WorkLLM
Vs
Langdock
Langdock is an enterprise AI platform that provides team chat, AI agents, visual workflows, and a developer layer for building custom AI automations. WorkLLM is an AI workspace for companies that want everyday work, including prospecting, outreach, product development, research, hiring, content creation, client delivery, and more, to be run by AI agents without any technical expertise.
This page compares how WorkLLM and Langdock work in practice so you can choose the right product 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
AI Chat
| Capability | WorkLLM | Langdock |
|---|---|---|
| Multi‑LLM chat |
Access 200+ models including GPT, Claude, Gemini, Llama, Mistral, and more from a single chat interface, without separate subscriptions.
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Supports multiple model providers including OpenAI, Anthropic, and Mistral through a unified API, but not 200+ models, with the option to bring your own model provider keys.
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| Side‑by‑side model comparison |
Select up to 4 models, send the same prompt, and compare their answers together in one view.
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Does not provide a built‑in side‑by‑side comparison view for multiple different models in a single conversation.
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| Standard Q&A |
Available for everyday company workflows across sales, marketing, operations, hiring, and delivery.
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Available as secure enterprise chat.
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| Q&A with web search |
Search the web and get cited, up-to-date answers inside your workspace, grounded in sources and your organization memory.
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Available through RAG and web search.
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| Deep research workflows |
Can do deep research using specialized models.
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Available through integrations and agents rather than a single dedicated research mode.
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| Chat with documents |
Upload and chat with PDFs, Word files, spreadsheets, presentations, and more, grounded in projects and org memory.
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Supports files, folders, CSV, and XLSX analysis.
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| Coding capabilities |
Coding models help with code generation and debugging in the chat interface only, not directly inside your codebase.
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Available through prompts and agent-based Python execution.
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| Image capabilities |
Supports chatting with images as well as generating, editing, and modifying images for work content and workflows.
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Supports image upload, analysis, and generation.
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| Video & audio capabilities |
Chat with video and audio is supported for understanding, summarizing, and answering questions, but cannot directly modify or generate media.
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Partial support: audio and video can be uploaded and transcribed, but the platform does not generate music or video.
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| Chat with work apps |
Coming soon, with the ability to chat with work apps such as Gmail, Calendar, Drive, Slack, and more.
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Chat with connected work apps is available, with permissions mirrored 1:1 from the source tool.
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| Shared threads |
Supports shared threads with sharing, co-prompting, commenting, and tagging so people can work with AI together.
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Limited collaboration is available through shared Projects, but not a dedicated co-prompting, inline commenting, or tagging experience around threads.
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Memory & Context
| Capability | WorkLLM | Langdock |
|---|---|---|
| Personal memory | Available for individual user preferences and context. |
Available. |
| Thread memory | Maintains context within ongoing conversations. |
Maintains context within conversations. |
| Project memory | Available for shared project-specific knowledge and workflows. |
Project-level context is available for organizing work and data. |
| Organization memory | Built for shared company-level knowledge, including documents, links, brand guidelines, competitors, product context, and internal reference material. |
Not positioned as a dedicated organization-wide memory layer in the same way; context lives mainly at the project and connected-data level. |
| Context memory | Supports structured context inside projects, such as product, customer, campaign, or business-specific context. |
Not positioned as a separate structured context layer. |
| Memory architecture | Designed around organization → project → context → thread memory. |
Project and thread-level context capabilities are available, but not organized into a layered organization-wide memory architecture. |
Team Collaboration
| Capability | WorkLLM | Langdock |
|---|---|---|
| Multi-user AI threads | Built for teams to collaborate inside shared AI conversations. |
Limited collaboration is available through shared Projects. |
| Tagging and commenting | Built-in commenting and team discussion around AI outputs. |
Not positioned as a dedicated commenting workflow. |
| Team projects | Organize work around shared projects, customers, products, or initiatives. |
Project workspaces are available for organizing work. |
| Project contexts | Create specific context areas inside a project, such as a product, customer, campaign, or use case. |
Project-level context is available, though not broken into the same granular context-area structure as WorkLLM. |
AI Agents
| Capability | WorkLLM | Langdock |
|---|---|---|
| Ready-made agents |
Library of prebuilt agents for sales, marketing, HR, product, operations, and more so teams can start using AI on day one.
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Agents for specific use cases are available, but not organized into a department-by-department prebuilt library in the same way.
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| Creating new agents for company tasks |
Create work agents in minutes by defining the job, inputs, outputs, and guardrails, without technical skills or prompt engineering.
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A custom agent builder is included, letting teams create agents with defined context and behavior.
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| Knowledge-based agents |
Knowledge agents answer questions using organization memory, documents, links, and FAQs so teams get consistent, up-to-date answers without pinging experts.
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Agents with defined context are available, and can draw on the centralized Library of files and knowledge assets.
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| Task-based agents |
Coming soon – Task agents will run repeatable jobs like content, outreach, summaries, reports, and proposals using structured inputs and consistent outputs.
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Reusable Skills and agents for specific use cases can approximate repeatable tasks, but there isn't a distinct, separately governed task-agent type.
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| Workflow agents |
Coming soon – Planned workflow agents to run recurring processes across tools, such as daily summaries, handoffs, follow-ups, and status updates.
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Visual workflows with event triggers and multi-step automation are available for building recurring processes.
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| Governance & ownership |
Agents have clear owner controls for private, team, or organization access, with versioning and consistent behavior across users.
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Workspace-level permissions let admins manage settings and access, with source-tool permissions mirrored 1:1 in integrations.
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Security & Governance
| Capability | WorkLLM | Langdock |
|---|---|---|
| Tenant isolation |
Every customer gets a dedicated cloud or on-premise tenant, with data, embeddings, and access controls isolated at the infrastructure and application layer.
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Single-tenant SaaS and bring-your-own-cloud deployment options provide dedicated infrastructure and tenant isolation.
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| Deployment flexibility |
Supports managed cloud, private VPC, or fully on-premise deployment depending on compliance needs.
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Supports multi-tenant SaaS, single-tenant SaaS, bring-your-own-cloud, and on-premise deployment via Kubernetes.
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| Encryption at rest & in transit |
All customer data encrypted at rest and in transit using industry-standard protocols.
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Customer data is encrypted at rest and in transit, hosted primarily in the EU via Microsoft Azure.
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| Role-based access control (RBAC) |
Granular permissions across users, assistants, agents, and integrations.
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Workspace-level permissions let admins manage access, with source-tool permissions mirrored 1:1 in integrations.
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| SSO / SAML authentication |
SAML-based SSO supported for enterprise deployments.
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SSO, SCIM, and SAML are included on both the Business and Enterprise plans.
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| Audit logs & activity tracking |
All meaningful actions logged and available to workspace admins for compliance and investigations.
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Workspace admins have visibility and control over settings and access, though detailed audit log capabilities are not clearly documented.
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| Input & output guardrails |
Automatic redaction of sensitive data, prompt restrictions, and output policy enforcement built into the workspace.
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Independent penetration testing and vendor reviews are part of the security program, but a dedicated prompt-level guardrail layer is not clearly detailed.
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| No training on customer data |
Customer data is never used for model training; processed transiently for inference only.
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A zero data retention policy for model training is enforced at the API level for every model integration Langdock supports.
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| Data retention control |
Zero data retention at the LLM layer — requests to model providers are not retained.
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Data is hosted primarily in the EU, with configurable model hosting locations and optional usage analytics.
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| Admin dashboard & governance controls |
Central dashboard for integrations, sharing, usage visibility, and access revocation.
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Workspace-level admin controls manage settings and access across the organization, backed by independently audited compliance certifications.
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Disclaimer: Information about Langdock is based on publicly available documentation and product information as of July 2026. Features, pricing, and capabilities may change over time.
WorkLLM vs Langdock
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