Deploy and Host Aegra on Railway

Aegra is a self-hosted backend for building and running LangGraph-compatible AI agents. It provides a production-oriented runtime for agent applications with persistent PostgreSQL storage, configurable graphs, and flexible model provider support.

About Hosting Aegra

This Railway template deploys Aegra together with PostgreSQL as its persistent data layer.

Aegra acts as the runtime backend for AI agents and LangGraph-based applications, while PostgreSQL stores persistent application and agent state.

The deployment is designed for developers who want to self-host LangGraph-compatible workloads without manually provisioning the underlying infrastructure.

Common Use Cases

Self-hosted AI agent backends
LangGraph-compatible agent deployments
Stateful AI applications
Agent orchestration
Backend services for AI assistants
Custom LLM applications
Internal AI platforms
Research and experimentation with agent systems
AI-powered APIs
Developer platforms for agent-based applications

Dependencies for Aegra Hosting

This template uses:

Aegra for the agent runtime and application backend
PostgreSQL for persistent application and agent state
Railway Private Networking for secure internal communication
Railway managed infrastructure for deployment and persistence

Architecture

        Users / Applications
                │
                ▼
              Aegra
                │
                │
                ▼
           PostgreSQL

Aegra handles the agent runtime and application layer, while PostgreSQL provides the persistent data backend.

How Aegra Works

Aegra provides infrastructure for serving LangGraph-compatible agents as self-hosted applications.

Instead of acting as a visual workflow builder, Aegra runs agent graphs defined in application code and configuration.

This makes it suitable for developers who want greater control over:

Agent logic
LangGraph applications
Application dependencies
Model providers
Authentication
Persistent state
Deployment infrastructure

Persistent Architecture

The Aegra application itself is designed to remain lightweight, while persistent state is stored in PostgreSQL.

This separation makes the application easier to redeploy and maintain while keeping important runtime data persistent.

Component	Role
Aegra	Agent runtime and application backend
PostgreSQL	Persistent application and agent state

LangGraph Compatibility

Aegra is designed around LangGraph-compatible applications.

Developers can define graphs in Python and deploy them using Aegra as the serving and runtime layer.

This enables use cases such as:

ReAct agents
Stateful assistants
Multi-step AI workflows
Tool-using agents
Long-running agent applications
Custom LangGraph architectures

LLM Provider Flexibility

Aegra does not force a single LLM provider.

The agents deployed through Aegra can use different model providers depending on the application and graph configuration.

This makes it possible to build applications using providers such as:

OpenAI
Anthropic
Together AI
OpenRouter
Self-hosted model endpoints
Other LangChain-compatible providers

Aegra vs Managed Agent Platforms

Feature	Aegra	Managed Agent Platform
Self-hosted	✅	❌ / Limited
LangGraph compatibility	✅	✅
Infrastructure control	✅	❌
PostgreSQL persistence	✅	✅
Custom agent code	✅	Varies
Model provider flexibility	✅	Varies
Deployment portability	✅	Limited
Railway hosting	✅	❌

Why Self-Host Aegra?

Self-hosting Aegra gives developers control over the infrastructure running their agent applications.

This can be useful when you need:

Control over application deployment
Private infrastructure
Custom networking
Independent model provider selection
Persistent agent state
Integration with internal systems
Reduced dependency on managed agent platforms

Why Deploy Aegra on Railway?

Railway provides a simple platform for running Aegra together with PostgreSQL and the infrastructure required for persistent deployments.

Railway handles much of the underlying deployment work, including:

Service orchestration
Private networking
Persistent storage
HTTPS
Environment management
Application redeployment
Infrastructure scaling

This allows developers to focus primarily on building and deploying their agents instead of managing servers.

Who Is This Template For?

This template is suitable for:

LangGraph developers
AI application developers
Backend engineers
AI platform teams
Researchers building agent systems
Developers migrating away from managed agent deployment platforms
Teams building internal AI services