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There is no single best self-hosted AI agent framework for every VPS. Choose LangGraph when you need explicit, stateful orchestration; Dify when you want a visual application platform with RAG and model management; CrewAI for Python-based role-playing agent teams; OpenHands for software-engineering agents; and n8n when the job is connecting services, webhooks, databases and AI steps.
Your VPS is only the operating environment. The right size depends on whether models run elsewhere, how much state and uploaded data you retain, how many jobs run at once, and how safely tools are isolated. The guide below matches each framework to a workload, then shows how to deploy and operate one without turning a small server into an unreliable production system.
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Quick answer: which framework fits your VPS project?
| Framework | Best fit | Authoring style | Important VPS considerations |
|---|---|---|---|
| LangGraph | Stateful, controllable agent orchestration | Code-first graphs | Self-hosted stack uses Docker plus Redis and Postgres; Lite edition allows up to 1 million executed nodes; Enterprise requires a license. |
| Dify | Visual agent applications, RAG and model operations | Visual workflows and product UI | Official minimum is 2 CPU cores and 4 GiB RAM for trying the stack, not a production-capacity guarantee; Docker Compose and persistent data are required. |
| CrewAI | Python teams building role-based multi-agent workflows | Python framework with high- and low-level APIs | No single VPS size is established in the project material; size it from Python workers, model location, tools and concurrency. |
| OpenHands | Agents whose main task is software engineering | Agent workspace with tool execution | Docker Sandbox can mount a projects directory; no-sandbox mode grants full filesystem access and demands stronger isolation. |
| n8n | Integration-heavy automation | Visual workflow editor | Community edition is free only when you or people in your organization create and edit workflows; external workflow building, white-labeling or hosting n8n as a service requires a commercial license. |
No controlled, cross-framework benchmark establishes a universal speed, cost or throughput winner. Treat performance claims as workload-specific and measure your own deployment.
What to evaluate before renting a VPS
Where the model runs
If inference is provided by an external model service, the VPS mainly handles orchestration, API calls, queues, databases, uploads and logs. If you run a model locally, memory, storage bandwidth and possibly a GPU become the dominant constraints. A framework that appears light with remote inference can become a very different workload when it also hosts model files and performs generation.
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State, databases and queues
Durable agent work requires more than an application container. LangGraph’s self-hosted deployment explicitly depends on Postgres and Redis. Other stacks still need persistent storage for workflow definitions, credentials, conversation history, vector indexes, uploaded files or execution logs. Keep these data services and volumes separate from disposable containers so an image replacement does not erase state.
Concurrency rather than headline CPU count
One interactive agent and twenty simultaneous jobs have different requirements. Estimate peak active runs, average tool calls per run, maximum output size and how long external services take to respond. Queue work instead of allowing every request to start a worker immediately. Measure memory use, queue depth and latency under that expected load before adding workers or accepting more users.
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A dashboard or webhook endpoint on the open internet needs HTTPS, authentication, firewall rules and rate limits. Store model-provider keys in environment or secret storage rather than source files or workflow text. Plan backups for Postgres, Redis state where applicable, uploaded files, workflow definitions and the environment configuration needed to recreate the service.
LangGraph: maximum orchestration control
Why choose it
LangGraph models an agent as an explicit graph of steps and transitions. That makes state, branching, retries and human approval points visible in code instead of hidden inside an opaque loop. It is the strongest choice when you need deterministic control over a long-running process, durable execution and a team comfortable maintaining Python or TypeScript services.
Self-hosted limits and editions
The self-hosted Lite version is documented as a limited LangGraph Platform edition that can run locally or on your own infrastructure with up to 1 million nodes executed. That is an execution allowance, not a statement that a small VPS can process a million complex tasks quickly. The self-hosted Enterprise path requires a license. Confirm which edition and terms apply before exposing a paid or multi-tenant service.
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Deployment workflow
- Build or select the LangGraph application and define its state schema, graph nodes and failure paths.
- Use
langgraph devfor lightweight local development and graph iteration. - For a containerized deployment, use the Docker-based
langgraph uppath described in the deployment documentation. - Provision Postgres and Redis as persistent services, keeping their data outside ephemeral containers.
- Place the API and any studio or dashboard behind a reverse proxy with HTTPS and authentication.
- Run a recovery test: restart containers, confirm queued work resumes as intended, and restore a database backup on a separate instance.
VPS profile
LangGraph is a good fit for a developer-managed VPS where you can operate multiple services and monitor them. Its documentation does not establish a universal CPU or RAM minimum. Allocate resources from graph complexity, the number of concurrent runs, database growth and whether models are remote. A tiny single-container VPS is a poor fit for the documented Redis-and-Postgres architecture unless those services are hosted elsewhere.
Dify: a visual agent application platform
Why choose it
Dify combines a visual interface for agent workflows with retrieval-augmented generation, model management and observability features. It suits a team that wants to assemble and operate applications through a product UI rather than build every orchestration detail in code. It is also useful when non-developers need to inspect or adjust a workflow under controlled permissions.
Documented resource floor
Dify’s current project README lists a minimum of 2 CPU cores and 4 GiB of RAM. That is a documented floor for installing or trying the stack, not a production capacity promise. Concurrent users, indexing volume, file uploads, background jobs and local model inference can require substantially more memory and storage.
Docker Compose deployment
- Choose a VPS with persistent disk sized for the application database, uploaded documents, vector data and logs.
- Obtain the versioned Compose configuration from the official Dify project and pin the release rather than using a floating image tag.
- Set secrets and model-provider credentials through the deployment’s environment or secret mechanism.
- Start the server with the project’s documented Docker Compose procedure.
- Configure a reverse proxy for HTTPS, then restrict administrative access and webhook endpoints.
- Import a small test knowledge base and run representative workflows before adding users or large document collections.
What the minimum does not cover
The 2-core, 4-GiB figure does not guarantee fast generation, indexing throughput or stable operation under simultaneous runs. If the VPS also hosts a local model, reserve memory for that model separately. Monitor disk growth from uploaded files, embeddings and execution history; a full volume can break an otherwise healthy container.
CrewAI: Python teams and role-based agent crews
Why choose it
CrewAI is an open-source Python framework with high-level abstractions and low-level APIs for production-ready multi-agent workflows. Its central concepts—agents with roles, tasks, crews and flows—map naturally to a team of specialized workers. Choose it when your developers want Python tests, package management and direct control over tool functions instead of a primarily visual editor.
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How to size it
The project does not publish one VPS CPU or RAM floor that applies to every crew. A useful estimate starts with the number of Python processes, the largest prompt and tool payload, expected parallel tasks and where inference runs. Remote models make the VPS mostly an orchestration host; local models, browser automation, document parsing or large in-memory data sets increase requirements.
Operational pattern
- Package the crew and its dependency lockfile in a versioned container image.
- Keep agent prompts, tool credentials and flow configuration outside the image so they can be rotated without rebuilding code.
- Use a job queue or scheduler for long tasks rather than holding an HTTP request open indefinitely.
- Persist task results and any files that later agents need; do not rely on a container’s writable layer.
- Add timeouts, retry limits and human approval for tools that can send messages, change records or spend money.
CrewAI gives you flexibility, but that flexibility transfers responsibility for persistence, observability and isolation to your application design. Build structured logs that identify the crew, task, tool call and failure reason.
OpenHands: choose it for software-engineering work
Why it is different
OpenHands is aimed at agents that inspect repositories, edit files, run commands and complete software tasks. Its value comes from a ready-made engineering workspace rather than from generic business-process orchestration. It is the natural shortlist choice for code generation, issue resolution and repository maintenance.
Docker Sandbox versus no-sandbox mode
The documented Docker Sandbox mounts a projects directory for the agent’s work. This gives you a boundary between the agent environment and the rest of the VPS while still allowing access to selected code. Treat the mounted directory as untrusted: remove secrets, limit permissions and use a dedicated service account.
No-sandbox mode gives the agent full filesystem access. That may simplify setup, but a prompt injection, malicious repository script or faulty command can affect every readable file and service on the host. Use stronger isolation decisions—such as a separate VM, disposable machine or tightly restricted account—before selecting this mode for anything beyond a controlled experiment.
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Deployment checklist
- Create a dedicated host or VM for agent execution when possible.
- Place only the intended repositories in the mounted projects directory.
- Keep cloud credentials, SSH keys and production configuration outside the workspace.
- Set CPU, memory, process and network limits appropriate to the jobs.
- Review command logs and generated diffs before allowing changes into a production branch.
- Destroy and recreate the workspace after handling untrusted code if you cannot prove it is clean.
OpenHands can be resource-intensive even when the model is remote because builds, tests, package managers and repositories consume CPU, RAM and disk. Size the VPS for the heaviest build or test operation, not merely for the chat interface.
n8n: integration-first automation with AI steps
Why choose it
n8n is strongest when your workflow crosses SaaS APIs, webhooks, databases and scheduled jobs, with AI used as one step among many. Its visual editor lets you connect triggers, transformations, approvals and model calls without implementing an orchestration service from scratch.
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Community-edition licensing boundary
The official licensing FAQ says the free self-hosted Community edition is available when only you or people in your organization create or edit workflows. External workflow building, white-labeling, or hosting n8n as a service requires a commercial license under its Sustainable Use License. Check the current terms before allowing customers or unrelated organizations to design workflows through your installation.
VPS deployment pattern
- Choose persistent storage for workflow definitions, execution data and binary files.
- Run the official self-hosted package with a pinned release and a database configuration appropriate to your workload.
- Expose only the webhook and UI routes that are required, through HTTPS and authentication.
- Set execution-retention limits so failed runs and large binary payloads do not silently fill the disk.
- Back up workflow definitions and credentials using the project’s supported method, then test importing them on a clean instance.
n8n can be lighter than a full agent platform for simple integrations, but execution history and binary data can grow quickly. Measure disk use and queue depth before increasing parallel execution.
Comparison by the decisions that matter on a VPS
Orchestration and authoring
| Decision | LangGraph | Dify | CrewAI | OpenHands | n8n |
|---|---|---|---|---|---|
| Control model | Explicit stateful graph and durable execution | Visual application and agent workflows | Roles, tasks, crews and flows in Python | Engineering agent operating a workspace | Event-driven integration workflow |
| Best author | Developers who need precise branching and state | Product teams and operators using a visual UI | Python developers building multi-agent teams | Developers delegating repository tasks | Automation builders connecting services |
| Observability emphasis | Durable run state and application-level instrumentation | Built-in product-oriented observability | You design logging and run inspection | Command, file and test activity must be reviewed | Workflow execution history and node-level inspection |
Persistence, tools and isolation
| Framework | Persistence you should plan | Tool and integration profile | Isolation concern |
|---|---|---|---|
| LangGraph | Postgres and Redis are explicit dependencies; graph state and application data need backups | Highly programmable tools and branches | Isolation is your container and application design |
| Dify | Database, uploads, retrieval data and workflow configuration | Visual RAG, model and workflow components | Protect uploaded data and administrative UI |
| CrewAI | Results, files, queues and any external databases your app uses | Python functions and services | Tool permissions and worker accounts are your responsibility |
| OpenHands | Repositories, generated patches, logs and workspace artifacts | Shell, coding and development tools | Use Docker Sandbox when possible; no-sandbox grants full filesystem access |
| n8n | Workflow definitions, execution history, credentials and binary data | Broad webhook, SaaS, database and API connectivity | Restrict credential access and externally editable workflows |
A repeatable VPS deployment plan
- Define the workload. Write down model location, tools, data retention, peak concurrent runs and whether outside users can trigger or edit workflows.
- Pin the software. Use an official Docker or CLI installation path and a specific release. Floating image tags make rollback and incident diagnosis harder.
- Separate state from containers. Put databases, queues and uploads on persistent volumes or managed services. For LangGraph, account explicitly for both Postgres and Redis.
- Secure the edge. Terminate TLS at a reverse proxy, require authentication, apply firewall rules and rate-limit public APIs and webhooks.
- Protect secrets. Keep model keys, database passwords and signing secrets in environment or secret storage. Do not commit them to repositories or workflow exports.
- Instrument before scaling. Record memory, CPU, disk growth, queue depth, request latency, failed tool calls and restart recovery.
- Back up and restore. Include Postgres, Redis state where used, uploaded files, workflow definitions and environment configuration. Perform a restore drill on a separate instance.
- Test failure paths. Stop a worker during a long run, revoke a model key, fill a staging disk and restart the host. Confirm jobs fail safely or resume according to your design.
- Review licensing. LangGraph Enterprise requires a license, and n8n’s Community edition has explicit limits on who may create or edit workflows and how the software may be offered.
Common VPS failure modes and fixes
The server runs out of memory
Check whether local inference, document indexing, browser processes or too many concurrent workers caused the spike. Reduce parallelism, move model inference off the VPS, increase RAM or split the database and workers onto separate hosts. Do not treat swap as a substitute for adequate memory during sustained generation.
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Data disappears after an upgrade
This usually means state was stored inside an ephemeral container or an unmounted path. Stop the deployment, verify volume mappings, restore the latest backup and pin the image version before trying again.
Webhooks time out
Long agent runs should be queued and acknowledged asynchronously instead of held open behind a proxy timeout. Return a job identifier, process work in a worker and expose a status endpoint or callback.
An agent performs an unsafe tool call
Reduce tool permissions, require approval for irreversible actions and run untrusted code in a sandbox or separate host. OpenHands no-sandbox mode deserves particular caution because it can access the full filesystem.
Disk usage grows unexpectedly
Inspect execution history, uploaded files, vector indexes, container logs and build artifacts. Set retention policies, rotate logs, move large artifacts to dedicated storage and alert before the volume reaches capacity.
Decision framework
- Pick LangGraph if correctness depends on explicit state transitions, resumable execution and code-level orchestration.
- Pick Dify if you need a visual product surface that combines agents, RAG, model management and observability, and your VPS can meet more than its 2-core, 4-GiB trial floor as usage grows.
- Pick CrewAI if your team already works in Python and wants role-based agents, tasks, crews and flows that can be tested and extended in code.
- Pick OpenHands if the primary output is a code change, test run or repository task, and you are prepared to isolate the execution workspace.
- Pick n8n if the difficult part is connecting systems and triggering reliable business automations, with AI as one component in a larger workflow.
Start with the smallest VPS that comfortably supports your chosen framework’s documented dependencies and your expected concurrency, then scale from measurements. A framework decision is also an operations decision: persistence, backups, security boundaries and licensing determine whether a self-hosted agent remains dependable after the demo.
Quick Recap
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