Montage — The runtime for agentic user interfaces vs WeKnora: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Montage — The runtime for agentic user interfaces and WeKnora — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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Montage — The runtime for agentic user interfaces
Montage
A model-agnostic SDK that renders agentic, interactive UIs, hydrates artifacts faster, and reduces token usage across underlying models.
Key features
- Model-agnostic SDK: A single SDK and runtime that works with any underlying model provider, enabling developers to render agentic UI components without locking into a specific LLM.
- Agentic UI Rendering: Renders rich, interactive components (agentic widgets) that encapsulate agent behavior, state, and UI in a reusable format for web applications.
- Faster Hydration: Optimized client/server hydration flow to initialize interactive artifacts quickly, reducing perceived latency for end users.
- Token Usage Reduction: Built-in optimizations for prompt/state management that cut token usage across models, lowering operational costs and improving efficiency.
- Stateful Artifact Management: Manages and persists the state and lifecycle of interactive artifacts and their events, simplifying developer handling of agent-driven UI.
- Seamless Front-end Integration: Designed to integrate with common front-end workflows and frameworks, allowing easy embedding of agentic interfaces into existing apps.
- Model Swapping and Fallbacks: Enables switching or combining different underlying models without reauthoring UI logic, supporting fallbacks and provider-agnostic strategies.
- Developer Tooling: Provides developer-oriented APIs and runtime controls to debug, test, and tune agentic components and their interactions.
- Single SDK to render rich, interactive agentic UI components
- Runtime that hydrates interactive artifacts faster
- Reduces token usage across underlying models
- Model-agnostic operation that works with any underlying model
- Focus on rendering agentic interfaces and interactive components
Best for
- Embedding conversational, agent-driven widgets in web apps to handle complex, interactive user workflows without rebuilding front-end UI each time.
- Prototyping agentic interfaces quickly using the single SDK to iterate on UI/agent behavior while swapping underlying models for evaluation.
- Reducing LLM operational costs by leveraging Montage’s prompt and state optimizations to cut token usage across responses and background context.
- Hydrating server-rendered interactive artifacts on the client for faster startup and seamless handoff from server logic to agent-driven interactions.
- Integrating multiple model providers and failover strategies so applications can route requests to different models without changing UI code.
- Managing long-lived, stateful agent interactions (e.g., multi-step assistants or tool-using agents) with built-in lifecycle and state management.
- Build interactive agentic user interfaces with a single SDK
- Embed agent-driven UI components into applications while minimizing token costs
- Hydrate and rehydrate interactive artifacts quickly for responsive UX
- Integrate agentic UI rendering on top of different underlying language models
WeKnora
Tencent
Tencent's open-source LLM knowledge framework turning documents into a RAG-queryable, agent-reasoned, self-maintaining wiki.
Key features
- RAG Quick Q&A: Semantic retrieval over ingested documents for everyday lookups, with editable retrieval chunks that support per-version diff, rollback and automatic reindexing.
- ReAct Agent Orchestration: An autonomous agent that plans across retrieval, MCP tools, a per-tenant skill catalog, sandboxes and web search to resolve complex multi-step questions.
- Wiki Mode: Agents distil raw uploads into a self-maintaining, interlinked markdown knowledge base with an interactive knowledge graph, in-browser editing, line-level diffs and one-click rollback.
- Skill Sandbox Runtime: Session-persistent Docker, E2B and Cube sandbox backends with per-tenant network policy, skill installation from ClawHub, SkillHub, git or zip, snapshots and live progress.
- Cross-Session Long-Term Memory: Profile, preference, fact, task and interest memory extracted automatically with user confirmation and searchable across sessions.
- Multi-Source Ingestion: Auto-syncing knowledge from Feishu Wiki and Drive, GitLab, Tencent IMA, Notion, Yuque, DingTalk Docs and RSS, with 10+ document formats including PDF, Word, Excel, images and XMind.
- Swappable Provider Stack: 20+ LLM providers including OpenAI, DeepSeek, Qwen, Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, LiteLLM and Ollama, with interchangeable vector databases and storage backends per workspace.
- Enterprise Multi-Workspace RBAC: A four-tier role matrix with per-resource ownership, per-workspace audit logs, scoped API keys with a principal model, OIDC JWKS verification and Langfuse OTel tracing.
Best for
- Internal Knowledge Base: Turning scattered company documents into a queryable wiki that agents keep current instead of a folder of stale files.
- Data-Sovereign Deployment: Running a full RAG and agent stack on private cloud or local infrastructure where documents cannot leave the network.
- IM-Channel Support Bot: Serving grounded answers from company documents directly inside WeCom, Feishu, Slack or Telegram.
- Multi-Source Documentation Sync: Keeping a single searchable index over Notion, GitLab, Feishu and Yuque content that syncs automatically as sources change.
- Retrieval Quality Tuning: Editing, diffing and reverting individual retrieval chunks in the UI to fix bad answers without rebuilding the whole index.
- Agent Pipeline Observability: Using Langfuse tracing and the runtime task queue dashboard to see agent reasoning, token usage and worker pool behaviour in production.
- Embedded Public Agents: Publishing a knowledge agent to an external website through embed widgets and scoped API keys.