprise, but requires DevOps investment.

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What's New in 2026

Through early 2026, Agent Cloud has continued expanding its connector catalog past 260 sources, deepened its multi-agent runtime around CrewAI-style role-based agents, and improved compatibility with locally hosted open-source models for organizations standardizing on Llama and Mistral. The platform's positioning has sharpened around data sovereignty as more enterprises seek alternatives to hosted AI services amid tightening global data privacy regulations.

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