Claude 218d5ef709 feat(connectors): HubSpot checks follow the design's rows, copy and states
HubSpot follows the design:
- Names and order: the 9 checks are the design's 9 rows ("Sign in to
  HubSpot", "Read tickets", "List linked records", "Read record
  viewers"...). Dependencies follow the design's run order. "List linked
  records" and "Sample notes" run after the sign-in, not after the read
  checks: an empty type would stop them, but they can sample a record of
  another type. They leave out a denied type, which its read check
  reports.
- Every failure gives a short line and a description. A denial names the
  private app scope that HubSpot needs. Passing checks give what they
  found ("Signed in to account {id}", "Found {n} tickets", "{n} users
  found"...).
- Checks that passed without testing anything now say so. An empty type
  is SKIPPED. No record of any selected type FAILS the first read check.
  No record in scope skips the samples. A record without notes, and no
  listed user, are INCOMPLETE.

Fixes from the design's review:
- A rate limit past the retries is RateLimitedValidationError, which
  stops the run.
- Sign-in reports a missing token, an unreachable HubSpot and a timeout.
  On a form run, an unreachable HubSpot or a timeout fails sign-in.
- HubSpotApiError is a SourceApiError with the request and HubSpot's
  error body, so Error Details show them. The gateway raises
  HubSpotConnectionError for a failed or timed-out connection.
- Permission sync validation accepts a portal with no records.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KFMazQbuG8ui3jxBxm47sC
2026-10-17 02:50:21 +00:00
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Discord Documentation Website License

onyx-dot-app/onyx | Trendshift

Onyx - The context layer powered by all your apps

"LLMs know about everything public, but what if it could also know what's going on in our team? I want an AI coworker, not an AI new hire."

Onyx is the knowledge/context layer for your team and AI agents.

Onyx connects to your application to index and surface knowledge from 50+ applications while protecting your data sovereignty through flexible self-hosted deployments.

Beyond search, Onyx enables LLMs with advanced features like web search, sandboxes, skills, and more.

Tip

Deploy with a single command:

curl -fsSL https://onyx.app/install_onyx.sh | bash

Onyx chat answering a question about use cases


How does Onyx work

Onyx creates a representation of knowledge across all connected sources. It pulls data along with metadata, permissions, etc. and ingests it so that the information is easily accessible for downstream use cases.

Compared to MCP based searches and index-free approaches, Onyx provides a more reliable, low latency, and low cost context for any given query whether it's a simple keyword query or a complex research task.

Instead of an agent coordinating and iteratively searching dozens of MCP and burning many thousands of tokens, Onyx fetches the context across its internal representation instantly and filters down to only the most relevant ground truth documents.

⭐ Features

  • 🔍 Agentic RAG: Get best in class search and answer quality based on hybrid index + a custom agent harness tuned for information retrieval.
  • 🔬 Deep Research: Get in depth reports with a multi-step research flow.
  • 🤖 Custom Agents: Build AI Agents with unique subsets of knowledge, custom instructions, and the ability to take actions.
  • 🌍 Web Search: Augment internal knowledge with live web search.
    • Supports Serper, Google PSE, Brave, SearXNG, and others.
    • Comes with an in house web crawler and support for Firecrawl/Exa.
  • ▶️ External Actions & MCP: Let Onyx agents take actions in external applications to complete tasks end to end.
  • 💻 Secure Sandbox: Execute code and work with intermediate artifacts in a sandbox for complex workflows.
  • 📄 Artifacts: Generate documents, graphics, and other downloadable artifacts.
  • 🎙️ Voice Mode: Interact with Onyx via text-to-speech and speech-to-text.

Onyx supports all major LLM providers, both self-hosted (like Ollama, LiteLLM, vLLM, etc.) and proprietary (like Anthropic, OpenAI, Gemini, etc.).

To learn more - check out our docs!


Security and Data Processing

Onyx architecture: everything runs inside your environment

When connecting up your organization's knowledge, it's critical that this sensitive IP is not leaked to the wrong parties both external and internal.

Onyx provides an air-gappable, self-hosted deployment where the document index, database, and processing all happen within a self-contained set of services.

You can also choose a trusted embedding model and LLM provider (both of which can also run locally).


Access Onyx from anywhere

The same security and fine grained permissions apply no matter where the question comes from.

  • Web and desktop app - Ask questions, interface with Onyx AI agents, and everything else in the feature list above.
  • Slack and Discord bot - Get answers directly in Slack or Discord from a bot connected to your org's knowledge.
  • MCP server - Point Claude Code, Open Code, Codex, or any MCP client at Onyx. Your AI agents get company context with the same access controls as the person running them.
  • Chrome extension - Query Onyx from any tab with context from the page, directly in Chrome.
  • Embeddable Widget - Easily add Onyx functionality to your app or website.

🚀 Deployment Modes

Onyx supports deployments in Docker, Kubernetes, Helm/Terraform and provides guides for major cloud providers. Detailed deployment guides found here.

Onyx supports two separate deployment options: standard and lite.

Standard Onyx

The complete feature set of Onyx which is recommended for serious users and larger teams. Additional components not included in Lite mode:

  • Vector + Keyword index for RAG.
  • Background containers to run job queues and workers for syncing knowledge from connectors.
  • AI model inference servers to run deep learning models used during indexing and inference.
  • Performance optimizations for large scale use via in memory cache (Redis) and blob store (MinIO).

Onyx Lite

The Lite mode can be thought of as a lightweight AI Chat UI. It requires less resources (under 1GB memory) and runs a less complex stack but is not capable of indexing documents. It is great for users who want to test out the Onyx UI quickly or for teams who are only interested in the Chat UI and Agents functionalities.

Tip

To try Onyx for free without deploying, visit Onyx Cloud.


🏢 Onyx for Enterprise

Onyx is built for teams of all sizes, from individual users to the largest global enterprises:

  • 👥 Collaboration: Share chats and agents with other members of your organization.
  • 🔐 Single Sign On: SSO via Google OAuth, OIDC, or SAML. Group syncing and user provisioning via SCIM.
  • 🛡️ Role Based Access Control: RBAC for sensitive resources like access to agents, actions, etc.
  • 📊 Analytics: Usage graphs broken down by teams, LLMs, or agents.
  • 🕵️ Query History: Audit usage to ensure safe adoption of AI in your organization.
  • 💻 Custom code: Run custom code to remove PII, reject sensitive queries, or to run custom analysis.
  • 🎨 Whitelabeling: Customize the look and feel of Onyx with custom naming, icons, banners, and more.

📚 Licensing

There are two editions of Onyx:

  • Onyx Community Edition (CE) is available freely under the MIT license and covers all of the core features for RAG, AI Chat, Agents, and Actions.
  • Onyx Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations.

For feature details, check out our website.

👪 Community

Join our open source community on Discord!

💡 Contributing

Looking to contribute? Please check out the Contribution Guide for more details.

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