Langflow vs Sim (2026)

Components are organized as Core components grouped by purpose and Bundles grouped by service provider, covering language models, embeddings, vector stores, document loaders, data sources and tools.

An API Request component calls arbitrary endpoints, custom Python components extend the catalog arbitrarily, and Tool Mode on any component exposes it to an Agent component as a callable tool. As an MCP client Langflow connects to external MCP servers and uses their tools.

Provider bundles include Apify actors and search providers, and vector database and model provider bundles span the major commercial and local options.

Sourcedocs.langflow.org components overview, bundles, MCP client and API request pagesread 2026-08-31

More than 1,000 native integrations span communication (Gmail, Slack, Teams, Telegram, WhatsApp), productivity (Notion, Google Workspace, Airtable), development (GitHub, Jira, Linear), CRM and revenue (Salesforce, HubSpot, Attio, Gong), data and search (PostgreSQL, BigQuery, Pinecone, Perplexity, Exa) and security and IT (CrowdStrike, Okta, AWS IAM), exposed to agents as tools inside workflows. Anything not built in connects through MCP.

Sourcedocs.sim.ai/introductionread 2026-09-29

Flows are node graphs of typed components with loop and branching behavior, parsing and type conversion between incompatible ports, and dynamic ports that open as templates reference new variables. Components can be grouped into a single reusable component and saved to the component menu, and freezing a component pins it and all upstream components so their output is reused rather than recomputed. Agents can use components and flows as tools, so one agent can invoke another flow, and individual components can be run in isolation to test dependencies.

Sourcedocs.langflow.org agents, components overview and flows pagesread 2026-08-31

A visual workflow builder offers built in control blocks including Router, Condition, Loop, Parallel and a Workflow block for composing sub workflows. Agents reason inside Agent blocks and act through tools, and Mothership, the natural language control plane, scaffolds and edits workflows, tables and knowledge bases across the workspace, with API and SDK build paths running alongside the canvas.

Sourcedocs.sim.ai/introductionread 2026-09-29

A Webhook component gives each flow its own POST /v1/webhook endpoint, authenticated by API key by default, so an event in an external application starts a run, and flows can also run through the API. Channels include the REST API, an embeddable langflow-chat web component, the Playground and flows published as MCP tools. Langflow has no built-in scheduler or cron trigger.

Sourcedocs.langflow.org llms.txt index and sitemap (webhook and component-webhook pages)read 2026-09-29

Six core triggers, Start, Schedule, Webhook, RSS Feed, Table and Sim Workspace Events, start workflows, and deployments expose any workflow to the outside world as an API, a chat interface or an MCP server.

Sourcedocs.sim.ai/introductionread 2026-09-29

Retrieval is assembled from component classes, document loaders, text splitters such as the Recursive Character Text Splitter with configurable chunk size and overlap, embedding model components, and vector store components supplied as provider bundles, so the customer chooses and owns the store. Retrieval components feed context into prompt templates through typed ports, with a Type Convert component bridging incompatible types. A file management system stores the names, paths, sizes and storage providers of uploaded files, and chat memory can itself be backed by an external vector store.

Sourcedocs.langflow.org components overview, vector store bundles, memory management and file management pagesread 2026-08-31

Knowledge bases are a workspace resource of their own, with connectors, tag based filtering, chunking strategies and retrieval debugging, and agents retrieve relevant passages from customer documents to ground answers.

Sourcedocs.sim.ai/tools/knowledgeread 2026-09-29

Memory bases store an agent's long-term chat history in a vector store for semantic retrieval of past conversations across sessions, filling themselves from each attached flow's runs through auto-capture and a configurable ingestion threshold, one memory base per flow and several per flow allowed, on Chroma, Chroma Cloud, OpenSearch or pgvector. Chat conversations are also stored by session ID, with the Agent component's chat memory on by default and a Message History component with store and retrieve modes, and message logs are editable and deletable.

Sourcedocs.langflow.org memory-bases and session-id pagesread 2026-09-29

Memory on the Agent block needs a conversation ID to persist across runs, and the agent can be set to one of four memory modes. None sends independent requests, Conversation holds the full history keyed by a conversation ID, Sliding Window by messages keeps the N most recent, and Sliding Window by tokens keeps messages up to a token limit.

The Mem0 integration lets agents keep persistent memory across workflow executions, recalling past conversations, remembering user preferences and building on previous interactions, with add, semantic search and retrieve operations, and Zep ships as a second external memory option. Tables hold durable structured rows that workflows read and write, and Variables carry in-run state.

Sourcedocs.sim.ai agent block, Mem0 tool and workflow documentationread 2026-08-31

Human-in-the-Loop pauses a flow, creates a checkpoint and waits for a human decision, after which the run resumes from the checkpoint along the selected branch without re-executing completed steps. It works two ways. The Human Input component pauses the flow where it is placed and creates one output branch per configured user action such as Approve or Reject, and per-tool approval on the Agent component, set by enabling Requires approval on an individual tool, pauses the run only when the agent attempts to call that tool. Human-in-the-Loop arrived in the Langflow 1.11 release.

Sourcedocs.langflow.org human-in-the-loop, human-input component and agent tools pagesread 2026-08-31

Human in the Loop and Guardrails ship as core workflow blocks alongside Condition and Wait, and enterprise access control enforces which blocks and model providers each permission group can use, both in the UI and at execution time.

Sourcedocs.sim.ai/introductionread 2026-09-29

Langflow supports API key and JWT authentication, external authentication through an upstream OIDC proxy or corporate SSO gateway with JWKS validation and automatic user provisioning, and a pluggable authorization layer with viewer, developer and admin roles, shares and an authorization audit log. The open-source edition registers a pass-through authorization service and ships no enforcement plugin, so per-resource RBAC is not enforced without a third-party plugin. No security attestation applies to the self-hosted open-source project, and access enforcement is left to the operator.

Sourcedocs.langflow.org authorization and external-authentication pagesread 2026-09-29

Sim's Trust Center at trust.sim.ai lists SOC 2 Type II, ISO 27001 and GDPR, and SOC 2 compliance is among the Enterprise features. Access controls include SAML 2.0 and OIDC SSO across Okta, Entra ID, Google Workspace and ADFS, workspace roles and permission groups enforced at execution time, verified domains, session policies with maximum lifetime, idle timeout and sign out of all members, dedicated outbound IPs, secrets management and configurable data retention.

Sourcesim.ai homepage and pricing page, docs.sim.ai enterprise Security pageread 2026-09-29

Each component has an Inspect control showing its output and logs for the most recent run, and a Last Run indicator confirms execution, so a builder can see what every node in the graph produced. Monitor endpoints expose stored messages at GET /v1/monitor/messages, filterable by session ID. The Playground shows message logs per session with full history, and log level and file settings are configurable through environment variables. Langflow does not describe tracing integrations with LangSmith or Langfuse.

Sourcedocs.langflow.org components overview, API reference and playground pagesread 2026-08-31

Logs record every run block by block as a workspace resource of their own, with logging and alerts, cost visibility for each run, enterprise audit logs tracking configuration and security relevant actions, and data drains that stream workflow logs, audit logs and Mothership data to a customer owned S3 bucket or HTTPS webhook on a schedule.

Sourcedocs.sim.ai/introductionread 2026-09-29

Langflow is an MIT-licensed open-source Python application that the customer self-hosts, deployable in containers that serve flows over a network, installable as a Python package, or run as Langflow Desktop, a downloadable application for macOS and Windows that bundles dependencies. LFX builds Langflow as a dependency within another application. Storage is a database the operator controls, LANGFLOW_USE_NOOP_DATABASE disables persistence entirely for testing, and chat memory can be directed to external databases the customer already runs.

Sourcedocs.langflow.org deployment overview, LFX overview, memory management pages and langflow.org desktopread 2026-08-31

Sim runs cloud hosted at sim.ai or self hosted through Docker Compose and Kubernetes, including a fully offline setup with local models through Ollama, and self hosted deployments unlock the entire enterprise feature set through environment configuration, so the customer can control hosting entirely.

Sourcedocs.sim.ai/introductionread 2026-09-29

Several pre-built templates come ready to use or customize, and many tasks begin by creating a flow from a named template such as Basic Prompting or the Memory Chatbot starter example. New workspaces open with a Starter Project folder by default. Grouped components can be saved into the Core components menu as reusable custom components, and flows are importable and exportable as JSON so complete assets can be shared and adopted.

Sourcedocs.langflow.org get started, memory, web-search and import and export pagesread 2026-08-31

Agent skills provide reusable packaged capabilities and Mothership scaffolds complete systems from a natural language description, and Sim does not describe a template gallery, prebuilt agent library or marketplace.

Sourcedocs.sim.ai/introductionread 2026-09-29

A Language Model component selects provider and model per node, so users add an OpenAI key or pick a different provider and model. Model providers come as Bundles grouped by provider, covering the major commercial options including OpenAI, Anthropic, Google, Cohere and Hugging Face, alongside local models through Ollama. The component can emit either a model response or a Language Model output that powers a downstream component's reasoning, so one flow can drive several models. Custom Python components allow any additional provider to be added.

Sourcedocs.langflow.org components overview, model components and web-search pagesread 2026-08-31

More than fifteen providers are supported, including OpenAI, Anthropic, Google Gemini, Groq, Cerebras, DeepSeek, Mistral, xAI and OpenRouter, with bring your own key at base provider pricing and no markup, and local models run through Ollama or VLLM at no API cost. Enterprise permission groups can restrict which providers are allowed.

Sourcedocs.sim.ai/blocks/agentread 2026-09-29

Langflow exposes flows as MCP servers that external MCP clients can use, and it also acts as an MCP client connecting to external MCP servers. A REST API runs flows, lists components at /api/v1/all, triggers runs and returns monitor data, authenticated by API key. Flows import and export as JSON, custom components are written in Python and can be shared, an embeddable langflow-chat web component brings flows into a site with host URL and API key props, and LFX builds Langflow as a dependency.

Sourcedocs.langflow.org MCP, API reference, import and export, custom components and publish pagesread 2026-08-31

Sim is open source under Apache 2.0 with the full source on GitHub, a public API and SDKs, a Function block for custom JavaScript, custom tools, and MCP in both directions, so agents consume MCP tools and any workflow deploys as an MCP server, with enterprise custom blocks on top.

Sourcedocs.sim.ai/introductionread 2026-09-29

For testing and debugging, Langflow offers the Playground for interactive flow testing with custom session IDs, running a single component in isolation via build_vertex without executing upstream dependencies, Inspect for per-component output and logs, Freeze to keep a component and its upstream from re-running while preserving last output, message logs that can be edited or deleted to change later chatbot behavior, and automatic backup flows before component updates that may contain breaking changes. Langflow offers no evaluation, scoring, regression or benchmarking capability.

Sourcedocs.langflow.org playground, components overview and memory pagesread 2026-08-31

The Evaluator block is a core block that uses AI to score and assess content quality against metrics the customer defines, each with a name, description and numeric range, with accuracy, clarity and relevance on one to five scales as worked examples.

The block generates a JSON Schema response format from those metrics and enforces strict mode so the model returns only numeric scores, and it supports quality control and A/B testing, including comparing several AI generated responses and assessing Agent block outputs to create feedback loops. A published pattern chains Agent to Evaluator to Condition against a threshold.

Guardrails, knowledge base retrieval debugging, block-by-block run traces and per-run cost visibility help with debugging. Sim does not describe keeping test sets, stored expected outputs or a regression history across versions.

Sourcedocs.sim.ai evaluator block and core blocks documentationread 2026-08-31

No browser, desktop or computer-use component ships with Langflow. The closest capability is the Web Search component, which combines web, news and RSS modes and uses web scraping subject to rate limits, alongside an API Request component for arbitrary HTTP calls. Browser capability reaches Langflow only through third parties, an Apify bundle that calls a hosted RAG Web Browser actor, and community-authored custom components wrapping external browser-automation libraries.

Sourcedocs.langflow.org web-search and components referenceread 2026-08-31

Browser Use and Stagehand ship as workflow tools in the integrations catalog alongside Apify and Bright Data, letting workflows drive real browsers against interfaces that expose no programmatic API, with Stagehand performing natural language navigation and action on live pages. These are third party browser engines wired in as catalog integrations, not a hosted or local browser or desktop session Sim runs itself.

Sourcedocs.sim.ai tools catalogread 2026-08-31