Your team chose LangGraph for its graph-based state management, but now you're stuck debugging state persistence issues while your product roadmap stalls. The agent works in development, then fails in production when real data hits edge cases your tests didn't anticipate.

Evaluating alternatives feels like the logical next step. A different approach might fit your workflow better. As you compare options, including LangGraph, CrewAI, LlamaIndex, and Logic, two questions emerge: which orchestration style fits your workflow, and whether production infrastructure is included, available through paid add-ons, or left to you entirely. How you handle prompt management, testing, versioning, model routing, and structured outputs determines how quickly you ship.

How LangGraph, Logic, CrewAI, and LlamaIndex Compare

Teams searching for LangGraph alternatives have several options to consider. Each takes a different approach: Logic focuses on abstracting the infrastructure layer entirely, CrewAI focuses on role-based agent coordination, and LlamaIndex on retrieval-first architecture. The comparison below breaks down how they differ.

LangGraph: Graph-Based Orchestration Framework

LangGraph structures agent workflows as directed graphs, giving you explicit control over how agents move between states. This architecture fits teams building workflows with complex conditional logic, where the next step depends on prior outputs and agents may need to loop back or branch based on results.

LangGraph extends LangChain, so teams already invested in that ecosystem have a natural adoption path. The graph model also provides visibility into agent decision-making, which matters for workflows where you need to explain or audit why an agent took a particular path.

Graph-based abstractions require significant upfront design work and carry a steep learning curve. Defining state schemas, managing checkpointers, and handling graph cycles requires understanding LangGraph's execution model before writing business logic. A simple conditional workflow that takes 10 minutes in pseudocode can require multiple hours of LangGraph configuration.

That investment might seem worthwhile for complex workflows, but the maintenance burden tells a different story. When requirements change, you're not just updating logic; you're tracing node dependencies, reworking execution paths, and retesting graph traversals. A change that should be near-instant stretches into days. And because LangGraph depends on LangChain, you inherit that ecosystem's update cycles and breaking changes alongside your own. Each layer adds friction to every future modification.

What you build yourself: Prompt management, testing infrastructure, version control, error handling, structured output parsing, multi-model routing, and deployment pipelines.

Logic: Production API with Orchestration and Infrastructure Included

Logic takes a different approach. Instead of providing orchestration primitives that you have to build on top of, you simply describe what you want and get a production API with the infrastructure already included.

Engineers write a spec describing the agent's behavior: what inputs it accepts, what logic it applies, what outputs it returns. Logic generates typed REST APIs with auto-generated tests, version control with instant rollback, and multi-model routing across ChatGPT, Claude, and Gemini. When requirements change, you update the spec and the API updates instantly without redeployment.

Teams building highly custom orchestration or novel AI architectures may prefer low-level control and find Logic less flexible than they need. Logic fits teams who value getting to production quickly over customizing every aspect of their LLM infrastructure.

What's included: Prompt management, testing infrastructure, version control, error handling, structured output parsing, multi-model routing, exposing your agent as a REST APIs, MCP server, or web interface.

{{ LOGIC_WORKFLOW: moderate-product-listing-for-policy-compliance | Moderate product listings for policy compliance }}

CrewAI: Role-Based Orchestration Framework

CrewAI organizes agents into role-based teams that collaborate on tasks through sequential delegation. The framework fits teams whose workflows map naturally to collaborative patterns: research that feeds into drafting that feeds into review, or multi-step processes where distinct responsibilities hand off sequentially.

The role-based model tends to be more intuitive for teams new to agent development than LangGraph’s state machines, since the "team of specialists" metaphor maps to how many teams already think about dividing work. If you can describe your workflow as "a team of specialists working together," CrewAI provides abstractions that match how you already think about the problem. CrewAI also offers a managed cloud platform for teams who want to reduce some operational overhead.

The challenge emerges when production workflows don't map cleanly to sequential handoffs between defined roles. When agents need to backtrack based on intermediate results, collaborate dynamically rather than linearly, or handle responsibilities that shift based on context, the task delegation model creates friction. Each role boundary becomes a potential failure point requiring error handling, and each handoff needs validation logic to ensure the previous agent completed its work correctly.

Teams building dynamic agent interactions, where responsibilities shift based on context, sometimes find rigid role definitions limiting. The same applies to teams who prefer fewer agents handling broader responsibilities for easier maintainability.

What you build yourself: Prompt management, testing infrastructure, version control, error handling, structured output parsing, multi-model routing, deployment pipelines, and exposing your agent as an API (unless using CrewAI's Enterprise plan).

LlamaIndex: Retrieval-Focused Framework

LlamaIndex focuses on retrieval-augmented workflows, providing infrastructure for connecting agents to external data sources. While the framework has expanded beyond its RAG origins to support general agent workflows, its core strength remains document-heavy pipelines with complex retrieval strategies.

For retrieval-heavy use cases, LlamaIndex handles complexity that other frameworks leave to you: chunking strategies, index optimization, and query routing across multiple data sources. Teams building RAG applications often find LlamaIndex saves significant development time compared to building retrieval infrastructure on top of a general-purpose orchestration framework.

Teams whose agents don't primarily interact with document stores may find the retrieval focus adds complexity without benefit. If your workflow is orchestration-heavy but retrieval-light – e.g. routing requests, coordinating multi-step processes, or handling structured data transformations –  LlamaIndex's strengths don't apply.

What you build yourself: Prompt management, testing infrastructure, version control, error handling, structured output parsing, multi-model routing, and deployment pipelines.

The Production Infrastructure Gap

Orchestration is only part of the picture. Managing how agents move between states, call tools, and coordinate workflows gets you partway to production. The rest requires additional infrastructure layers, each demanding dedicated engineering time.

Prompt Management

Agent behavior changes as you iterate, and each change carries risk. Teams need systems to track which prompts are running in production, what changed between versions, and how to verify that fixes don't introduce new failures. Without this infrastructure, debugging production issues becomes guesswork.

Testing Infrastructure

LLM outputs vary between runs, so traditional testing approaches don't transfer cleanly. Teams need evaluation systems that assess output quality across diverse inputs, catch regressions before deployment, and scale as agent complexity grows. Building and maintaining this infrastructure requires ongoing engineering investment.

Version Control and Rollback

Deployments sometimes go wrong, and teams need the ability to revert to a known-good state without redeploying the entire application. This requires version control designed for prompt and configuration changes, not just code, along with rollback mechanisms that can restore previous behavior quickly.

Error Handling

LLM integrations fail in ways that traditional software doesn't: API timeouts, rate limits, malformed responses, context window overflows. Production systems need retry logic, fallback strategies, and graceful degradation paths that only surface as requirements once real-world load exposes them.

Structured Output Parsing

Agents that return consistently structured responses in demos often return different responses in production when inputs vary from expected patterns or when there has been a change to the underlying model. Teams need validation and transformation layers that ensure downstream systems receive reliable, structured data regardless of how the LLM formats its output.

Multi-Model Routing

Different models have different strengths, costs, and latency profiles, and production systems often need to route requests based on task requirements rather than hardcoding a single provider. Teams end up building routing logic and managing multiple provider integrations alongside the orchestration layer.

Observability and Debugging

An agent fails in production. The logs may show "LLM request failed" but not which prompt version was running, what the input was, what the model actually returned, or why validation failed. Teams need infrastructure that connects requests to prompt versions, captures full input/output pairs, and surfaces the specific point of failure. Without this visibility, debugging becomes trial and error.

This infrastructure competes directly with product development for the same engineering hours. Every month your team spends building agent infrastructure is a month they're not shipping features that move your business forward.

With LangGraph, CrewAI, and LlamaIndex, you manage this infrastructure yourself or pay for additional platforms to cover parts of it. Logic includes all of it out of the box: prompt management, auto-generated tests, version control with instant rollback, multi-model routing, error handling, and structured outputs. The infrastructure gap disappears so your engineers stay focused on your core product.

Making the Infrastructure Decision

Every approach requires a decision about infrastructure ownership. LangGraph, CrewAI, and LlamaIndex give you orchestration primitives, but you manage the production infrastructure yourself. Logic lets you offload that infrastructure entirely. The right choice depends on where your team's engineering capacity should go.

When Managing Infrastructure Yourself Makes Sense

Managing infrastructure yourself makes sense when that infrastructure is your competitive advantage. If your differentiation comes from proprietary orchestration patterns, custom model fine-tuning, or novel agent architectures, the infrastructure work creates value rather than consuming it.

It also makes sense when you need architectural control that platforms can't provide. Systems requiring on-premises deployment, proprietary model hosting, or integration with classified networks sometimes require ownership of every layer.

Finally, managing infrastructure yourself fits teams with dedicated platform engineering capacity. If you have engineers whose job is building internal tooling and infrastructure, and that capacity doesn't compete with product development, the calculus shifts.

When Offloading Infrastructure Makes Sense

Offloading infrastructure makes sense when speed to production matters more than architectural control. Teams under competitive pressure, facing board questions about AI roadmaps, or racing to validate product-market fit often can't afford weeks or months of infrastructure development before shipping value.

It also fits teams with constrained engineering bandwidth. Early-stage startups typically have small engineering teams where every engineer is needed for product development. Diverting engineers to infrastructure work for months creates opportunity cost that compounds as competitors ship.

Offloading also changes who owns agent logic after initial deployment. With Logic, domain experts can update rules if you choose to let them, with every change versioned and testable using guardrails you define. Nothing goes live without passing your tests. Engineering stays focused on product work while the people closest to the business logic maintain it.

The Honest Assessment

For most early-stage startups, the assessment favors offloading. Engineering bandwidth is constrained, timelines are compressed, and competitive advantage typically lives in the product rather than the infrastructure underneath it. The Dust CTO analysis puts it directly: even technically capable AI companies offload infrastructure when the engineering commitment would pull capacity from differentiated product work.

Managing production infrastructure yourself means months of engineering time on prompt management, testing, versioning, deployment, and monitoring systems. Logic handles all of it so your engineers stay focused on your core product without accumulating infrastructure debt.

Offloading Infrastructure with Logic

For teams whose assessment favors offloading, the next question is what that actually looks like in practice. Logic provides the production infrastructure that most frameworks leave to you: prompt management, testing, versioning, model routing, error handling, and structured outputs. You can have a working proof of concept in minutes and ship to production the same day.

You write a spec describing what you want: what inputs the agent accepts, what logic it applies, what outputs it returns. The spec controls the agent. Logic generates a typed REST API with structured JSON outputs that integrate cleanly with existing systems. Behind each agent, 25+ processes execute automatically: research, validation, schema generation, test creation, and model routing optimization. All of that complexity runs in the background while you see the production API appear.

The spec is simultaneously your agent's behavior definition and your API contract. Update the spec, and the API schema updates automatically without breaking existing integrations. With other frameworks, you're managing code, prompts, and API schemas as separate concerns that can drift out of sync.

When requirements change, you update the spec and the API updates instantly without redeployment — all while the API contract remains stable. Version control with instant rollback means you can iterate safely, and auto-generated tests validate changes before they go live.

The platform processes 200,000+ jobs monthly with 99.999% uptime over the last 90 days, backed by SOC 2 Type II certification with HIPAA available on Enterprise tier. Deploy through REST APIs, MCP server for AI-first architectures, or the web interface for testing and monitoring.

Without Logic, this infrastructure becomes custom development work: engineering time spent on prompt management systems, testing harnesses, deployment pipelines, and monitoring rather than your core product. Logic compresses months of infrastructure development into minutes, so you ship production-ready capabilities without diverting engineers from differentiated product work.

Offloading Infrastructure in Practice

Garmentory's marketplace faced this decision when scaling their content moderation. The platform processes roughly 1,000 new product listings daily, each requiring validation against a 24-page standard operating procedure. Four contractors worked eight-hour shifts to keep pace, but review times still stretched to seven days with a 24% error rate. During Black Friday, backlogs reached 14,000 items. Products under $50 couldn't be listed at all because moderation costs exceeded margins.

Managing custom moderation infrastructure would have meant months of engineering work: prompt development, testing frameworks, validation pipelines, deployment systems, and ongoing maintenance as marketplace guidelines evolved. That engineering commitment would have competed directly with product development for the same limited team capacity.

Garmentory chose to offload infrastructure instead. Their merchandising team described the moderation rules in a Logic spec and had a working API the same day. Processing capacity increased from 1,000 to over 5,000 products daily. Review time dropped from seven days to 48 seconds per listing. Error rate fell from 24% to 2%. The contractor team went from four to zero. The product price floor dropped from $50 to $15, unlocking thousands of listings that previously couldn't justify moderation costs.

The platform now handles 190,000+ monthly executions with 250,000+ total products processed. When marketplace guidelines change, Garmentory updates the spec without engineering cycles or deployment risk, because Logic provides version control with instant rollback and auto-generated tests that validate changes before they go live.

From Framework Search to Shipped Product

Teams searching for LangGraph alternatives discover that orchestration style matters, but infrastructure ownership often matters more. LangGraph's graphs suit workflows with complex conditional logic and state transitions. Logic fits teams who need to ship without building infrastructure themselves. CrewAI's role-based delegation fits sequential handoffs where distinct responsibilities pass work linearly. LlamaIndex's retrieval focus serves document-heavy pipelines. Matching the right approach to your workflow patterns and infrastructure constraints saves development time, and each brings genuine strengths for the right use case.

The deeper question isn't orchestration. It's whether production infrastructure competes with your product development for engineering time, or whether you offload it entirely. That work competes directly with product development for the same engineering hours, and for most early-stage startups, the assessment favors offloading.

Logic provides that production infrastructure so your team ships AI capabilities without months of custom development. Your engineers stay focused on your core product. Start building with Logic.

Frequently Asked Questions

Can I migrate to Logic if I've already started building?

Yes. Logic generates standard REST APIs, so you can run it alongside existing implementations during transition. Teams typically start by offloading one workflow to Logic while keeping others on their current stack, then expand based on results.

What happens when my requirements change after deployment?

You update the spec and the API updates instantly without redeployment. Every change is versioned with instant rollback available, and auto-generated tests validate changes before they go live. Domain experts can own these updates if you choose to let them, with guardrails you define.

How does Logic handle complex orchestration patterns like branching or looping?

Logic handles conditional logic, branching, and multi-step workflows within the spec. For teams that need fine-grained control over agent state transitions or custom orchestration patterns beyond what the spec model supports, frameworks like LangGraph offer more flexibility at the cost of managing infrastructure yourself.