The orchestration
runtime for AI workflows
PlexRun is the infrastructure layer for building, deploying, and scaling AI-powered automation pipelines. Run multi-agent systems, manage workflow state, and observe execution — without managing infrastructure.
Works with the tools and models your team already uses
Built for production
Not a prototype. Every design decision — retries, idempotency, state isolation — is made for production workloads, not demos.
Developer-native
Python SDK, CLI, and YAML-first. Define workflows in code, version them in Git, deploy with one command.
Full observability
Per-step execution traces, token usage, latency, and cost attribution. Every failure is debuggable in seconds, not hours.
Who it's built for
ML Engineers
InfrastructureTired of writing retry logic and queue management from scratch for every new pipeline.
Backend Engineers
ObservabilityDeploying AI agents to production means debugging across 5 different tools with zero execution trace.
Engineering Leaders
ReliabilityAgent pipelines that work in demos fail in production. The gap is always orchestration, not the model.
The problem
AI in production is
an infrastructure problem.
Most AI workflows die between the demo and production. The model works. The orchestration doesn't. These are the four problems every team hits.
Fragmented orchestration
Engineering debtTeams stitch together LLMs, queues, schedulers, and APIs into brittle pipelines that fail in production. Every new model or tool means more glue code.
Scaling agents is hard
Infrastructure ceilingMulti-agent systems hit infrastructure limits fast — concurrency, shared state, retries, and backpressure are unsolved. You either over-provision or things break.
Zero observability
Operational painWhen an AI workflow fails at 2am, debugging means grepping scattered logs across five tools with no execution trace. Mean time to fix is hours, not minutes.
Reliability is an afterthought
Production incidentsLLM calls fail. APIs time out. Queues back up. Most teams have no retry policy, no idempotency, and no recovery path — until they're paged at midnight.
How it works
From idea to production
in three steps.
PlexRun abstracts infrastructure complexity so your team can focus on building workflows, not managing servers.
Define your workflow
Describe your AI pipeline in Python or YAML. Declare steps, models, branching logic, and retry policies.
Deploy in seconds
One command. PlexRun provisions compute, queues, and state stores automatically.
Monitor and iterate
Every execution is fully traced. Debug failures, inspect token usage, replay steps.
Platform
Infrastructure for AI workflows — solved.
Everything you need to ship AI automation to production. No glue code, no hand-rolled retry logic, no custom orchestration layer.
Workflow Engine
CoreDAG-based execution with native support for branching, fan-out, fan-in, and conditional routing. Define complex pipelines in Python or YAML — no state management required.
Multi-Agent Orchestration
Run coordinated agent swarms with shared state, message passing, and supervised execution.
Distributed Execution
Auto-scaling worker pools across regions. Sub-second cold starts on serverless runtime.
Real-Time Observability
Per-step traces, token usage, latency, and cost. Replay any execution. Export to OpenTelemetry.
Retries & Idempotency
Exponential backoff, circuit breakers, dead-letter queues, and idempotent step keys built in.
Event-Driven Triggers
Schedule, webhook, queue, or file-based triggers. React to any upstream system in real time.
Versioning & Rollback
Immutable workflow deployments. Promote between environments. One-command rollback.
Secure by Default
VPC isolation, encrypted state, audit logs, and least-privilege execution by default. RBAC and SSO on Enterprise.
API-First
Python and TypeScript SDKs. REST API. CLI. Webhooks. Integrate with anything.
Architecture
Production infrastructure, out of the box.
PlexRun runs on AWS-native infrastructure — purpose-built for AI workloads. Serverless. Auto-scaling. Multi-region. Observable.
Use cases
Built for teams shipping real AI.
Customer Support Automation
Triage tickets, route to specialists, generate first-pass replies, and escalate edge cases — all in a single observable workflow.
Document Processing
Extract, classify, validate, and route documents through multi-stage AI pipelines with full audit trails.
Research & Enrichment
Crawl, synthesize, and structure unstructured data using coordinated agent workflows at scale.
Multi-Agent Systems
Run agent teams with shared memory, role specialization, and supervised execution across complex task graphs.
Data Transformation
ETL pipelines with AI-native steps. Classify, summarize, and enrich millions of records without hand-rolled retry logic.
Internal Tool Automation
Replace fragile Zapier flows with reliable, observable AI-powered automations your on-call can actually debug.
How we compare
Built for AI workflows.
Not adapted from them.
Existing orchestration tools were built for data pipelines and microservices. PlexRun is the first orchestration runtime designed from the ground up for LLM-native, multi-agent workloads.
| Feature | PlexRunours | Step Functions | Prefect | Temporal |
|---|---|---|---|---|
AI-native step model Designed for LLM calls, not generic tasks | ||||
Per-step token tracking Token usage + cost attributed per step, per run | ||||
Idempotent retries Retry without re-calling the LLM on success | ||||
Zero infra to manage Serverless by default, no fleet to operate | ||||
Python-native SDK Define workflows with decorators, no DSL | ||||
YAML workflow definition Declarative pipelines with DAG support | ||||
Built-in observability Execution traces without extra instrumentation | ||||
Multi-model support Route steps to different LLMs independently | ||||
Based on publicly available documentation as of 2025. Feature parity may vary by version.
Build with us.
Shape the roadmap.
We're working with a small cohort of engineering teams who are actively building AI-powered products. Design partners get direct access to the team, free platform usage during beta, and a permanent seat at the table on product direction.
We're looking for teams building real AI products in production — not side projects. Ideal partners have an existing orchestration pain point they need solved in the next 30–90 days.
Direct engineering access
Weekly 1:1 with the founding team. Your use case shapes the product roadmap directly.
Free platform usage
Full platform access at no cost throughout private beta. No credit card, no commitments.
Custom integrations
We'll build the connectors, triggers, or SDK methods your stack needs — on request.
Early adopter pricing
Lock in founding-customer rates that never increase as you scale to production.
Developer experience
Code-first. Infrastructure invisible.
Define workflows in Python, TypeScript, or YAML. Deploy with one command. PlexRun handles the rest — provisioning, scaling, retries, observability.
- Type-safe SDKs for Python & TypeScript
- Declarative YAML for GitOps workflows
- Local dev mode with hot reload
- Built-in tracing and replay debugging
- Native integrations: OpenAI, Bedrock, Anthropic, Cohere
from plexrun import Workflow, step, agent
from plexrun.aws import bedrock
workflow = Workflow(class="tok-string">"document-pipeline", runtime=class="tok-string">"aws-lambda")
@step(retry=3, timeout=60, queue=class="tok-string">"docs-input")
async def extract_content(document: bytes) -> dict:
class="tok-string">""class="tok-string">"Extract structured content via Bedrock."class="tok-string">""
return await bedrock.invoke(
model=class="tok-string">"anthropic.claude-3-sonnet",
prompt=fclass="tok-string">"Extract key fields: {document}",
)
@step(depends_on=[class="tok-string">"extract_content"], timeout=30)
async def classify_intent(content: dict) -> str:
classifier = agent(class="tok-string">"intent-classifier", model=class="tok-string">"claude-3-haiku")
return await classifier.run(content)
@step(depends_on=[class="tok-string">"classify_intent"])
async def route(intent: str, content: dict) -> None:
await queue.publish(fclass="tok-string">"workflow.{intent}", content)
workflow.deploy(region=class="tok-string">"us-east-1", concurrency=100)Community
Stay in the loop.
We build in public. Follow along on GitHub, join the Discord during beta, or read the changelog — we ship every two weeks.
GitHub
github.com/plexrun/sdk
Source code, SDK, CLI, examples, and issue tracking. Star to follow updates.
Discord
plexrun community
Ask questions, share workflows, and get support directly from the engineering team.
Newsletter
plexrun.com/changelog
Weekly digest: what shipped, what's next, and technical deep-dives from the team.
We build in public · Shipping every 2 weeks
Pricing
Pay for execution. Not infrastructure.
Usage-based pricing. No seats. No infrastructure surprises.
Free
For solo builders and prototypes.
- 10,000 workflow runs / month
- 1 concurrent worker
- Community Slack support
- 7-day execution history
- Open-source SDKs
Pro
For production AI workloads.
- 1M workflow runs / month
- 10 concurrent workers
- Priority email support
- 90-day execution history
- Custom domains & webhooks
- Multi-region deployment
- Advanced observability
Enterprise
For scale, compliance, and SLAs.
- Unlimited workflow runs
- Dedicated infrastructure
- VPC peering & PrivateLink
- Custom retention & audit logs
- Compliance roadmap (SOC 2 planned pre-GA)
- Uptime SLA on request
- Dedicated solutions engineer
All plans include open-source SDKs, public docs, and community support.
Early access
Get early access.
Shape the product.
Join the waitlist for private beta access, release notes, and a direct line to the team. No spam — ever.
Free to start · No credit card · Cancel anytime
LangChain provides primitives for building LLM apps. PlexRun provides the runtime to execute them at production scale — distributed workers, retries, queuing, state management, and observability. We compose well together: many users build with LangChain and deploy on PlexRun.