🚀 Langflow 1.12 is live Langflow 1.12 introduces OpenTelemetry support, giving teams better visibility into Langflow service health and flow execution as deployments grow in scale and complexity. This release adds support for emitting traces, metrics, and logs through OTLP, making it easier to connect Langflow to OpenTelemetry-compatible observability backends such as New Relic and Instana. What’s new in this release: 🔹 OpenTelemetry support Langflow can now emit telemetry signals that help teams understand how the service is running in production environments. With OpenTelemetry support, teams can monitor Langflow using the observability tools they already rely on, without being locked into a single vendor-specific backend. Langflow can export traces, metrics, and logs through standard OpenTelemetry environment variables. Example: OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 OTEL_SERVICE_NAME=langflow This gives deployment teams a clearer way to observe service health, monitor latency and errors, investigate slow requests, and connect logs with execution traces in production environments. Why it matters: - Makes Langflow easier to monitor in production - Connects Langflow to existing observability stacks - Supports traces, metrics, and logs through standard OTLP - Helps teams operate Langflow more confidently at scale - Improves visibility as workflows and deployments become more complex Also in 1.12: 🔹 Langflow Extensions update Langflow 1.12 completes the transition to Extension bundles, making the default installation lighter and more modular. A curated set of providers is included by default, while additional providers can be installed separately when your flows need them. Saved flows continue to open normally, and existing component class names remain compatible. Before upgrading, review the release notes for packaging and installation changes in this release. 👉 Explore more about Langflow 1.12: langflow.org/blog/langflow-1…

Not every request needs your most powerful model. A simple task and a complex reasoning task shouldn’t automatically take the same path. With Smart Router in Langflow, you can route each request to the model that fits, based on the logic you define. Simple request → faster, lower-cost model. Complex request → more capable model. Same flow. Different models. Smarter routing. Build once. Let Smart Router decide where each request should go. #Langflow #AI #LLM #AIAgents

Give your coding agent access to your API standards Your coding agent can read the code in its workspace. But your company’s API standards may live somewhere else. In this demo, a Langflow project exposes a flow with fictional Acme API standards as an MCP tool. The agent retrieves those rules before generating list_invoices(). The flow provides requirements for: - OAuth2 Bearer authentication - The Authorization and X-Acme-Workspace headers - Cursor pagination, with a maximum of 50 records per page The generated function is tested against a local mock API: 120 invoices retrieved across 3 pages. Why it matters: A Langflow flow can make company-specific guidance available to an MCP-compatible coding agent when it needs it, instead of assuming those rules are already in the project. Demo note: Acme is fictional, and the test uses a local mock API. In this setup, the API key protects access to the Langflow MCP server; the Bearer token in the sample code belongs to the fictional Acme API. Learn how to expose Langflow flows as MCP tools: docs.langflow.org/mcp-server

New models keep shipping. Your agent shouldn’t have to start over every time. In Langflow, switching models is one field. The rest of your flow stays in place, your MCP tools, human-in-the-loop approvals, and everything around the model. Then ship it as an API or MCP server. Swap the model. Keep the flow.

🚀 Langflow Desktop 1.12 is now available Langflow 1.12 brings OpenTelemetry support for better visibility into service health and flow execution, along with a lighter, more modular installation through Langflow Extensions. This release includes: - OpenTelemetry support for traces, metrics, and logs through OTLP Support for OpenTelemetry-compatible backends, including New Relic and Instana - Langflow Extensions update with a smaller default installation and opt-in provider bundles If you are already using Langflow Desktop, upgrade in the application to version 1.12. To install Langflow Desktop for the first time, and to learn more about the new 1.12 features, visit: langflow.org/blog/langflow-1…

Langflow Extensions - Langflow 1.12 A lighter, more modular way to install and extend Langflow. As Langflow grows, so does the number of providers, tools, vector stores, and integrations available to builders. But not every deployment needs every integration installed by default. With the launch of Langflow 1.12, Langflow completes the transition to Extension bundles. Provider integrations are now packaged as installable extensions, making the default Langflow installation smaller while allowing teams to add extra providers only when their flows need them. The default install now includes a curated provider set. Additional bundles can be installed separately for specialized workflows, integrations, and deployment needs. This gives teams more control over what is included in their Langflow environment, helping reduce unnecessary dependencies while keeping the platform flexible. Saved flows continue to open normally, with existing component class names remaining compatible. If a flow requires a provider that is not installed, teams can install the corresponding bundle and continue working without changing the flow structure. Langflow Extensions also create a clearer path for developers to package, validate, and distribute their own components as extensions. Why it matters: - Keeps the default installation lighter - Makes dependency management cleaner - Lets teams install only the providers they need - Supports custom and community-built extensions - Helps Langflow scale as more integrations are added - Makes Langflow easier to maintain across different environments 👉 Explore Langflow Extensions in Langflow 1.12: langflow.org/blog/langflow-1…

We are taking 25 developers into early access for the next version of Langflow. Building an AI agent is the easy part now. Getting permission to run it inside a real organisation is not. Access control, credential handling, deployment, and knowing what your agents actually did are the things that decide whether an agent ever leaves the prototype stage. That is what we are building, and we want 25 people building it with us. Langflow Champions get pre release builds, a private channel with the maintainers, monthly office hours with the core team, and genuine influence over what ships. Find a bug and we fix it. Tell us a feature is wrong and we change it. Apply Now: ibm.biz/champions_x

OpenTelemetry support - Langflow 1.12 Observe Langflow like a production service. Langflow 1.12 adds OpenTelemetry support to help teams monitor service health, investigate latency and errors, and understand how flows execute in production. With standard OTLP, Langflow can emit traces, metrics, and logs to OpenTelemetry-compatible backends, including New Relic and Instana. This is application observability for Langflow itself, not prompt-level LLM tracing. Prompts, completions, tool arguments, and flow payloads are withheld from the default telemetry path. Watch the video and explore OpenTelemetry support in Langflow 1.12: langflow.org/blog/langflow-1…

When one vector is not enough for RAG Single-vector retrieval works well for many workflows. But when documents get longer, denser, or visually complex, compressing everything into one embedding can lose important context. A long technical report, a PDF full of tables, a slide deck, or a scanned document often contains meaning across tokens, layout, charts, and visual structure. That is where multi-vector retrieval changes the game. In our latest blog, we break down how Langflow 1.11 introduces first-class multi-vector retrieval with the new lfx-nextplaid extension bundle. With NextPlaid, Langflow flows can now use ColBERT-style late interaction and ColPali-style visual document retrieval without custom glue code. What this enables: - Token-level retrieval instead of relying on a single pooled document vector - Better search quality for long and information-dense documents - Visual retrieval for PDFs, tables, charts, slide decks, and scanned pages - PLAID indexing for faster, more memory-efficient multi-vector retrieval - New NextPlaid and vLLM Multivector Embeddings components inside Langflow The benchmark results are significant. On long technical reports, text multi-vector retrieval via PLAID reached 94.7 Recall@10, compared to 73.7 with single-vector retrieval. For image retrieval, multi-vector retrieval reached 89.7, compared to 22.6 with a single-vector baseline. If you build RAG workflows over complex documents, technical reports, enterprise knowledge, or visual PDFs, this deep dive is worth reading. 👉 Read the full blog: langflow.org/blog/blog-nextp…

🚀 Langflow Desktop 1.11 is now available Langflow 1.11 brings new capabilities for building agentic workflows with more control, interoperability, and API-driven execution. This release includes: - Human-in-the-Loop for review and approval gates - A2A protocol support for agent-to-agent interoperability - AG-UI-compatible streaming through the Workflow API If you are already using Langflow Desktop, upgrade in the app to version 1.11. To install Langflow Desktop for the first time, visit: langflow.org/desktop

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Working with Tabular Data in Langflow using Table Operations The Table Operations component applies dataframe-style operations to Table objects inside a Langflow workflow. It can be used to filter rows, sort values, select columns, merge tables, rename fields, and remove duplicates. What this component does Table Operations processes structured tabular data generated by other components in the workflow. Common use cases include: • Filtering rows based on conditions • Sorting records before passing them to another stage • Selecting only relevant columns • Merging tables from multiple sources • Removing duplicated rows Available Operations • Add Column • Concatenate • Drop Column • Filter • Head / Tail • Merge • Rename Column • Replace Value • Select Columns • Sort • Drop Duplicates Example Workflow API Request → Smart Transform → Type Convert → Table Operations In this workflow: • API Request retrieves JSON data • Smart Transform extracts or restructures relevant fields • Type Convert converts the JSON into a Table • Table Operations applies dataframe-style transformations to the resulting table How It Works The component receives a Table object as input and applies a selected operation to generate a modified Table. For multi-step transformations, multiple Table Operations components can be chained together in the workflow. How to get started Add the Table Operations component after any component that produces Table data. If the workflow still returns JSON, components such as Type Convert can convert the data into a Table before processing. Learn more about Langflow: langflow.org/?utm_source=x&u…

Langflow Use Case: PR Documentation Generator Keep technical documentation aligned with code changes automatically. This Langflow workflow analyzes GitHub PR diffs, identifies documentation impact, and generates Markdown updates that match your existing docs structure and writing standards. It can detect API changes, new configuration options, deprecated behavior, and missing documentation gaps directly from PR context. Use it to reduce documentation drift, generate consistent release notes, and streamline docs reviews across engineering teams without adding manual overhead to the shipping process. 🔗 Template: langflow.org/templates/use-l…

Use Google Docs inside Langflow agents With Composio components in Langflow, you can connect Google Docs directly into a Langflow workflow and let agents create, search, and update documents automatically. Typical operations include: - Creating documents from agent outputs - Inserting structured text into existing docs - Searching documents by context or title - Updating documentation automatically - Generating reports, notes, or summaries from workflows All of this can be composed visually alongside LLMs, APIs, databases, and other tools inside the same flow. Example: automated documentation workflow A common pattern is using Google Docs as a dynamic output layer for AI workflows: 1. Process incoming data or conversations 2. Generate structured summaries or reports with an LLM 3. Create or update a Google Doc automatically 4. Keep documentation synchronized as new information arrives This makes Google Docs useful for workflows involving meeting notes, internal documentation, AI-generated reports, research summaries, or collaborative knowledge sharing. 👉 Learn more about Langflow: langflow.org/?utm_source=x&u…

Langflow Assistant: Video Building custom components usually requires writing boilerplate code, configuring inputs and outputs manually, and validating component structure before it can be used inside a workflow. With the launch of Langflow 1.9, Langflow Assistant introduces an in-product AI workflow assistant capable of generating reusable components directly from prompts: - Natural language → Python component generation - Automatic creation of inputs, outputs, metadata, and component structure - Real-time code generation using the Langflow component SDK - Direct validation and insertion into the canvas - Tool Mode support for agent-compatible components Example: In this demo, the assistant generates a "NewsFinderComponent" from a single prompt requesting a tool that searches news articles from a user query. The generated component is validated and added directly to the flow canvas, where it can immediately be connected to agents and other workflow nodes. Why it matters: - Reduces manual component boilerplate - Speeds up workflow prototyping and iteration - Makes custom tool creation accessible directly inside the Langflow interface 👉 Explore Langflow Assistant in Langflow 1.9: langflow.org/blog/langflow-1…

Multi-Agent Travel Planning Workflows in Langflow This template implements a multi-agent workflow where different agents handle separate parts of a travel planning task. Instead of relying on a single agent to search, validate, calculate, and generate responses, the workflow distributes responsibilities across multiple agents and tools. The flow combines: • Web search for travel-related information • URL content extraction • Budget and arithmetic calculations • Final itinerary generation Each agent operates on a narrower scope and passes its output to the next stage. Workflow Structure One agent performs search and information gathering using external tools. Another agent processes and structures the retrieved content. A calculator tool handles numerical operations such as budget estimation. The final agent combines all previous outputs into a consolidated travel plan. This architecture can be adapted for: • Travel itinerary generation • Multi-step research workflows • Financial or cost estimation systems • Task-specific assistant orchestration • Agent pipelines that require tool usage across multiple stages How to get started This template is already available inside Langflow. Click New Flow, search for Travel Planning Agents, and run the template in the Playground. Learn more about Langflow: langflow.org/?utm_source=x&u…

Langflow Use Case: Voice of Customer Report Generator Turn unstructured user feedback into structured, actionable Voice of Customer reports. This Langflow workflow collects comments from YouTube and Reddit, normalizes the data, analyzes sentiment and recurring themes, and generates a structured report with key insights, representative quotes, and prioritized recommendations. Use it to identify real customer pain points, track sentiment shifts, and turn public feedback into product, support, and marketing decisions without manual research. 🔗 Template: langflow.org/templates/use-l…

Flow DevOps Toolkit - Langflow 1.9 Feature The Flow DevOps Toolkit (LFX SDK) provides a CLI-based workflow for versioning, testing, and deploying Langflow flows. Instead of exporting and importing flow JSON through the UI, flows are managed as code within a project structure. Using the CLI, you can: - pull flows from a Langflow instance into local files - track changes through version control - validate flows before deployment - push updates back to the server - generate dependencies (requirements.txt) - serve flows as API endpoints A typical workflow: lfx pull lfx status lfx validate lfx push Flows are stored as JSON and can be versioned and tested alongside application code. The toolkit supports multiple environments (local, staging, production) using environment variables for API keys and instance configuration. Flows move across environments using the same deployment patterns as application services. What the video shows In the video, the workflow is executed in a local environment using the CLI. A project is initialized, generating a structured directory with folders for flows, configuration, and test scaffolding. A flow is then pulled from a Langflow instance into the flows/ directory, where it is stored as a JSON file. An API key is configured as an environment variable, enabling authenticated communication with the Langflow server. The flow is then validated locally, ensuring that its structure and dependencies are correct before execution. After validation, the flow is served locally, exposing it as a runnable endpoint. Finally, the same flow is opened in the Langflow interface, confirming that the version managed via CLI is synchronized with the runtime. Available in Langflow. langflow.org/?utm_source=x&u…

Block Custom Components - Langflow 1.9 Feature Langflow allows custom components to execute Python code inside the runtime. The Block Custom Components setting disables this capability at the server level. When the environment variable is set: LANGFLOW_ALLOW_CUSTOM_COMPONENTS=false the system prevents: - creating new custom components - editing component code in the visual editor This restricts arbitrary code execution within the Langflow environment. When unset or set to true, custom components remain enabled and behave as before. In controlled environments, this can be combined with an allow-list by specifying trusted component paths, ensuring that only predefined components are available while blocking others. This feature is useful when running Langflow in shared or production environments where executing untrusted or generated code is not allowed. Available in Langflow. langflow.org/?utm_source=x&u…