Persistent document storage
Uploaded files and their embeddings live on plan storage, not in ephemeral container disk.
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Install AnythingLLM
Private agents and RAGAnythingLLM
Install AnythingLLM
on managed AI hosting.
Chat with private documents, build no-code agents, and choose the model and vector database that fit the work. Darwa adds the managed runtime, HTTPS, persistent storage, and lifecycle tracking around it.
One-click deploymentAlways-on runtimePersistent storage
Why AnythingLLM
Workspaces that keep document sets apart
AnythingLLM is organised around the workspace: each one has its own documents, its own embeddings, and its own permissions, so a legal collection never leaks into a support answer.
- Interface
- Document and agent workspace
- Deployment
- Managed container
- Source
- Open source on GitHub
AnythingLLM features prepared for a managed deployment.
The visible capabilities come from the verified catalog entry; secrets and signed installer assets remain private to the backend.
Generated authentication secret
Session and login signing material is unique to your instance.
HTTPS routing
A working address with a valid certificate from the first health check, no manual TLS setup.
Health verification
The deployment is only marked live once the application answers a real health probe.
No-code agents
Configure approved models, tools, and instructions visually rather than in code.
Multiple model providers
Point the deployment at hosted or local models and switch without changing the interface.
What AnythingLLM talks to.
A selection of what the upstream project supports. Credentials for any third-party service are yours to add and revoke.
- OpenAI
- Anthropic
- Ollama
- LM Studio
- LocalAI
- Together
- LanceDB
- Chroma
- Pinecone
- Qdrant
- Weaviate
- DOCX
- Confluence
- GitHub
- YouTube
How a question becomes a sourced answer.
Documents are chunked and embedded once, then every question searches that index before the model writes anything.
Useful starting points for AnythingLLM.
Document Q&A
Create focused workspaces over private collections and retrieve relevant context for answers.
No-code agents
Give teams a visual way to configure approved models, tools, and instructions.
Knowledge workspaces
Separate projects and document collections while retaining one managed deployment boundary.
What Darwa runs so you do not have to.
The hosting layer is the same for every agent, which is what makes a one-click install honest.
Verified manifest
Deployments run from a reviewed catalog entry rather than an arbitrary image you have to trust.
Isolated runtime
Each agent gets its own workload boundary, not a shared container with an unrelated website.
Persistent storage
Plan storage is allocated up front so application state survives restarts and releases.
Managed HTTPS
A working address with a certificate from the first health check, with custom domains where supported.
Health tracking
Runtime and resource signals come from the actual workload, so a failure is visible rather than silent.
Subscription lifecycle
Grace, suspension, retention, and restoration follow the plan instead of an untracked server.
Plans that can run AnythingLLM.
One AI Agent subscription covers the managed runtime, storage, and endpoint. Model provider usage is billed by that provider.
Live pricing is temporarily unavailable. AnythingLLM needs at least 1 vCPU, 2 GB RAM, and 10 GB of storage.
View AI agent plansFrom catalog page to a running AnythingLLM service.
- 1
Choose Install
Darwa keeps AnythingLLM selected when the authenticated launch flow opens.
- 2
Confirm a plan
Use an active agent subscription or choose a plan with enough compute and storage.
- 3
Choose an address
Start with an agents.darwa.co address or select an eligible verified custom domain.
- 4
Launch and track
The backend verifies the manifest, creates the runtime, and records its service and container identities.
AnythingLLM questions.
Darwa hosts the open-source application independently; upstream licensing and product behavior remain with its project.
What file types can I upload?+
AnythingLLM handles common document formats including PDF, Word, plain text, and Markdown, plus connectors for sources such as web pages and repositories. Very large collections need proportionally more storage.
Do workspaces share documents?+
No. Separating collections is the point of the workspace model. A document indexed in one workspace is not retrieved when answering in another.
Can I choose the vector database?+
Yes. It ships with an embedded vector store and can be pointed at an external provider instead if you already run one.
Are answers linked to sources?+
Retrieved chunks are shown with the answer, so a reader can check which part of which document was used.
What does Darwa manage for AnythingLLM?+
Darwa manages the verified deployment manifest, isolated runtime, persistent storage allocation, HTTPS endpoint, health tracking, usage visibility, and paid-plan lifecycle. You manage the agent's models, credentials, tools, content, and decisions.
How do I install AnythingLLM?+
Choose Install AnythingLLM, sign in, and select an AI Agent plan if the workspace does not already have one. The launch flow keeps AnythingLLM selected while you choose its address and start the managed deployment.
What resources does AnythingLLM require?+
The current catalog minimum is 1 vCPU, 2 GB RAM, and 10 GB of storage. Real requirements depend on concurrency, models, document volume, integrations, and enabled features.
Does AnythingLLM data survive restarts?+
Darwa allocates persistent storage according to the selected plan. Important application data should still follow the backup and recovery practices required by your business.
Can AnythingLLM use a custom domain?+
The application can start with a managed Darwa address. A domain you own can be selected when custom-domain support is available for the launch flow and its DNS is verified.
Is Darwa affiliated with AnythingLLM?+
No affiliation is implied. AnythingLLM is a third-party open-source project. Darwa provides independent managed hosting and links to the upstream repository for project information and licensing.
Ready when you are
Sign-in and an active Darwa AI Agent plan are required before deployment.Deploy AnythingLLM in one click.
Sign in, pick a plan with enough resources, and Darwa builds the managed runtime around it.
