Aegra vs Temporal AI

Side-by-side comparison of features, pricing, and ratings

Analysis generated Live tool data as of 2026-10-31
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Aegra or Temporal AI: which should you choose?

DimensionAegraTemporal AI
PricingFree (open-source Apache 2.0; you pay only for your own servers)Freemium; cloud usage-based billing with Billable Action Count
Primary Use CaseSelf-hosted backend for LangGraph agents with data sovereigntyDurable execution for reliable AI agents and multi-step workflows
Key FeatureDrop-in replacement for LangGraph Platform; same LangGraph SDK compatibilityAutomatic state capture, retries, recovery, and full visibility UI
Target UserLangGraph developers wanting self-hosting, cost control, and custom authTeams building fault-tolerant, long-running workflows at scale
HostingSelf-hosted on your own infrastructure via Docker ComposeCloud (Temporal Cloud) or self-hosted; serverless workers available
Open SourceApache 2.0 fully open-sourceOpen-source core; cloud offering has paid tiers

Choose Temporal AI if you need a battle-tested durable execution platform for complex, long-running workflows with automatic recovery, and you're okay with usage-based billing at scale. Choose Aegra if you're already using LangGraph and want to self-host without per-node fees, gaining full data control—and you're comfortable managing your own infrastructure. They serve different ecosystems: Temporal is general-purpose workflow orchestration; Aegra is a LangGraph-specific deployment backend.

Aegra
Aegra

Self-hosted Agent Protocol server that runs LangGraph agents on your own Postgres with the same LangGraph SDK.

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Temporal AI
Temporal AI

Durable execution platform that keeps AI agents and long-running workflows alive through crashes, retries, and abandoned sessions.

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Pricing
Free
Paid
Plans
CommunityFree
FreeFree trial
Pay-As-You-GoFrom $50/1M actions
Business Support Add-On$500/mo
Enterprise and Mission CriticalCustom
Popularity
13 views
7.5k views
Skill Level
Intermediate
Advanced
API Available
Platforms
API
WebAPI
Categories
🕸️ Agent Frameworks & Orchestration⚙️ Developer Infrastructure
🕸️ Agent Frameworks & Orchestration⚙️ Developer Infrastructure
FeaturesAegraTemporal AI
Self-hosted Agent Protocol server for LangGraph agents
Drop-in URL swap for existing LangGraph SDK clients
LangGraph SDK compatibility for Python and JS
Agent Protocol endpoints: threads, runs, state, assistants, store, crons
PostgreSQL persistence for threads, runs, and checkpoints as plain tables
Migrations applied automatically on server start
Custom auth in Python via decorators such as @auth.on.threads.create
Auth providers: JWT, OAuth, Firebase
Per-thread ownership scoping to the authenticated user
SSE streaming of token, value, and event output
Resume streaming runs after a client disconnect
Redis worker queue with leases, heartbeats, and crash recovery
OTLP tracing to Langfuse, Phoenix, or a custom collector
Every run a trace, every node a span, set as a server setting
AG-UI compatible chat frontend via CopilotKit
Durable execution captures Workflow state at every step with no checkpointing or recovery code
Native SDKs for Go, Java, Python, TypeScript, .NET, PHP, Ruby, and Rust
Activities retry automatically with backoff, four timeout classes, and heartbeating
Signals, Queries, and Updates read and mutate running Workflows mid-flight
Workflow Streams for real-time interactivity with running executions
Durable AI agents via OpenAI Agents SDK and Google ADK running LLM and tool calls as Activities
Standalone Activities as a durable job-queue pattern, GA across six SDKs (2026-09-15)
Serverless Workers for Google Cloud Run, public preview (2026-10-05)
Serverless Workers for AWS Bedrock AgentCore, pre-release (2026-09-18)
Native Task Queue priority and fair distribution without a custom queueing layer
Durable Timers that sleep for months; cron Schedules with backfill and Continue-As-New
Saga pattern via compensating transactions that read like try/catch
Worker Versioning pins Workflows to a version so deploys don't corrupt in-flight runs
Cloud UI Strict Session Mode: 15-minute inactivity timeout, 12-hour max session (GA)
Replay tests validate against real histories; time-skipping tests fast-forward long timers in CI
IntegrationsAegraTemporal AI
LangGraph SDK
PostgreSQL
Redis
Langfuse
Phoenix
LangGraph Studio
Agent Chat UI
CopilotKit
OpenAI Agents SDK
Google ADK
AWS Lambda
Google Cloud Run
Amazon Bedrock AgentCore
Kubernetes
GitHub Actions
GCP Marketplace
Azure
Slack

Who should pick Aegra, and who should pick Temporal AI?

  • LangGraph developer wanting self-hosting
    Pick: Aegra

    Aegra is purpose-built for deploying LangGraph agents on your own infrastructure, with drop-in replacement for LangGraph Platform, custom auth, and no per-node costs.

  • Enterprise building resilient multi-step AI workflows
    Pick: Temporal AI

    Temporal's durable execution ensures workflows survive crashes with automatic state capture and retries, and integrates with OpenAI Agents SDK and Google ADK for complex AI pipelines.

  • Startup with tight budget needing agent backend
    Pick: Aegra

    Aegra is fully free (open-source) with no usage fees; you only pay for your own servers. Ideal if you already use LangGraph and want to avoid vendor costs.

  • Team needing human-in-the-loop for financial transactions
    Pick: Temporal AI

    Temporal's Signal and Saga pattern support enables pausing workflows for manual approval and compensating rollbacks, critical for financial systems.

  • Solo developer learning AI agent orchestration
    Pick: Aegra

    Simple Docker Compose setup, same LangGraph SDK, and free unlimited usage make Aegra an easy starting point without worrying about costs or complex infrastructure.

Frequently Asked Questions

Aegra vs Temporal AI: which should you choose?

Choose Temporal AI if you need a battle-tested durable execution platform for complex, long-running workflows with automatic recovery, and you're okay with usage-based billing at scale. Choose Aegra if you're already using LangGraph and want to self-host without per-node fees, gaining full data control—and you're comfortable managing your own infrastructure. They serve different ecosystems: Temporal is general-purpose workflow orchestration; Aegra is a LangGraph-specific deployment backend.

Can I use Aegra without LangGraph?

No, Aegra is a backend specifically for deploying LangGraph agents. It requires the LangGraph SDK client and is not a standalone agent builder.

Does Temporal AI support human-in-the-loop?

Yes, Temporal supports human-in-the-loop via signals and pause/resume, allowing workflows to wait for manual input or approval.

Which one is easier to set up?

Aegra is easier if you are familiar with Docker Compose and LangGraph—it can be deployed in minutes. Temporal may require more setup but offers serverless workers to reduce ops.

Is Aegra fully open-source?

Yes, Aegra is licensed under Apache 2.0, allowing free use, modification, and distribution.

Can Temporal AI be self-hosted?

Yes, Temporal has an open-source core that can be self-hosted. Temporal Cloud is a managed option with additional features like serverless workers.

Does Aegra handle retries and recovery like Temporal?

Aegra relies on LangGraph's checkpoint and persistence mechanisms; it does not offer the same level of built-in durable execution as Temporal's automatic state capture and recovery.

Which integration ecosystem is larger?

Temporal has a broader set of integrations including OpenAI Agents SDK, Google ADK, Slack, NVIDIA GPU fleet, Salesforce, and more. Aegra focuses on LangGraph SDK, common LLMs, and tracing tools.

What are the hosting options for each?

Temporal Cloud offers managed hosting; also supports self-hosting. Aegra is designed for self-hosting on your own infrastructure using Docker Compose; no managed cloud option.

What else are Aegra and Temporal AI compared with?

Where can you read more about Aegra and Temporal AI?

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