WorkLLM
Vs
Langdock
Langdock is a secure, GDPR-compliant enterprise AI platform for internal chat and knowledge access, with workflow automation still in beta. WorkLLM converts your company’s everyday work into AI agents that run it automatically, no technical expertise required.
This page compares how WorkLLM and Langdock work in practice so you can choose the right approach for your company.
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WorkLLM vs Langdock
What each product is best at
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Full Comparison - WorkLLM vs Langdock
Asking AI
| Capability | WorkLLM | Langdock |
|---|---|---|
| Multi-model access and comparison |
Access 200+ models, including GPT, Claude, Gemini, Llama, and Mistral, from one chat interface, and compare up to 4 side by side on the same prompt.
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Switch between GPT-4, Claude, and Mistral, plus BYOK for other models, but not a 200+ model marketplace with confirmed side-by-side comparison.
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| AI answers, web search & research capabilities |
Get cited, up-to-date answers with web search, plus deep research using specialized models, right inside your workspace.
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AI chat and Agents answer questions grounded in internal company knowledge.
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| Chat with documents |
Upload and chat with PDFs, Word files, spreadsheets, and presentations, grounded in your projects and organization memory.
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Documents can be uploaded and connected as knowledge sources for chat and Agents.
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| Remembers your preferences and context |
Remembers your personal preferences, stays consistent within a conversation, and carries context across a shared project.
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A persistent Canvas workspace maintains context for ongoing text and code work.
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| AI answers grounded in your company knowledge |
Every answer can pull from your company's own documents, decisions, and past work automatically, not a generic guess.
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This is Langdock's core strength — Agents access internal knowledge bases through retrieval-augmented generation, genuinely comparable, secure knowledge grounding.
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Getting the work done
| Capability | WorkLLM | Langdock |
|---|---|---|
| Create docs, sheets, slides, flowcharts, wireframes and diagrams |
Docs, sheets, slides, flowcharts, and diagrams, delivered as an actual file or visual you can use immediately.
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Canvas supports complex text and code creation, but isn't built around creating documents, spreadsheets, or slides as standalone files.
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| Create & edit images, video & audio |
Supports generating and editing images directly in chat; for video and audio, it understands but cannot create or edit them.
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Not positioned around media generation.
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| Write & debug code |
Coding models help with generation and debugging inside the chat interface, not directly in your codebase.
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Canvas supports complex code creation as part of its persistent workspace.
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| Write in your brand's voice and tone |
Every generated document, email, or piece of content can pull your brand voice, tone, and business context from organization memory automatically.
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Grounds answers in internal knowledge, but isn't built around a dedicated, curated brand-voice layer applied automatically.
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| Do your work in your work apps |
Ask in chat, and get data from your CRM, send a message in Slack, or update any record in any tool connected natively or through MCP.
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Integrations and tool connections exist, but breadth and native reach aren't specifically confirmed or documented.
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Automating the work
| Capability | WorkLLM | Langdock |
|---|---|---|
| Ready-made agent library for your workflows |
A library of ready-made AI agents for sales, marketing, HR, product, operations, and more, each built to work across multiple apps in one run.
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Agents are knowledge-based, grounded in internal RAG sources for answering questions, not a library of role-specific workflow agents.
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| Automate your workflow in minutes |
Describe your workflow in one prompt, and see the exact steps the AI agent will take before it runs, edit any step, and adjust its instructions or model.
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Workflow automation is explicitly still in beta and not fully available to all users, confirmed directly by independent reviewers.
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| Run your workflows in your work apps |
An AI agent can act directly inside 100+ natively connected tools, or any other tools through MCP, no waiting for a native integration to be built.
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Independent reviews confirm workflows "fall short when it comes to executing... end to end," with humans still completing most operational tasks.
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| Full transparency: review every step, or approve as it runs |
See exactly what an AI agent will do before it runs, edit any step, and decide whether it runs fully on its own or holds for your approval.
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With workflow automation still in beta, a mature, confirmed step-by-step review flow isn't documented.
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| Governance & usage: version history, run reports, and spend tracking |
See exactly who ran each AI agent, how many times, what model it used, and what it cost, with full execution logs down to the individual run.
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Execution logs with 30-day retention exist for workflows, but overall automation governance is limited by the beta status of the workflow product itself.
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| Shared AI agents across your workspace |
Once an AI agent is built, anyone in the workspace can use it, and it always runs from the current version, so updating it once means every future run reflects that change automatically, for everyone.
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No confirmed, mature sharing model for workflow agents specifically, given the beta status of workflow automation.
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Working with your team
| Capability | WorkLLM | Langdock |
|---|---|---|
| Co-prompting, commenting, & tagging colleagues on AI answers |
Multiple people work inside the same AI thread, co-prompting, commenting, and tagging each other, so a conversation becomes shared work.
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No dedicated co-prompting or commenting layer documented.
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| Shared team projects |
A project holds multiple folders, each with its own memory, and anyone with access can start a thread or add context.
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Workspace sharing and centralized management give teams a genuinely comparable shared environment.
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Security and control
| Capability | WorkLLM | Langdock |
|---|---|---|
| Tenant isolation and deployment options |
Enterprise customers can run on dedicated, physically isolated infrastructure, a private VPC with your own servers and storage.
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Single-tenant deployments give each customer a fully isolated instance — genuinely comparable, EU-hosted infrastructure.
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| Encryption and single sign-on |
Data encrypted at rest and in transit, with SAML-based SSO for enterprise deployments.
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SSO, SCIM, and SAML are all confirmed on the standard Business plan.
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| Access control, audit logs & guardrails, on every plan |
Role-based permissions, audit logs, and input/output guardrails included by default, not gated behind a higher tier.
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Role-based access, admin controls, and usage limits for expensive models are genuinely confirmed and well-regarded by reviewers.
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| Your data is never trained on or retained |
Customer data isn't used for model training, with zero data retention at the LLM layer.
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Fully GDPR-compliant with zero data retention, ISO 27001 certified, and listed on Germany's official government AI marketplace — genuinely strong, EU-specific compliance.
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| Admin dashboard and governance |
A central admin dashboard with usage analytics per user and per agent.
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Strong, well-regarded admin controls are confirmed, including usage limits and internal documentation linking, though pricing complexity across seats, workflows, and API usage makes overall cost governance harder to track.
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Disclaimer: Information about Langdock is based on publicly available documentation and product information as of September 2026. Features, pricing, and capabilities may change over time.
WorkLLM vs Langdock
When To Choose Which
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