# LangGraph vs Brahmalabs: framework with a platform, or platform with a DSL > LangGraph's MIT framework and ELv2 server vs Brahmalabs' governed workflows: licensing, approvals, sandboxing, observability, and pricing compared with sources. - Canonical: https://www.brahmalabs.io/compare/langgraph-vs-brahmalabs/ - Published: 2026-07-05 - Facts verified: 2026-07-05 - Author: Karthick Selvam - Publisher: Brahmalabs (https://www.brahmalabs.io) — note: Brahmalabs is a party or adjacent vendor; see disclosure at the end. **The short version:** LangGraph is the more capable engineering tool: explicit graph semantics, code-level control over agent topology, and observability the rest of the market measures itself against. Brahmalabs ships governance — approvals, sandboxing, egress control, audit — as platform properties rather than code you write, but it is a young managed platform with SOC 2 still in progress. Choose by team shape: agent engineers with platform capacity should take LangGraph; teams that must clear a security review without building that layer themselves should look at Brahmalabs. ## The disagreement is about the unit you operate Your agents work. Now someone from security is asking three questions: who approved the agent's last production action, where did its network traffic actually go, and what happens to a run when the pod dies halfway through. LangGraph and Brahmalabs are both credible answers. They disagree about what the thing you operate should be. LangGraph is a framework with a platform attached. You write a graph in Python or TypeScript — nodes, edges, a checkpointer — and you get exact control over agent topology. [LangChain's own description](https://github.com/langchain-ai/langgraph) is "a low-level orchestration framework for building stateful agents," and the low-level part is deliberate: it [does not abstract prompts or architecture](https://www.langchain.com/langgraph). The platform, LangSmith Deployment (renamed from LangGraph Platform in [October 2025](https://changelog.langchain.com/announcements/product-naming-changes-langsmith-deployment-and-langsmith-studio)), then hosts that graph with queues, persistence, and streaming. Brahmalabs is a platform with a DSL inside. The unit is a governed workflow: a JSON document with 15 node types — agent, tool, condition, loop, human approval, code, HTTP, fork-join parallelism, child workflows, and others — validated for cycles and schema compatibility before it deploys. The visual builder and the DSL are the same artifact. Approvals, sandboxing, egress control, and audit are properties of the platform, not code you write around a graph. Neither model is wrong. They fit different teams, and the rest of this essay is about figuring out which one fits yours. ## What LangGraph gets right Credit first, because there is a lot to credit. **Explicit graph semantics with real durability.** LangGraph's state model is checkpointer-based: graph state snapshots [persist per thread](https://docs.langchain.com/oss/python/langgraph/durable-execution), enabling resume-after-failure, time travel through prior states, and interrupts for human review. The docs are honest about the seams — the in-memory saver does not survive restarts, production needs the Postgres or SQLite checkpointer, and checkpoints accumulate in long conversations and need pruning — but the model itself is sound and battle-tested. The managed runtime layers on [task queues, horizontal scaling for long-running workloads, durability across restarts, cron scheduling, and webhooks](https://www.langchain.com/langsmith/deployment). **Human-in-the-loop as a framework primitive.** First-class [interrupt APIs](https://www.langchain.com/blog/langchain-langgraph-1dot0) pause a graph mid-execution for review or modification, supporting multi-day approval processes, and LangChain 1.0 added human-in-the-loop middleware alongside summarization and PII-redaction middleware. **Observability that the rest of the market measures itself against.** LangSmith [ingests and exports OpenTelemetry, ships SDKs for Python, TypeScript, Go, and Java, and traces non-LangChain code](https://www.langchain.com/langsmith/observability) — the OpenAI SDK, the Anthropic SDK, Vercel AI SDK, LlamaIndex. Dashboards cover token usage, P50/P99 latency, error rates, and cost breakdowns; evaluation supports LLM-as-judge and code evals runnable from CI. You can adopt LangSmith without adopting LangGraph at all, and many teams do. **Production credibility.** The 1.0 announcement states LangGraph [has been powering agents at companies like Uber, LinkedIn, and Klarna for more than a year](https://www.langchain.com/blog/langchain-langgraph-1dot0); the GitHub README separately cites [Klarna, Replit, and Elastic](https://github.com/langchain-ai/langgraph). LangGraph 1.0 shipped October 22, 2025 with a commitment to no breaking changes until 2.0, and the release cadence is active — [1.2.7 landed June 30, 2026](https://github.com/langchain-ai/langgraph). **Compliance paper, today.** LangSmith is [SOC 2 Type II certified and HIPAA compliant](https://changelog.langchain.com/announcements/langsmith-is-now-soc-2-type-ii-compliant), with BAAs available on Enterprise plans; LangGraph Platform separately achieved SOC 2 Type II in August 2025. Cloud customers choose among [four regional instances](https://docs.langchain.com/langsmith/regions-faq) — GCP US, GCP EU, GCP APAC, and AWS US — on all plans, though there are no cross-region organizations, no region migration, and LangChain has no EU legal entity for direct contracting. Enterprise plans also get [custom roles with per-entity permissions and SCIM group sync](https://docs.langchain.com/langsmith/rbac) — genuinely ahead of Brahmalabs, which offers three fixed roles and no SCIM. **MCP in both directions.** The langchain-mcp-adapters library converts MCP tools into LangGraph tools, and [every agent deployed on LangSmith exposes its own /mcp endpoint](https://docs.langchain.com/langsmith/server-mcp) under the same auth as the rest of the API. ## The licensing split, walked through precisely This is the friction teams discover late, so it deserves precision rather than insinuation. There are two different pieces of software with two different licenses. The LangGraph framework library — the thing you `pip install` and build graphs with — is [MIT-licensed](https://github.com/langchain-ai/langgraph/blob/main/LICENSE), genuinely free, run it anywhere. The production server runtime, `langgraph-api` — the Agent Server that provides the HTTP API, persistence, task queues, and streaming around your graph — is licensed [Elastic License 2.0, not MIT](https://pypi.org/project/langgraph-api/). Running that official server in production requires a commercial license key, typically tied to an Enterprise plan. And a self-hosted install must set a `langsmithLicenseKey` in the Helm chart and [allow egress to beacon.langchain.com for license verification and usage reporting](https://docs.langchain.com/langsmith/kubernetes). For an enterprise, that resolves into three concrete consequences: 1. **Procurement.** "We're on the open-source stack" is true of the framework and not of the serving layer. The component that actually runs in production — queues, persistence, streaming endpoints — is a commercial dependency with a contract behind it. Your legal review should treat it as one from day one, [not discover it during deployment planning](https://rvernica.github.io/2026/03/langchain-license). 2. **Network policy.** Teams often choose self-hosting specifically to control egress. A self-hosted LangSmith install requires an outbound exception to beacon.langchain.com. That is a documented, bounded phone-home — license verification and usage reporting — but if your environment is air-gapped or your egress rules are allowlist-only, this is a negotiation, not a checkbox. 3. **The build-your-own escape hatch is real but expensive.** The MIT license fully permits writing your own serving layer around the framework. That means rebuilding the HTTP API, task queues, checkpointer persistence, and streaming yourself — the exact work you adopted a framework-plus-platform to avoid. None of this is hidden; it is all in the docs. But the mental model "LangGraph is open source" does not survive contact with the deployment architecture, and it is better to know that before the security review than during it. ## The churn tax The second honest friction is older and better documented: the LangChain family has a long-running reputation for abstraction and naming churn. The abstraction criticism peaked in a [1,000-plus-point Hacker News thread titled "Why we no longer use LangChain for building our AI agents,"](https://news.ycombinator.com/item?id=40739982) where the recurring complaint was that the abstractions obscure what is happening at each step — a real problem when prompt engineering depends on seeing exactly what went to the model. Even a [broadly positive framework comparison](https://news.ycombinator.com/item?id=41722243) noted that for experienced developers the abstractions can add unnecessary complexity, and that built-in parallelism "can complicate debugging a lot." LangGraph is lower-level than LangChain classic and dodges much of this, but it inherits the ecosystem's habits: LangGraph Platform became LangSmith Deployment and LangGraph Studio became LangSmith Studio in [October 2025](https://changelog.langchain.com/announcements/product-naming-changes-langsmith-deployment-and-langsmith-studio) — deployments and contracts unchanged, but every tutorial, runbook, and job description written before the rename now describes products that no longer exist by name. The pricing model has moved too: at [platform GA in May 2025](https://www.langchain.com/blog/langgraph-platform-ga) the free tier was framed around 100k node executions per month; the [July 2026 pricing page](https://www.langchain.com/pricing) instead bills per deployment run plus per-minute uptime, and adds newer metered products (Fleet runs, Engine compute units, per-second sandboxes). Two counterweights, in fairness. The 1.0 stability pledge — [no breaking changes until 2.0](https://www.langchain.com/blog/langchain-langgraph-1dot0) — directly addresses the churn history, and it has held since October 2025. And when [three CVEs were disclosed in March 2026](https://thehackernews.com/2026/03/langchain-langgraph-flaws-expose-files.html) (including a CVSS 9.3 deserialization flaw in langchain-core and a SQL injection in LangGraph's SQLite checkpointer), all three were patched; the residual risk sits with teams running older pinned versions, which is a supply-chain fact of any large OSS surface, not a LangChain-specific failing. ## What you operate on Brahmalabs On Brahmalabs the unit is not a graph you govern; it is a governed workflow. The distinction is where the controls live. Runs are durable by construction — every run is a Temporal workflow, so a run that started yesterday can finish today, and pause, resume, and cancel are signals rather than process management. An approval wait parks the run durably without burning compute. The human-approval node is a policy object, not an interrupt you build UI around: single approver or N-of-M multi-party consensus, rejection policies, SLA timeouts, escalation chains, and dynamic approval forms, delivered to a reviewer inbox. The execution surface is contained by default. Each agent step runs in an isolated container matched to the workload — browser image, data image, CI image — with CPU, memory, and timeout ceilings, from digest-pinned images. A DNS-plus-firewall sidecar enforces a per-run domain allowlist, and the domains a run actually contacted are captured and shown on the run page — so the security engineer's "where did the traffic go" question has a screen, not a grep. Credentials never reach the agent: the vault injects short-lived, scoped references at call time, so an exfiltrated prompt has nothing to leak. Guardrails and PII redaction run before inference, spend ceilings apply per workflow, and guardrail events are hash-chained. An append-only audit log spans eight event categories — auth, config, run, HITL, integration, key, tenant, guardrail — and is queryable and exportable. Customer data segregation is structural: isolated per-customer database schemas mean queries physically cannot cross tenants. MCP runs in both directions here too — external MCP servers come in with activation validation, per-workflow allow-listing, and per-server egress policy, and Brahmalabs itself can be driven from any MCP client, with the notable property that its MCP server requires explicit human approval for every mutation. The LLM cannot self-approve. On LangGraph, every one of those capabilities is achievable — `interrupt()` gives you the pause, LangChain 1.0 gives you PII middleware, LangSmith gives you traces. But the approver routing, the consensus policy, the escalation chain, the sandbox, the egress capture, and the audit record are application code and infrastructure you build, buy, or defer. That is the actual trade: LangGraph gives you a better graph and asks you to build the governance; Brahmalabs gives you the governance and asks you to accept its workflow model. And the candor Brahmalabs owes you: it is a young product in early access, with no public case studies or customer logos, SOC 2 Type II in progress rather than certified, and no public status page. There is no self-hosting — it is a managed platform, and on-prem or VPC deployment is an Enterprise engagement, not a downloadable core. API and DSL docs live behind the dashboard login rather than a public portal. There is no CLI, git-sync, or Terraform provider; workflows are versioned in-platform. Roles are three and fixed, with no SCIM. The integration catalog spans hundreds of actions across 300+ providers, which is real but far behind Zapier's roughly 8,000 apps, and its depth varies by provider; MCP servers close some of the gap. Per-run LLM cost in the UI is a labeled estimate, not exact metering. ## What the bill looks like Both prices below are as published in July 2026 and both vendors change pricing; check the linked pages before budgeting. | | LangGraph / LangSmith | Brahmalabs | |---|---|---| | Framework | [MIT, free](https://github.com/langchain-ai/langgraph/blob/main/LICENSE) | None — managed platform only | | Free tier | [Developer: 1 seat, 5k base traces/mo](https://www.langchain.com/pricing) | Free: 1,000 credits, 5 workflows, 3 agents | | Team tier | [Plus: $39/seat/mo, 10k base traces/mo, 1 free dev deployment](https://www.langchain.com/pricing) | Pro: $299/mo flat, 30,000 credits, SSO, multi-party approval | | Usage metering | [Traces $2.50/1k (base) to $5.00/1k (extended); deployments $0.005/run + uptime $0.0036/min (prod)](https://www.langchain.com/pricing) | Credits per run + sandbox minutes; not seats | | Inference | Your model keys via your code | Your model keys; no inference markup; flat platform fee per 1k tokens | | Self-hosting | [Enterprise: hybrid or full, license key + beacon egress](https://docs.langchain.com/langsmith/kubernetes) | Not available | | Compliance | [SOC 2 Type II, HIPAA BAA on Enterprise](https://changelog.langchain.com/announcements/langsmith-is-now-soc-2-type-ii-compliant) | SOC 2 Type II in progress; EU data residency by default | The structural difference matters more than the numbers. LangSmith meters seats, traces, runs, and uptime minutes — cost scales with how many people watch the system and how much telemetry it emits, and trace volume is the line item [reviewers most often flag](https://rvernica.github.io/2026/03/langchain-license). Brahmalabs meters credits per run and sandbox minutes — cost scales with execution volume, and the run stream, per-node traces, and run replay are part of the platform rather than a separately metered product. Neither model is cheaper in the abstract; a large team running few workflows pays less on Brahmalabs, a small team running enormous volume may pay less on LangSmith's Developer tier. ## When LangGraph is the right choice Advice you can hold me to: - **You need custom agent topologies.** Hierarchical multi-agent systems, dynamic control flow, agents that rewrite their own plans — [single, multi-agent, and hierarchical control flows in one framework](https://www.langchain.com/langgraph) is the product's core promise and it delivers. A 15-node DSL cannot express what arbitrary Python can. - **You want workflows as code in git.** Code review, CI, tests, reproducible builds. Brahmalabs has no CLI or git-sync today; if workflow-as-code is how your team works, LangGraph fits and Brahmalabs fights you. - **You want the deepest observability available.** LangSmith's [OTel-native tracing, four language SDKs, and framework-agnostic instrumentation](https://www.langchain.com/langsmith/observability) are worth adopting even without LangGraph. Nothing in Brahmalabs' run stream matches LangSmith's eval tooling. - **You already have LangChain investment.** Team fluency plus the ecosystem LangChain advertises as [1,000+ integrations](https://www.langchain.com/langchain) (code-level packages, not a connector marketplace) make the graduated path from OSS framework to managed deployment the low-friction option. - **You need compliance paper now.** [SOC 2 Type II and HIPAA BAAs](https://changelog.langchain.com/announcements/langsmith-is-now-soc-2-type-ii-compliant) exist today, with [four cloud regions](https://docs.langchain.com/langsmith/regions-faq) to choose from. Brahmalabs cannot match that this quarter. - **You must run in your own VPC and can sign Enterprise.** The [hybrid model](https://www.langchain.com/blog/langgraph-platform-ga) puts the data plane in your infrastructure. Brahmalabs has no equivalent below an Enterprise engagement — and no downloadable option at all. ## When Brahmalabs is the wrong choice Symmetry demands the same honesty in reverse. Do not choose Brahmalabs if: - **Self-hosting is a hard requirement.** There is no downloadable core and no on-prem product. If your data cannot live in a managed EU-resident platform, LangGraph Enterprise's hybrid deployment — or a fully OSS stack like Temporal with your own agent code — is honestly the better fit. - **Procurement needs certifications today.** SOC 2 Type II is in progress, not certified. If the audit letter is a gate, it is not yet in hand. - **You need reference customers.** There are no public case studies or logos. LangGraph can point at [Uber, LinkedIn, and Klarna](https://www.langchain.com/blog/langchain-langgraph-1dot0); Brahmalabs cannot point at anyone publicly yet. - **Your team works workflow-as-code.** No CLI, no git-sync, no Terraform provider. Versioning lives in-platform. - **You need custom roles or SCIM.** Three fixed roles is the whole story; LangSmith Enterprise's [custom roles and SCIM group sync](https://docs.langchain.com/langsmith/rbac) are simply better here. - **RAG is central to your product.** Brahmalabs' knowledge base is file-based cited retrieval, not a vector-RAG pipeline. The LangChain ecosystem's retriever and vector-store tooling is far richer. - **Your topology exceeds the DSL.** If your architecture genuinely needs code-level control flow, wrapping it in a 15-node workflow is the wrong abstraction, full stop. ## Where this leaves you The rubric is team shape, not feature count. **A team of agent engineers with platform capacity** — people fluent in Python or TypeScript, plus someone who can own infrastructure — should take LangGraph. You get exact topology control, the strongest observability in the category, and a stability pledge that has held since 1.0. Budget honestly for what you are also taking on: the governance layer (approval routing, sandboxing, egress control, audit) becomes application code, the serving layer is a commercial ELv2 dependency despite the MIT framework, and self-hosting carries a license key and a beacon egress exception. **A team that answers to a security review before anything ships** — where the questions are "who approved this," "where did the traffic go," and "show me the audit log," and nobody is staffed to build the systems that answer them — gets more from Brahmalabs. The approvals, sandbox, egress capture, and audit trail are properties of the platform on day one. The price is real too: a young vendor, certification still in progress, a workflow model you must fit inside, and no exit to self-hosting. **A team already on LangChain** should stay. The migration cost buys you governance you could build incrementally instead. **A regulated team that needs paper this quarter** goes LangGraph Enterprise, whatever else is true. If the governed-workflow model fits your team's shape, Brahmalabs is in early access at [brahmalabs.io](https://www.brahmalabs.io). ## Questions people actually ask ### Is LangGraph fully open source? Partly. The LangGraph framework library is MIT-licensed and free to use anywhere. The production server runtime, langgraph-api, is licensed under the Elastic License 2.0, and running it in production self-hosted requires a commercial license key tied to a LangChain Enterprise plan, plus network egress to beacon.langchain.com for license verification. Teams wanting a fully open stack must build their own serving layer around the MIT framework. ### Does LangGraph support human-in-the-loop approvals? Yes. LangGraph has first-class interrupt APIs that pause a graph for human review, and LangChain 1.0 ships human-in-the-loop middleware. The approval workflow around the pause — who reviews, consensus rules, escalation, the audit record — is application code you write. Brahmalabs makes the approval itself the primitive: its HITL node configures single-approver or N-of-M consensus, SLA timeouts, and escalation chains without code. ### How does LangGraph pricing compare to Brahmalabs pricing? As of July 2026, LangSmith bills seats ($39/seat/month on Plus), traces ($2.50–$5.00 per 1,000), and deployments ($0.005 per run plus per-minute uptime charges). Brahmalabs bills a flat $299/month Pro tier metered in credits per run and sandbox minutes, not seats, with bring-your-own model keys and no inference markup. LangGraph costs scale with team size and telemetry volume; Brahmalabs costs scale with execution volume. ### Can I self-host LangGraph or Brahmalabs? LangGraph: yes, on the Enterprise plan — hybrid (data plane in your VPC) or fully self-hosted, with a commercial license key and required egress to beacon.langchain.com. The MIT framework alone can run anywhere if you build your own server. Brahmalabs cannot be self-hosted today; it is a managed platform, and on-prem or VPC deployment is an Enterprise engagement, not a downloadable product. ### Is LangGraph SOC 2 compliant? Is Brahmalabs? LangSmith is SOC 2 Type II certified and HIPAA compliant (BAAs on Enterprise plans only), and LangGraph Platform separately achieved SOC 2 Type II in August 2025. Brahmalabs' SOC 2 Type II is in progress, not certified; it offers EU data residency by default. If your procurement process requires certifications today, LangGraph has the paper and Brahmalabs does not yet. ### Which is better for custom multi-agent architectures? LangGraph, without much argument. It is a low-level orchestration framework built for exactly this: single, multi-agent, and hierarchical control flows expressed in Python or TypeScript with checkpoint-level state control. Brahmalabs expresses workflows in a 15-node JSON DSL, which covers approvals, loops, parallelism, and child workflows but cannot express arbitrary topologies the way code can. ## Sources (checked 2026-07-05) - [LangGraph GitHub repository](https://github.com/langchain-ai/langgraph) — accessed 2026-07-05 - [LangGraph product page](https://www.langchain.com/langgraph) — accessed 2026-07-05 - [LangGraph MIT license file](https://github.com/langchain-ai/langgraph/blob/main/LICENSE) — accessed 2026-07-05 - [langgraph-api on PyPI (Elastic-2.0)](https://pypi.org/project/langgraph-api/) — accessed 2026-07-05 - [LangSmith self-hosted Kubernetes docs](https://docs.langchain.com/langsmith/kubernetes) — accessed 2026-07-05 - [LangChain pricing](https://www.langchain.com/pricing) — accessed 2026-07-05 - [LangChain and LangGraph 1.0 announcement](https://www.langchain.com/blog/langchain-langgraph-1dot0) — accessed 2026-07-05 - [LangSmith Deployment and Studio naming changes](https://changelog.langchain.com/announcements/product-naming-changes-langsmith-deployment-and-langsmith-studio) — accessed 2026-07-05 - [LangSmith SOC 2 Type II announcement](https://changelog.langchain.com/announcements/langsmith-is-now-soc-2-type-ii-compliant) — accessed 2026-07-05 - [LangSmith regions FAQ](https://docs.langchain.com/langsmith/regions-faq) — accessed 2026-07-05 - [LangSmith RBAC docs](https://docs.langchain.com/langsmith/rbac) — accessed 2026-07-05 - [LangGraph durable execution docs](https://docs.langchain.com/oss/python/langgraph/durable-execution) — accessed 2026-07-05 - [LangSmith Observability](https://www.langchain.com/langsmith/observability) — accessed 2026-07-05 - [LangSmith Deployment](https://www.langchain.com/langsmith/deployment) — accessed 2026-07-05 - [LangGraph Platform GA announcement](https://www.langchain.com/blog/langgraph-platform-ga) — accessed 2026-07-05 - [LangSmith server MCP endpoint docs](https://docs.langchain.com/langsmith/server-mcp) — accessed 2026-07-05 - [LangChain integrations page](https://www.langchain.com/langchain) — accessed 2026-07-05 - [HN: Why we no longer use LangChain for building our AI agents](https://news.ycombinator.com/item?id=40739982) — accessed 2026-07-05 - [HN: agent framework comparison thread](https://news.ycombinator.com/item?id=41722243) — accessed 2026-07-05 - [rvernica: the LangChain license split](https://rvernica.github.io/2026/03/langchain-license) — accessed 2026-07-05 - [The Hacker News: LangChain/LangGraph CVE disclosure](https://thehackernews.com/2026/03/langchain-langgraph-flaws-expose-files.html) — accessed 2026-07-05 --- Disclosure: this comparison is published by Brahmalabs, one of the two products compared. Competitor facts are dated and linked to primary sources; corrections: hello@brahmalabs.io.