# ibl.ai vs LangGraph

> Source: https://ibl.ai/resources/comparisons/self-hosted-ai-vs-langgraph
> Last updated: 2026-10-20


*An MIT-licensed graph framework you build a production agent stack around, or an owned platform that ships that stack — agents, memory, RAG, evals, guardrails — on your infrastructure*

**On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.**

## What's the difference between ibl.ai and LangGraph?

On ownership, ibl.ai and LangGraph are closer than most comparisons admit. LangGraph is MIT-licensed and runs wherever your Python or JavaScript runs. On ibl.ai you own all the code and the data as well: the license delivers the platform source, deployed inside your perimeter.

So the real difference is what you receive. LangGraph is a low-level orchestration library — graphs, state, durable execution, human-in-the-loop. Memory stores, RAG ingestion, evaluation, guardrails, access control and an end-user interface are yours to assemble.

The production tooling around LangGraph is LangSmith. Its Plus plan is $39 per seat per month and cloud-only; self-hosted and hybrid LangSmith require the Enterprise plan and a license key, per LangChain's pricing and docs.

ibl.ai ships agents, memory, datasets, evals, MCP connectors, safety filters, RBAC and SSO as one platform. It is model-agnostic, has no per-seat pricing, and you deploy anywhere: your cloud, a VPC, on-premise or air-gapped. ibl.ai is family-owned and operated from New York, NY.

## How do ibl.ai and LangGraph compare feature by feature?

### Ownership & Deployment

| Criteria | ibl.ai | LangGraph |
|----------|--------------------|--------------------|
| Source code you receive | Platform source under license; the OS frontend and infra-cli are MIT on GitHub. | The library is MIT-licensed and open on GitHub (43.6K stars, October 2026). |
| Where the production stack runs | infra-cli provisions AWS or GCP, or bootstraps any existing server; air-gapped is supported. | The library runs anywhere. Self-hosted LangSmith (tracing, evals, deployment) needs the Enterprise plan. |
| Pricing shape | Usage-based with no per-seat pricing; spend caps per org, agent and user. | Library free. LangSmith Plus is $39 per seat per month plus usage billed in $1 LSUs. |
| Model choice | Provider cards include Amazon, Anthropic, Google, Microsoft, NVIDIA, OpenAI and xAI; switch per agent. | Model-agnostic by construction — a node calls whatever model your code calls. |

### Agent Engineering

| Criteria | ibl.ai | LangGraph |
|----------|--------------------|--------------------|
| Graph control flow & custom state | Visual workflows with If/else, While, User approval and Set state nodes — less expressive than code. | The reason LangGraph exists: arbitrary graphs, cycles, typed state and subgraphs in code. |
| Durable execution & human-in-the-loop | User approval nodes in workflows and scheduled tasks with run logs. | Checkpointed state resumes after failures; inspect and edit state at any step. |
| Ecosystem & community | 157 open agent skills in iblai/vibe, plus MCP to any external server. | The largest agent ecosystem: LangChain alone has 148K GitHub stars and hundreds of integrations. |

### What Ships Around the Agent

| Criteria | ibl.ai | LangGraph |
|----------|--------------------|--------------------|
| Memory | Global and agent-specific memories in PGVector, five default categories, REST API. | Short-term and long-term memory primitives; you pick and operate the store. |
| RAG ingestion | Datasets from PDF, DOCX, CSV, URLs, crawls, Drive, OneDrive and GitHub on a retrain schedule. | Retrieval is a node you write; loaders, vector store and re-indexing are yours. |
| Evals & tracing | Benchmarks up to 10,000 rows, LLM-as-Judge and human review, cost and latency analytics. | LangSmith is best-in-class tracing and evals — on per-seat cloud plans or Enterprise self-host. |
| Guardrails, RBAC & end-user UI | Moderation and safety prompts, PII redaction across 13 entity types, RBAC, SSO, SCIM and a chat UI. | Not in scope for an orchestration library; you build or buy each layer. |

## Where does LangGraph win, and where does ibl.ai?

### Is LangGraph or ibl.ai better at agent control flow?

**ibl.ai:** ibl.ai workflows cover branching, loops, approvals and state with 11 node types — enough for most business processes, and editable by non-engineers.

**LangGraph:** LangGraph is more expressive. Cycles, typed state, subgraphs and checkpointing in code let an engineer model behaviour no visual builder can.

**Verdict:** LangGraph wins on control flow. If your agent is the product and its graph is novel, that expressiveness is worth owning the rest of the stack yourself.

### What does each one cost to run in production?

**ibl.ai:** ibl.ai bills by usage against caps you set, with no per-seat pricing, so 50 builders and 5,000 end users do not multiply a seat line.

**LangGraph:** The LangGraph library is free. LangSmith Plus costs $39 per seat per month plus LSU-metered deployment compute, and self-hosting it is an Enterprise contract.

**Verdict:** Free code is not a free stack. Price the tracing, deployment, memory store, vector database and the engineers who run them, not the library alone.

### Who operates the stack in each case?

**ibl.ai:** ibl.ai runs on infrastructure you control, operated by your team or with forward-deployed engineers who connect it to your systems of record.

**LangGraph:** With LangGraph your team owns every layer it assembled — upgrades, hardening, incident response — or LangChain operates LangSmith in its cloud.

**Verdict:** Both keep the code in your hands. The question is whether your platform team wants to own the surrounding layers permanently.

## Should my team choose LangGraph or ibl.ai?

### Teams whose agent architecture is the product — LangGraph

Novel graphs, custom state and fine-grained checkpointing are LangGraph's strength, and a strong engineering team can supply the rest deliberately.

### Organizations deploying governed agents to thousands of users — ibl.ai

RBAC, SSO, PII redaction, audit and spend caps ship with the platform, and there is no per-seat pricing as the user count grows.

### Regulated or air-gapped environments — ibl.ai

The whole platform — not just the library — installs inside your perimeter without an Enterprise add-on for the tooling around it.

### Teams already invested in LangGraph — Either

Keep a specialist LangGraph workflow behind an MCP server and let ibl.ai agents call it, while users, data and governance live on the platform.

## How do I migrate from LangGraph to ibl.ai?

### LangGraph → ibl.ai (medium difficulty)

Timeline: Four to eight weeks for a typical agent portfolio, longer for heavily custom graphs

- Agents: each graph's system prompt and model become an ibl.ai agent created through the agent-with-settings API; branching graphs become Workflows with If/else, While and User approval nodes.
- Tools: wrap LangChain tools as MCP servers; ibl.ai agents connect to any MCP server URL over WebSocket or Streamable HTTP, with no auth, an API key or OAuth.
- Memory: checkpointed threads map to ibl.ai chat sessions, and long-term store entries map to global or agent-specific memories.
- RAG: re-ingest sources as agent datasets — files, URLs, crawls, Drive, OneDrive, GitHub — with a retrain schedule instead of your own loaders.
- Eval: move test questions into a CSV with an input column and optional expected_output, then score runs with LLM-as-Judge or human review.
- Deployment: provision with infra-cli on AWS or GCP, or bootstrap an existing server, and run both stacks in parallel until the eval set matches.

Docs and skills for this move:

- [Create an agent over the API (quickstart)](/developer/platform/agents/quickstart)
- [Skill: iblai-api-agent-create](/developer/skills/iblai-api-agent-create)
- [Workflows: the graph builder](/docs/os/chat-canvas/workflows)
- [Connect an MCP server](/developer/platform/mcp/mcp-connections)
- [Agent memory](/developer/platform/agents/memory)
- [Skill: iblai-api-agent-dataset](/developer/skills/iblai-api-agent-dataset)
- [Evaluations](/developer/platform/agents/evaluations)
- [Deploy with infra-cli](/developer/infra-cli)

### ibl.ai → LangGraph (medium difficulty)

Timeline: Weeks for the graph, longer for the surrounding layers

- Prompts and settings read out over the REST API, and chat history and evaluation results export as files — you hold the source and the data on both sides.
- Plan to rebuild memory, retrieval, guardrails, RBAC and the chat interface as your own layers.
- Decide whether tracing and evals run on LangSmith cloud seats or a self-hosted Enterprise install.
- Reasonable when one agent's control flow outgrows a visual workflow and the team wants it fully in code.

## FAQ

**Q: Is LangGraph open source?**

Yes. The LangGraph library is MIT-licensed on GitHub. LangSmith, the tracing, evaluation and deployment product around it, is commercial: Developer and Plus are cloud-only, and self-hosting requires the Enterprise plan.

**Q: Can I self-host LangGraph?**

The library, yes — anywhere Python or JavaScript runs. Self-hosted LangSmith needs an Enterprise plan and license key, and you supply Kubernetes or Docker, PostgreSQL and Redis, per LangChain's documentation.

**Q: Is ibl.ai built on LangGraph?**

No. ibl.ai is its own platform. Its MCP layer resolves connections at inference time in a LangChain runtime, and its agents can call a LangGraph workflow you expose as an MCP server.

**Q: Does ibl.ai charge per seat?**

No. ibl.ai has no per-seat pricing: you pay by usage against spend caps you set per organization, agent or user, so cost tracks consumption rather than headcount.

**Q: When should I pick LangGraph over ibl.ai?**

When the agent's control flow is the hard part and your team wants to own every surrounding layer. LangGraph is the more expressive tool for custom graphs, and that is a legitimate reason to choose it.

**Q: How does ibl.ai fit alongside LangGraph?**

On ibl.ai you own all the code and the data, run any model and deploy anywhere. Teams often keep one specialist LangGraph workflow and run users, data, evals and governance on the platform.


## Where do these facts come from?

1. LangGraph is MIT-licensed and offers durable execution, human-in-the-loop and short- and long-term memory. [langchain-ai/langgraph on GitHub](https://github.com/langchain-ai/langgraph)
2. LangSmith Plus is $39 per seat per month; Developer and Plus are cloud-hosted; Enterprise adds hybrid and self-hosted; usage is billed in $1 LSUs. [LangChain pricing](https://www.langchain.com/pricing)
3. Self-hosted LangSmith requires an Enterprise plan and license key; you provide Kubernetes or Docker, PostgreSQL and Redis. [LangChain docs: Deploy to self-hosted](https://docs.langchain.com/langsmith/deploy-to-self-hosted-overview)
4. LangChain's main repository is MIT-licensed with roughly 148K GitHub stars as of October 2026. [langchain-ai/langchain on GitHub](https://github.com/langchain-ai/langchain)


## Where does ibl.ai fit alongside LangGraph?

**ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.**

ibl.ai is the layer a LangGraph team would otherwise build: agents, memory, datasets, evaluation, MCP connectors, safety filters, RBAC and SSO, delivered as source and deployed on your infrastructure.

Agentic OS is the platform; Agentic Vibe gives coding agents 157 skills to build apps on it. You own all the code and the data, run any model, and keep a specialist graph behind MCP when code-level control flow still wins.

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.