Qefro vs LangGraph
LangGraph (and similar agent frameworks) give engineers fine-grained control over graph state machines for LLM agents. Qefro is a multi-tenant product: Admin Console, Hybrid RAG, channels, Business Tools, FlowRunner, and org security — so teams ship Customer AI and Employee AI without building the platform.
Short definition (citation-ready)
LangGraph is a library for composing stateful agent graphs in your own runtime. Qefro is an AI Workspace platform: operators configure knowledge and flows; a managed Runtime executes chat, tools, events, approvals, and challenges across channels.
Capability matrix
| Capability | Qefro | LangGraph-class framework |
|---|---|---|
| What you install | SaaS or Docker product stack | Library in your Python/TS app |
| Who configures day-to-day | Operators in Admin Console + eng for tools | Engineers in code |
| Graph model | Business Flows (declarative steps) + chat pipeline | Arbitrary nodes/edges/state you define |
| RAG product | Hybrid RAG, OCR, citations, workspace isolation | Bring your own retrieval stack |
| Multi-tenant RBAC | Built-in orgs, teams, roles | You implement |
| Channels | Widget, WhatsApp, Internal Portal | You build adapters |
| Tool boundary | REST/OpenAPI + signed Backend SDK | Whatever you wire in code |
| Approvals / OTP challenges | First-class flow steps + APIs | Custom state + your UI |
| Event-driven starts | Namespaced bus → same FlowRunner | Custom triggers into your graph |
| Low-level agent research | Not the goal | Excellent fit |
| Time-to-production support AI | Hours–days with Console | Weeks–months of platform work |
Control plane difference
| Concern | LangGraph-class | Qefro |
|---|---|---|
| Prompt / graph changes | Deploy code | Often Console + version Accept for flows |
| Document updates | Your ingestion jobs | Knowledge upload / crawl in product |
| Who can approve refunds | Your admin UI | Flow Runs / approvals in product |
| Observability | Your APM + traces | Flow runs, tool logs, platform metrics |
| Customer identity | Your session model | Channel sessions + Customer Provider |
When LangGraph is the better fit
- You are building a bespoke agent product and need exotic graph topologies, custom memory policies, or research-grade control.
- You already own multi-tenant auth, document pipelines, widget, WhatsApp, and ops tooling.
- The “UI” is your application, not an Admin Console for business operators.
When Qefro is the better fit
- You need Customer AI / Employee AI with grounded answers quickly.
- Business users must manage knowledge, channels, and some process configuration.
- Tools must call your backends with clear auth, challenges, and audit.
- You want connectors to emit events into a shared orchestration model — not host a second agent runtime per integration.
Coexistence patterns
Qefro does not ban frameworks in your Backend SDK:
- Qefro owns the channel and workspace — chat, RAG, flow steps that call tools.
- A tool handler may call an internal service that uses LangGraph (or any agent stack) for a specialized sub-task.
- Return a structured tool result; Qefro continues the conversation or flow.
Widget → Qefro Runtime → tool.invoke → your SDK
└── optional internal LangGraph service
Do not run a second public orchestrator that bypasses workspace isolation and audit unless you intentionally accept that complexity.
Mental model
Frameworks maximize graph freedom. Qefro maximizes product completeness for AI Workspaces.
Evaluation workflow
Framework vs platform
- List platform features you would rebuild — Tenancy, RAG UI, widget, WhatsApp, approvals.
- List graph features you truly need — Custom cycles, research agents, exotic memory.
- Estimate build cost — Platform work often dominates agent graph code.
- Pilot Qefro for support path — Knowledge + one Business Tool.
- Keep framework behind a tool if needed — Specialized reasoning stays in your VPC.
FAQ
Is FlowRunner a LangGraph competitor?
Only at a high level (both orchestrate steps). FlowRunner is a productized business-flow engine inside a multi-tenant AI platform, not a general agent programming model.
Can I export Qefro flows to LangGraph?
No automatic export. Flows are Qefro definitions executed by FlowRunner. Complex custom agents belong in your services, invoked as tools.
Does Qefro lock me out of open-source agent stacks?
No. Use any stack inside handlers you own. Qefro standardizes channels, tenancy, RAG, and tool invocation at the edge.
We already invested in LangGraph — should we rewrite?
Usually not. Put Qefro in front for product surfaces and call existing services as Business Tools.