Dynamiq vs Langflow

Langflow is an open-source, Python-based visual builder for agents, RAG apps and MCP servers. Dynamiq is a full governed platform, including voice agents, evals and a deployment team.

By Oleksii Babych, Machine learning engineer Updated September 29, 2026

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In short

Langflow is an open-source, MIT licensed, Python-based low-code builder for agentic and RAG applications that can also publish flows as MCP servers, running as a desktop app, in Docker or on Kubernetes, with guardrails, human-in-the-loop steps and built-in traces. Dynamiq is a full governed platform for chat, voice and workflow agents, with an Apache-2.0 SDK, built-in evals, inbound voice agents and a deployment team. Python developers who want a free, extensible visual builder fit Langflow; teams that need voice agents, built-in evals, enterprise SSO and a deployment team fit Dynamiq.

Side by side

Facts checked September 29, 2026. Sources below.

Dimension	Dynamiq	Langflow
Best for	Enterprises in regulated industries that need chat, voice and workflow agents on one governed stack, deployed inside their own environment.	Python developers who want an open-source, low-code way to build and deploy agents, RAG applications and MCP servers, and to customize any component in Python.
Deployment options	Dynamiq Cloud, or self-hosted on your AWS, Azure, GCP, IBM Cloud, Red Hat OpenShift or any Kubernetes 1.32+ cluster, or on-prem. Fully isolated or air-gapped setups are delivered with our engineers.	Self-hosted: Langflow Desktop for macOS and Windows, a Python package, Docker or Docker Compose, and a headless runtime on Kubernetes with Helm. Its homepage also mentions a free cloud account without naming who runs it.
Self-hosted or on-prem	Yes. Self-hosted on your cloud or on-prem, or Dynamiq Cloud; both run the same engine.	Yes. Langflow runs locally, in a container or on your own Kubernetes cluster, per its documentation.
License and source code	The Python SDK is open source under Apache-2.0 (github.com/dynamiq-ai/dynamiq), and the visual canvas runs on the same engine. The platform around it is not open source.	Open source under the MIT license, per its GitHub repository; IBM lists Langflow as a product of DataStax, an IBM company.
Model choice	29 model providers in the SDK, the most-used models behind one OpenAI-compatible AI Gateway endpoint, plus open models served on vLLM and LoRA fine-tuning.	Many providers through its language model component and bundles, including OpenAI, Anthropic, Google Gemini, IBM watsonx.ai, Azure, Mistral, Groq, NVIDIA and Ollama, plus any OpenAI-compatible provider.
Agent types (chat, voice, workflow)	Chat, inbound voice and workflow agents on one engine. AI Coworker works with its own cloud sandbox computer, in the browser, Slack, Microsoft Teams and Telegram; Voice Agents answer inbound calls; Agent Builder runs workflow agents.	Chat apps, agents and multi-agent flows, with agent-to-agent (A2A) support and human-in-the-loop steps; its voice mode is deprecated as of Langflow 1.10, and no voice agent product is offered.
Developer surface	Agent Builder pairs a visual canvas with the open-source Python SDK on one engine, with Choice and rules nodes, workflows generated from a prompt, and versions with rollback.	A visual editor where any component can be customized in Python, flows exported as JSON, run and workflow APIs with AG-UI streaming, an OpenAI-compatible endpoint, MCP as server and client, a CLI, a TypeScript client and an embeddable chat widget.
Governance (guardrails, approvals, SSO)	Guardrails that detect PII and prompt injection, policy detection, validators and approvals with editable fields on any tool step, on every plan; SSO (OIDC with Okta and Microsoft Entra ID) on Enterprise.	A Guardrails component that checks for PII, credentials, jailbreaks and prompt injection, human-in-the-loop checkpoints, API keys, and external authentication through an OIDC proxy or SSO gateway; full SSO and RBAC enforcement are plugins that open-source Langflow does not include.
Evals and observability	Evals with datasets built from traces, LLM-as-a-judge, predefined and code metrics, and real-time evaluations on live deployments; traces with cost and latency per node, monitoring, and an AI agent that surfaces issues from production traces.	Built-in traces with latency and token usage, OpenTelemetry export (Langflow 1.12), and integrations such as LangSmith, Langfuse and Arize; no built-in evaluation suite is documented.
Compliance	SOC 2 and CASA Tier 2, with a BAA for HIPAA workloads and a DPA under GDPR.	Not publicly documented.
Delivery help (engineers)	Forward-deployed engineers run a bootcamp in your environment, ship the first production agent with your team, then hand over.	Professional services and premier support on request through Langflow's contact page; no named engineering engagement is described.
Pricing model	Free to start ($0), and Enterprise with custom pricing. Available on AWS Marketplace, Microsoft Azure Marketplace and IBM Marketplace.	Free and open source to self-host; no cloud pricing is published.

Best for

DynamiqEnterprises in regulated industries that need chat, voice and workflow agents on one governed stack, deployed inside their own environment.

LangflowPython developers who want an open-source, low-code way to build and deploy agents, RAG applications and MCP servers, and to customize any component in Python.

Deployment options

DynamiqDynamiq Cloud, or self-hosted on your AWS, Azure, GCP, IBM Cloud, Red Hat OpenShift or any Kubernetes 1.32+ cluster, or on-prem. Fully isolated or air-gapped setups are delivered with our engineers.

LangflowSelf-hosted: Langflow Desktop for macOS and Windows, a Python package, Docker or Docker Compose, and a headless runtime on Kubernetes with Helm. Its homepage also mentions a free cloud account without naming who runs it.

Self-hosted or on-prem

DynamiqYes. Self-hosted on your cloud or on-prem, or Dynamiq Cloud; both run the same engine.

LangflowYes. Langflow runs locally, in a container or on your own Kubernetes cluster, per its documentation.

License and source code

DynamiqThe Python SDK is open source under Apache-2.0 (github.com/dynamiq-ai/dynamiq), and the visual canvas runs on the same engine. The platform around it is not open source.

LangflowOpen source under the MIT license, per its GitHub repository; IBM lists Langflow as a product of DataStax, an IBM company.

Model choice

Dynamiq29 model providers in the SDK, the most-used models behind one OpenAI-compatible AI Gateway endpoint, plus open models served on vLLM and LoRA fine-tuning.

LangflowMany providers through its language model component and bundles, including OpenAI, Anthropic, Google Gemini, IBM watsonx.ai, Azure, Mistral, Groq, NVIDIA and Ollama, plus any OpenAI-compatible provider.

Agent types (chat, voice, workflow)

DynamiqChat, inbound voice and workflow agents on one engine. AI Coworker works with its own cloud sandbox computer, in the browser, Slack, Microsoft Teams and Telegram; Voice Agents answer inbound calls; Agent Builder runs workflow agents.

LangflowChat apps, agents and multi-agent flows, with agent-to-agent (A2A) support and human-in-the-loop steps; its voice mode is deprecated as of Langflow 1.10, and no voice agent product is offered.

Developer surface

DynamiqAgent Builder pairs a visual canvas with the open-source Python SDK on one engine, with Choice and rules nodes, workflows generated from a prompt, and versions with rollback.

LangflowA visual editor where any component can be customized in Python, flows exported as JSON, run and workflow APIs with AG-UI streaming, an OpenAI-compatible endpoint, MCP as server and client, a CLI, a TypeScript client and an embeddable chat widget.

Governance (guardrails, approvals, SSO)

DynamiqGuardrails that detect PII and prompt injection, policy detection, validators and approvals with editable fields on any tool step, on every plan; SSO (OIDC with Okta and Microsoft Entra ID) on Enterprise.

LangflowA Guardrails component that checks for PII, credentials, jailbreaks and prompt injection, human-in-the-loop checkpoints, API keys, and external authentication through an OIDC proxy or SSO gateway; full SSO and RBAC enforcement are plugins that open-source Langflow does not include.

Evals and observability

DynamiqEvals with datasets built from traces, LLM-as-a-judge, predefined and code metrics, and real-time evaluations on live deployments; traces with cost and latency per node, monitoring, and an AI agent that surfaces issues from production traces.

LangflowBuilt-in traces with latency and token usage, OpenTelemetry export (Langflow 1.12), and integrations such as LangSmith, Langfuse and Arize; no built-in evaluation suite is documented.

Compliance

DynamiqSOC 2 and CASA Tier 2, with a BAA for HIPAA workloads and a DPA under GDPR.

LangflowNot publicly documented.

Delivery help (engineers)

DynamiqForward-deployed engineers run a bootcamp in your environment, ship the first production agent with your team, then hand over.

LangflowProfessional services and premier support on request through Langflow's contact page; no named engineering engagement is described.

Pricing model

DynamiqFree to start ($0), and Enterprise with custom pricing. Available on AWS Marketplace, Microsoft Azure Marketplace and IBM Marketplace.

LangflowFree and open source to self-host; no cloud pricing is published.

Choose Dynamiq when

One engine for chat, voice and workflow agents, including Voice Agents for inbound calls, not only a builder for flows.
Built-in evals: datasets from traces, LLM-as-a-judge, predefined and code metrics, and real-time evaluations on live deployments.
Guardrails and approvals are part of the platform, and SSO (OIDC with Okta and Microsoft Entra ID) comes with Enterprise, rather than as plugins you source separately.
Forward-deployed engineers ship the first production agent with your team, then hand over. Your team keeps the code and the environment.

Choose Langflow when

A free, MIT licensed builder with a very large community, where any component can be rewritten in Python.
A lightweight start as a desktop app, a Python package or a Docker container, with flows published as MCP servers for tools such as Claude Desktop or Cursor.
Frequent releases, recently adding guardrails, human-in-the-loop, A2A and OpenTelemetry, from DataStax, an IBM company.

Moving from Langflow

Flows built in Langflow export as JSON, which maps directly onto agents and workflows rebuilt in Dynamiq's Agent Builder, on the canvas or in the Python SDK. Custom Python components move to SDK functions or MCP servers, and knowledge bases and vector stores are re-ingested into Dynamiq Knowledge. Prompts, model settings and component configuration carry over as the starting point for each agent.

Questions and answers

Is Langflow open source like Dynamiq's SDK?

Yes. Langflow is open source under the MIT license, and Dynamiq's Python SDK is open source under Apache-2.0, per each project's GitHub repository. The Dynamiq platform around the SDK is not open source.

Does Langflow support voice agents?

No. Langflow's voice mode is deprecated as of version 1.10, per its documentation, and it offers no voice agent product. Dynamiq includes Voice Agents for inbound calls on the same platform.

Can Langflow run fully self-hosted?

Yes. Langflow runs as a desktop app, a Python package, a Docker container or a headless runtime on Kubernetes, per its documentation.

Does Langflow include guardrails, SSO and evals?

Langflow documents a Guardrails component, human-in-the-loop steps and built-in traces; full SSO and RBAC enforcement are plugins that open-source Langflow does not include, and no built-in evaluation suite is documented. Dynamiq includes guardrails, approvals and evals in one platform, with SSO on Enterprise.

Does Dynamiq offer the same flow-building experience as Langflow?

Dynamiq's Agent Builder is a visual canvas on the same engine as its open-source Python SDK, so developers can build visually, in code or both, and it can generate a workflow from a prompt.

Sources

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