Altilia vs Langflow (2026)

Pre-built connectors and an API-first architecture let AI assistants and robots be embedded across the customer's tech stack, from legacy systems to modern cloud applications, and agents take action on data. No individual connector, integration catalog or connector count is named for any application class. The platform is distributed through the Microsoft commercial marketplace. Documents and data are ingested from any source or format, including PDFs, emails, handwritten notes, scanned images and legacy databases.

Sourcealtilia.ai home, platform, why-altilia and modules pagesread 2026-08-31

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

Agent workflows are designed with visual scripting, automating and sequencing actions and connecting data sources with fine-tuned models, chains and processing functions, alongside skills, which are process orchestration tasks built in the low-code interface.

The platform provides governable agentic orchestration and ontology-driven workflows that coordinate agents across departments, with agents shipping as assistants or autonomous robots within the same workflow model. Independent analysts describe the agents-and-skills combination as providing robotic process automation capabilities. No branching, looping, conditional or parallel control-flow construct is named, and no failure or retry behavior is described.

Sourcealtilia.ai platform, agents and home pages, Intellyx analyst briefread 2026-10-05

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

Agents are triggered by document and data ingestion, with the platform ingesting and structuring data from any source or format, including legacy databases, emails, scanned images and forms, and an API-first architecture lets agents embedded in customer applications be invoked programmatically. No scheduled, cron or recurrence capability, inbound webhook or external event subscription is named, and no chat, email, messaging or embedded conversational channel reaches an agent beyond the in-platform Insights assistant.

Sourcealtilia.ai platform, home and modules pagesread 2026-08-31

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

The Knowledge Graph is the platform's core component, aggregating and organizing all company information and unifying structured, unstructured and physical document sources into a single coherent database. A hybrid neuro-symbolic architecture combines large and small language models with the graph and is described as GraphRAG with traversal reasoning.

Ingestion covers any source or format, including long complex documents, standardized forms, scanned images, handwritten notes, emails and legacy databases, transformed into semantic objects. Users interrogate the knowledge base in natural language and receive fact-based answers with source attribution, and RAG techniques retrieve and fine-tune LLMs and SLMs against enterprise knowledge. Accuracy above ninety-five percent on domain-specific tasks is vendor-reported.

Sourcealtilia.ai platform, home and why-altilia pagesread 2026-08-31

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

There is no memory store, session state or cross-run context. Continuous learning from human feedback, which Altilia describes as building training datasets, feeds the training loop.

Sourcealtilia.ai/platformread 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

In a review and validation stage, people review and validate data classifications, extractions and AI-generated answers, and the feedback of human experts is integrated to enhance accuracy and control at key stages, with reviewed corrections feeding continuous learning. Agents ship in two forms, assistants that support human decision-making and robots that automate processes autonomously, so the autonomy level is a design-time choice. No confidence threshold routing uncertain items to review, action allowlist, or scope or spend limit is described.

Sourcealtilia.ai platform, agents and home pagesread 2026-10-05

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

Identity and Access provides secure access with RBAC, SSO and multi-tenancy, with guardrails on model and agent behavior, and the site footer shows ISO/IEC 27001, ISO/IEC 27017 and ISO/IEC 27018, plus ISO 9001, as certified. The security page describes the management system as aligned with those standards, which is consistent with holding the certificates, but no certificate or registrar is named.

Sourcealtilia.ai/platformread 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

Auditability gives an end-to-end audit trail of every action and decision, and Observability gives real-time logging, monitoring and alerting, built into every platform module, alongside workflow and agent orchestration run step by step with monitoring and human validation.

Sourcealtilia.ai/platformread 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

SaaS, private cloud and on-premises deployment are offered, which Altilia calls deployment sovereignty, with domain-tuned small and large language models served locally so inference runs inside the deployment rather than being sent to a hosted provider.

Data is kept within the customer's secure infrastructure, and customers retain ownership of data, models and assets. The company positions itself as a sovereign European platform competing with US hyperscalers.

No region selection within the SaaS offering, installation requirement, or statement of which components run customer-side in the on-premises mode is described.

Sourcealtilia.ai why-altilia, home and platform pagesread 2026-08-31

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

Customers can deploy ready-to-use agents for common use cases, including extracting data from invoices, orders and transport documents, or use the integrated development environment to create bespoke agents. A broader specialized set covers document classification by semantic context, data extraction from unstructured sources, semantic search and question answering with source attribution, summarization into reports, and generation of emails and operational content, and blueprints for common use cases are referenced.

Altilia lists 45 named agents across eight industries and a cross industry group, among them Credit Memo Drafting, Customer Onboarding Data Capture, Secured NPL Data Acquisition, Insurance Claims Management, the DORA Compliance Agent, Hospital Order Management and the Enterprise Search Agent, each with a line on the job it does. Customers reach them through a demo request rather than a self serve deploy button.

Sourcealtilia.ai/agents, platform and modules pagesread 2026-10-05

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

The platform is model agnostic and runs any commercial or open-source model, with domain-tuned small and large language models served locally and model selection driven by cost and data type, which Altilia frames as customers owning their models.

RAG techniques retrieve and fine-tune LLMs and SLMs against enterprise knowledge, and AI agents trained on customer data remain the customer's intellectual property. Altilia positions model portability as the basis of its sovereign European alternative to hyperscaler platforms. No named provider list, model picker interface or automatic runtime routing between models is described.

Sourcealtilia.ai platform, why-altilia and home pagesread 2026-08-31

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

AI agents connect directly into the customer's operations and applications through an API-first architecture, embedded across the tech stack from legacy systems to modern cloud applications. Simple SDKs are one of three first-class routes for building agents, alongside natural language and visual scripting. No API reference, endpoint list, authentication documentation, SDK package, developer portal or MCP server is published.

Sourcealtilia.ai home, platform and why-altilia pagesread 2026-08-31

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

Monitoring and tracking of model performance spot inefficiencies, with continuous learning and improvement over time, alongside human review and validation of data classifications, extractions and AI-generated answers whose corrections feed the training loop. There is no held-out test set, recorded expected outputs, scored run, evaluation harness or comparison between agent versions. Accuracy above ninety-five percent on domain-specific tasks is vendor-reported.

Sourcealtilia.ai platform, why-altilia and home pagesread 2026-08-31

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

Altilia calls its autonomous agents robots, as distinct from assistants, and independent analysts describe the agents-and-skills combination as providing robotic process automation capabilities.

The mechanism is agents classifying, extracting, summarizing and generating against documents and a knowledge graph, reaching systems through an API-first architecture and pre-built connectors, not through interface operation. Computer vision and document analysis and recognition read scanned images and handwritten notes as part of document intake. There is no browser control, navigation, form filling, screen interaction, visual grounding or desktop automation.

Sourcealtilia.ai platform, agents and home pages, Intellyx analyst briefread 2026-10-05

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