There is no universal winner. Choose LangGraph when you need explicit, durable orchestration; Dify when you want a visual AI application platform; CrewAI for Python-based multi-agent teams; OpenHands for software-engineering agents; and n8n for integration-heavy automation. Your VPS supplies the compute and persistence, but the right size depends on where models run, how much state you retain, and how many jobs run concurrently.
What a self-hosted agent framework does on a VPS
An AI agent framework combines a model with instructions, tools, memory or state, and a control loop that decides what to do next. Running it on your own virtual private server (VPS) puts the orchestration service, databases, workflow definitions, uploaded files, and access controls under your administration. The model itself may run on the same machine or through an external provider; those are separate architecture choices.
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The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →A VPS is an operating environment, not a performance guarantee. CPU, memory, storage speed, network latency, model location, tool duration, and concurrent runs determine the practical experience. None of the primary documentation reviewed provides a controlled, cross-framework speed or total-cost benchmark, so claims that one framework is universally faster or cheaper would be misleading.
Quick comparison
| Framework | Best fit | Authoring style | Orchestration and state | Integration breadth | Isolation considerations | Documented VPS facts |
|---|---|---|---|---|---|---|
| LangGraph | Developers needing explicit, stateful graphs and durable execution | Code-first Python/JavaScript APIs and graph definitions | Fine-grained graph control; self-hosted stack requires Redis and Postgres | Tools are defined in application code; breadth depends on what you build | Separate your application and worker permissions; protect databases and secrets | Self-Hosted Lite allows up to 1 million nodes executed; Enterprise self-hosting requires a license |
| Dify | Visual, product-oriented agents, RAG, model management, and observability | Web interface with Docker Compose deployment | Platform-managed workflows, knowledge bases, and run history | Model providers, retrieval, tools, and application endpoints managed in the platform | Secure the web console, API, uploads, and worker services as one application | Project README lists a minimum of 2 CPU cores and 4 GiB RAM for installation; this is not a production capacity promise |
| CrewAI | Python teams building role-based multi-agent crews and flows | Python code with high-level abstractions and lower-level APIs | Agents, tasks, crews, and flows are composed by your application | Python ecosystem and tools you integrate yourself | Apply process, container, and credential isolation around your code and tools | No single CPU or RAM floor is stated in the cited project material |
| OpenHands | Agents whose primary job is software engineering | Agent interface plus a project workspace and tool execution | Task-oriented coding sessions; workspace state must be persisted deliberately | Developer tools, shells, repositories, and coding environments | Docker Sandbox is the safer default; no-sandbox mode grants full filesystem access | Documentation supports cloud-server deployment; resource needs vary with projects and concurrent sessions |
| n8n | Webhooks, SaaS, databases, and AI steps in visual automations | Visual workflow editor | Workflow executions, credentials, queues, and schedules managed by the automation service | Broad connector ecosystem for APIs, databases, webhooks, and services | Limit editor and webhook exposure; isolate workers and credential storage | Community edition is free when only you or your organization create and edit workflows; other use cases can require a commercial license |
LangGraph: maximum control over stateful orchestration
LangGraph is the strongest choice when the workflow itself is the product: you need explicit nodes and edges, resumable state, human approval points, retries, and deterministic control over what an agent may do. Instead of treating an agent as an opaque loop, you model transitions and state so that a long-running process can be inspected and resumed.
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How self-hosting works
LangGraph’s self-hosted documentation describes a Docker-based stack with Redis and Postgres dependencies. Redis is used for queueing or transient coordination in the deployment, while Postgres holds durable application state. Keep both on persistent storage and include them in your backup plan; deleting an ephemeral container must not delete the graph’s state.
The CLI distinguishes langgraph dev, intended for lightweight local development, from the Docker-oriented langgraph up path used to run the stack. Treat development and production as different operating modes: development conveniences are not a substitute for TLS, authentication, backups, and controlled upgrades.
Edition and execution limit
The Self-Hosted Lite version is documented as a limited LangGraph Platform edition that can run locally or in a self-hosted manner, up to 1 million nodes executed. That is an execution allowance, not a promise that any VPS can process a million nodes quickly. LangGraph’s self-hosted Enterprise path requires a license, so check the terms before exposing a hosted service to customers or other organizations.
When to choose it
- Use it for approval-heavy business processes where every transition must be visible.
- Choose it when resumability, checkpoints, retries, and explicit state matter more than a drag-and-drop editor.
- Budget operational time for Postgres, Redis, schema changes, worker processes, and observability.
Dify: a visual AI application platform
Dify is aimed at teams that want to assemble agents, retrieval-augmented generation (RAG), model connections, tools, and application endpoints through a web interface. It is closer to a complete product platform than to a small orchestration library, which makes it attractive when non-programmers need to edit prompts and workflows.
Resource floor
Dify’s current project README lists minimum installation requirements of 2 CPU cores and 4 GiB of RAM. Those figures are a documented floor for trying the stack, not a production capacity target. RAG indexes, uploaded documents, background workers, model traffic, and simultaneous users can require substantially more memory, CPU, and disk than the minimum.
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Deployment and persistence
The official installation path starts the server with Docker Compose. Pin the Compose file and image versions you intend to run, then map persistent volumes for the database, uploads, and any vector or search data used by your applications. A reverse proxy should terminate HTTPS in front of the console and API; do not expose every internal container port to the public internet.
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When to choose it
- Choose Dify when a team needs a visual editor, model-provider management, RAG, and run observability in one interface.
- Prefer another framework when your core requirement is a highly customized state machine or a coding agent with shell-level control.
- Plan for separate staging and production projects so prompt and workflow edits can be tested before publication.
CrewAI: Python-native teams of agents
CrewAI is an open-source Python framework with high-level abstractions and lower-level APIs for production-ready multi-agent workflows. Its central concepts are agents with roles, tasks, crews that coordinate those tasks, and flows that organize larger application logic.
Why Python teams select it
A Python codebase can keep agent definitions, tool wrappers, tests, dependency locking, and deployment configuration in the same version-controlled project. That is useful when the “team” metaphor maps to real responsibilities such as research, extraction, verification, and reporting, and when developers want ordinary Python control over branching and error handling.
VPS design
The project material does not state a universal CPU or RAM minimum. Size the VPS from the model endpoint, tool workload, and concurrency you expect rather than from the framework name. A single worker calling a hosted model has a very different footprint from several workers running browser automation, document parsing, or local inference.
Run crews under a dedicated Unix user or container, restrict outbound network access where possible, and keep provider keys outside source control. If a tool can execute code or modify files, give it a narrowly scoped workspace instead of the entire server filesystem.
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When to choose it
- Use CrewAI when your team is comfortable reviewing Python and wants role-based multi-agent composition.
- Use a graph-oriented system instead when durable, visual state transitions are the main requirement.
- Use an integration editor instead when most of the work is connecting SaaS APIs rather than coordinating custom Python tools.
OpenHands: software-engineering agents
OpenHands is specialized for agents that inspect repositories, edit files, run commands, and work through software tasks. Its documentation supports running on a cloud server and provides a Docker Sandbox that mounts a projects directory into the agent environment.
Sandbox choices
The Docker Sandbox is the sensible default for a shared or internet-accessible VPS because it places the agent’s commands in a more constrained environment. The no-sandbox mode gives the agent full filesystem access. Use that mode only when you have made an explicit isolation decision, understand the blast radius, and trust every task and tool input.
Storage and access planning
Persist the projects directory separately from the application container so a restart does not erase work. Use separate workspaces for unrelated repositories, restrict SSH keys and cloud credentials, and avoid mounting the host’s home directory or Docker socket unless the task genuinely requires it. A coding agent can produce large build artifacts and logs, so monitor disk growth as well as RAM.
When to choose it
- Choose OpenHands when writing, testing, and debugging code is the agent’s primary job.
- Do not select it merely because it can call tools; general business workflows usually need a workflow or graph platform instead.
- For multiple users, add authentication, per-user workspaces, resource limits, and a queue so one repository cannot starve every other session.
n8n: integration-first automation with AI steps
n8n is the natural fit when the workflow begins with a webhook, schedule, database event, or SaaS connector and invokes AI as one step among many. Its visual editor lets operators connect APIs, transform data, add approvals, and call model providers without writing an entire orchestration service.
Community-edition licensing boundary
n8n’s 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 can require a commercial license under its Sustainable Use License. Confirm the current terms before giving customers an editor or selling access to a hosted automation service.
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Operational model
Protect the editor separately from incoming webhook endpoints. Store credentials in the platform’s encrypted credential system, keep its encryption key in secret storage, and back up workflow definitions and execution data according to their retention value. If executions become numerous, separate web, queue, and worker roles so a long-running job does not block webhook handling.
When to choose it
- Choose n8n when connector coverage and fast visual integration matter more than bespoke agent reasoning.
- Use a code-first framework when you need complex state transitions that are difficult to express as nodes.
- Review licensing before allowing people outside your organization to design or edit workflows.
How to size the VPS
CPU and memory
Start with the documented floor only where one exists: Dify lists 2 cores and 4 GiB RAM for installation. For every framework, reserve headroom for the operating system, reverse proxy, database, queue, workers, and monitoring. Hosted-model calls may leave CPU mostly idle while waiting on the network; local models, embeddings, browser automation, and document conversion can consume several cores continuously.
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Persistent storage
Use fast persistent disks for Postgres, Redis data where applicable, vector indexes, uploaded documents, repositories, workflow definitions, and logs. Estimate growth from actual retention policies, then leave room for database vacuuming, image layers, and an emergency backup. A small root disk that fills during a run can corrupt otherwise healthy services.
Network, TLS, and regions
Place public dashboards and APIs behind HTTPS, authentication, firewall rules, and rate limits. Select a region close to your users and model provider to reduce latency, while checking whether the provider offers the GPU or egress characteristics your workload needs. A reverse proxy should expose only the endpoints users require; internal databases and queues should remain on private interfaces.
Concurrency
Expected simultaneous runs are often more important than average daily volume. A single interactive coding session, ten webhook-triggered automations, and a queue of long RAG jobs produce different memory and latency profiles. Begin with one or a few workers, measure queue depth and peak memory, and increase workers only after restart and overload behavior is understood.
A safe deployment sequence
- Define the workload. Record model location, tools, average and maximum run duration, data sensitivity, expected concurrent users, and retention requirements.
- Follow the official install path. Use the framework’s Docker Compose or CLI procedure rather than assembling undocumented containers. For LangGraph, distinguish
langgraph devfrom the Docker-basedlanggraph upworkflow. - Pin releases. Pin container tags, package versions, and Compose files. Floating images can change behavior during an unrelated restart.
- Separate state from containers. Put Postgres, Redis, uploads, repositories, vector data, and workflow definitions on named persistent volumes or managed services. LangGraph explicitly requires Postgres and Redis in its self-hosted stack.
- Configure secrets. Supply model-provider keys and encryption keys through environment variables or a secret manager. Never commit them to a repository or paste them into workflow text.
- Harden the edge. Terminate TLS at a reverse proxy, require strong authentication, apply firewall rules and rate limits, and disable unused public ports.
- Apply least privilege. Run services as non-root users where supported, isolate coding workspaces, and restrict tool network and filesystem access.
- Instrument before scaling. Watch RAM, CPU, disk growth, database size, queue depth, request latency, failed runs, and restart recovery under representative jobs.
- Back up and restore-test. Back up Postgres, relevant Redis state, uploaded files, repositories, workflow definitions, and environment configuration. Perform a restoration on a separate instance before calling the system production-ready.
- Document upgrades. Read release notes, snapshot persistent data, test migrations in staging, and keep a rollback image and backup available.
Security and failure modes
Exposed administration panels
A public editor or agent console is an attack target. Put it behind HTTPS and authentication, restrict administrative access by network where possible, and avoid exposing database, Redis, Docker, or internal worker ports.
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Untrusted tool execution
Agents can be induced to follow malicious instructions in documents, repositories, web pages, or tool output. Treat retrieved text as untrusted input, require confirmation before destructive actions, and isolate shells, browsers, and file mounts. OpenHands’ no-sandbox mode is especially consequential because it grants full filesystem access.
Lost state after a restart
If runs, uploads, or credentials disappear after a redeploy, a container was probably treated as storage. Move state to persistent volumes or external services, verify mount paths, and test restoration rather than assuming a successful container start means data safety.
Memory exhaustion
Symptoms include workers being killed, slow database queries, and timeouts during concurrent runs. Reduce worker count, cap job concurrency, shorten retention, move models or embeddings off the VPS, and add RAM only after identifying which component consumes it.
Queue buildup and latency
A growing queue means arrivals exceed service capacity or a downstream model or tool is stalled. Inspect provider latency, tool timeouts, worker health, and database locks before adding workers. More workers can increase contention and provider throttling rather than improve throughput.
License surprises
Self-hosting does not automatically grant rights to offer a framework as a hosted product. LangGraph’s self-hosted Enterprise path requires a license, and n8n places conditions on external workflow creation, white-labeling, and service hosting. Recheck the current license for your exact distribution and customer model.
Decision guide
- Pick LangGraph if explicit graphs, durable state, checkpoints, and orchestration control are the deciding criteria.
- Pick Dify if a visual team-facing platform with RAG, model management, and observability is more valuable than code-level control.
- Pick CrewAI if Python developers need role-based agents, tasks, crews, and flows in a maintainable codebase.
- Pick OpenHands if the agent must work directly on software projects and execute development tools.
- Pick n8n if webhooks, SaaS APIs, databases, schedules, and AI actions form the majority of the workflow.
For a mixed architecture, keep boundaries clear: an integration workflow can call a specialized agent service, while a coding agent can remain isolated from business databases. Each added service brings another upgrade path, secret store, backup set, and license to manage, so compose only what the workload requires.
Quick Recap
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