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Agent Cloud
Enabling companies to build and deploy secure Chat apps to talk to their data.
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Enabling companies to build and deploy secure Chat apps to talk to their data.
Most popular alternative: Chatbit (1,053 saves)
Author
Andrew Nada
🛠️ 1 tool
Agent Cloud is an open-source platform designed to enable companies to build, deploy, and engage with private LLM (Large Language Model) chat applications.
These applications allow teams to engage in secure conversations with their data, enhancing data accessibility and insights. The platform is compatible with both open-source and Cloud-hosted LLM, which makes it model agnostic.
This means users can connect their own open-source model or leverage tools like Open AI. To ensure enhanced privacy, users can connect Agent Cloud to their locally hosted models.
With built-in support for data from over 300 sources, the platform minimizes integration hassles enabling the easy chunking, splitting, and embedding of data.
Conversations with data are made possible by syncing, storing, and then constructing chat sessions using your choice of LLM. The platform's data pipeline is automated and can be set to manual, scheduled, or a cron expression, ensuring fresh and updated source data.
In terms of infrastructure, Agent Cloud has a modular open-source architecture designed to scale seamlessly with your organization. This includes a built-in ELT pipeline powered by Airbyte, a message bus powered by RabbitMQ, and a Vector Database powered by Qdrant.
Users thereby have access to a scalable, secure, and versatile platform for creating sophisticated AI applications while ensuring that their private data remains private.
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Releases
Get notified when a new version of Agent Cloud is released
Notify me
October 24, 2023
Andrew Nada
Initial release of Agent Cloud.
Pricing
Pricing model
Freemium
Paid options from
$99/month
Billing frequency
Monthly
Prompts & Results
Add your own prompts and outputs to help others understand how to use this AI.
Agent Cloud was manually vetted by our editorial team and was first featured on July 18th 2024.
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12,71099v2.1 released 2mo agoFree + from $45/mo
This is the first AI marketing tool I’ve used that actually helps me get things done, not just suggest generic ideas. The mix of AI-powered strategy and real experts executing the work makes it feel like having a high-performance marketing team without the overhead -
29,032396v1.0 released 11mo agoFree + from $35/mo
This team took the time to understand the industry, problem and its users and designed a perfectly engineered solution. Kudos.
Pros and Cons
Pros
Open-source platform
LLM chat applications
Allows secure data conversations
Compatible with open-source, cloud-hosted LLM
Model agnostic
Supports local models hosting
Data from 300+ sources
Minimizes integration hassles
Offers data chunking, splitting, embedding
Automated data pipeline
Manual, scheduled, cron expression updates
Modular open-source architecture
Scales with organization
ELT pipeline powered by Airbyte
Message bus powered by RabbitMQ
Vector Database powered by Qdrant
Trusted by renowned organizations
Use open-source or cloud-hosted LLM
Retrieve data from 300+ sources
Built-in data pipeline
Bring your own LLM
End to end RAG pipeline
Select your own data connectors
Customizable data preparation
Automated data storage in vector DB
Sync data at user-defined frequency
Chat with your synced data
Designed to scale from startup to enterprise
Built-in ELT pipeline
Built-in message bus
Built-in Vector Database
Private data chat in your cloud
Support for local Large Language Models
Support for cloud models
Local embedding models support
Advanced chunking methods
Custom fields selection for data sync
Control over sync frequency
Supports various file upload formats
View 34 more pros
Cons
Requires significant RAM
Limited OS compatibility
Requires manual data splitting
Chunking strategies not customizable
Table and field selection limited
Sync frequency requires manual configuration
Limited file upload formats
No Windows native support
Needs large data infrastructure
Data access through specific sources
View 5 more cons
7 alternatives to Agent Cloud for Chatbots
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Chatbots that are private, fully customizable, and model agnostic21,681728v0.9.5 released 10mo agoFree + from $200/moGreat product to offload the data analytics workflow. -
Build AI Agents with No-Code14,698135v2.3 released 6mo agoFree + from $150GPT Trainer is run by a highly responsive management team and support staff. Questions are answered quickly, feature requests are incorporated quickly. We have multiple chatbots that are live with GPT Traner, including for Healthcare, hospitality and solar energy providers. We highly recommend for both value and quality. -
Create custom AI assistants on your GPUs13,15063Released 2y agoFrom $3 -
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Q&A
What is Agent Cloud?
Agent Cloud is an open-source platform that equips businesses with the tools to construct, initiate, and engage with private Large Language Model (LLM) chat applications. This enables secure discussions with data, enhancing data accessibility and insights.
What are the main features of Agent Cloud?
Key features of Agent Cloud include the ability to build and deploy LLM chat applications, data accessibility from over 300 sources, an automated data pipeline, and a modular open-source architecture. Built-in ELT pipeline powered by Airbyte, a message bus powered by RabbitMQ, and a Vector Database powered by Qdrant are also notable features. Agent Cloud provides options for data to be set to manual, scheduled, or a cron expression to ensure fresh and updated source data. Moreover, users can connect Agent Cloud to locally hosted models.
How can Agent Cloud be used to build private LLM Chat apps?
Building private LLM Chat apps with Agent Cloud involves connecting to either an open-source model or utilizing an external tool such as Open AI. The platform accommodates conversations with data by syncing, storing, and subsequently creating chat sessions using the selected LLM. Users can leverage their own models hosted locally for increased privacy or opt for cloud-hosted models. The platform has been engineered to work seamlessly with either option, describing it as being 'model agnostic'.
How does it ensure data remains private in chats?
To ensure that data stays private in chats, users can connect Agent Cloud to their locally hosted models. This means the data is stored and managed within the infrastructure of the user, thus enhancing privacy. Agent Cloud thus enables the creation of sophisticated AI applications that offer privacy for their data.
What does it mean when Agent Cloud is described as model agnostic?
'Model agnostic' means that the platform is flexible and can work with any model, whether open-source or cloud-hosted. Users can connect Agent Cloud to their own open-source model, or even utilize other tools like Open AI, providing flexibility and versatility.
How does the platform handle data from various sources?
Agent Cloud has built-in support that allows users to minimizes integration hassles and manage data from over 300 sources. It enables the easy chunking, splitting, and embedding of data, providing an efficient way to handle diverse data.
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