Alex f5129a5656 fix(workflows): dispatch agent nodes by provider, not display label
/api/models reports `display_provider` when a catalog YAML sets one
(`foundry`, `azure_foundry`, `cloudflare`), and the builder persists that
string as a node's `llm_name`. The engine passed it straight to
LLMCreator, which only knows the names in PROVIDERS_BY_NAME and raises
`No LLM class found for type <label>`.

That fails the node before any LLM call, so the turn ends in ~60ms with
an empty answer and no tokens generated. It hits the *default* path: the
platform-default model's label is stamped into every newly dragged agent
node and validateWorkflow requires an agent node, so a new user's
untouched workflow could not produce a token regardless of what they
typed. Ordinary chat was unaffected because it resolves the provider from
the model registry and never reads the stored name.

Adds `resolve_dispatch_provider`, which prefers a stored name that is a
real dispatch provider, then the registry lookup, then the parent agent.
Nodes already saved with a label are repaired at run time, so no
migration is needed. The api_key now resolves from the normalized name
too — `get_api_key_for_provider` falls back to settings.API_KEY for names
it does not recognize, which would have sent the deployment key to
whatever endpoint the label happened to select.
2026-08-05 11:24:59 +01:00
2026-07-24 12:26:52 +01:00
2026-06-26 11:30:55 +01:00
2026-06-14 21:36:07 +01:00
2025-06-18 22:17:23 +01:00
2026-07-20 12:18:36 +01:00
2026-01-22 13:11:24 +02:00
2026-04-12 00:29:23 +01:00
2023-10-29 14:03:05 +05:30
2023-02-02 11:03:24 +00:00
2025-07-25 14:48:49 +05:30

DocsGPT 🦖

Private AI for agents, assistants and enterprise search

DocsGPT is an open-source AI platform for building intelligent agents and assistants. Features Agent Builder, deep research tools, document analysis (PDF, Office, web content, and audio), Multi-model support (choose your provider or run locally), and rich API connectivity for agents with actionable tools and integrations. Deploy anywhere with complete privacy control.


video-example-of-docs-gpt

Key Features:

  • 🗂️ Wide Format Support: Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, images, and audio files such as MP3, WAV, M4A, OGG, and WebM.
  • 🎙️ Speech Workflows: Record voice input into chat, transcribe audio on the backend, and ingest meeting recordings or voice notes as searchable knowledge.
  • 🌐 Web & Data Integration: Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.
  • ✅ Reliable Answers: Get accurate, hallucination-free responses with source citations viewable in a clean UI.
  • 🔑 Streamlined API Keys: Generate keys linked to your settings, documents, and models, simplifying chatbot and integration setup.
  • 🔗 Actionable Tooling: Connect to APIs, tools, and other services to enable LLM actions.
  • 🧩 Pre-built Integrations: Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.
  • 🔌 Flexible Deployment: Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).
  • 🏢 Secure & Scalable: Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.

Roadmap

  • Agent Workflow Builder with conditional nodes ( February 2026 )
  • Research mode ( March 2026 )
  • SharePoint & Confluence connectors ( March – April 2026 )
  • Postgres migration for user data ( April 2026 )
  • OpenTelemetry observability ( April 2026 )
  • Bring Your Own Model (BYOM) ( April 2026 )
  • Agent scheduling (RedBeat-backed) ( April 2026 )
  • Notifications & conversation search ( May 2026 )
  • Analytics & logs revamp with per-agent attribution ( June 2026 )
  • OIDC / SSO login with SCIM provisioning & groups ( June 2026 )
  • Admin dashboard & role-based access control (RBAC) ( June 2026 )
  • Agent import / export ( June 2026 )
  • Teams with team-scoped sharing & roles ( June 2026 )

You can find our full roadmap here. Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!

Production Support / Help for Companies:

We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.

Get a Demo 👋⁠

Send Email 📧

Join the Lighthouse Program 🌟

Calling all developers and GenAI innovators! The DocsGPT Lighthouse Program connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.

Learn More & Apply →

QuickStart

Note

Make sure you have Docker installed

A more detailed Quickstart is available in our documentation

  1. Clone the repository:

    git clone https://github.com/arc53/DocsGPT.git
    cd DocsGPT
    

For macOS and Linux:

  1. Run the setup script:

    ./setup.sh
    

For Windows:

  1. Run the PowerShell setup script:

    PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
    

Either script will guide you through setting up DocsGPT. Five options are available: using the public API, running locally, connecting to a local inference engine, using a cloud API provider, or building the docker image locally. The scripts will automatically configure your .env file and handle necessary downloads and installations based on your chosen option.

Navigate to http://localhost:5173/

To stop DocsGPT, open a terminal in the DocsGPT directory and run:

docker compose -f deployment/docker-compose.yaml down

(or use the specific docker compose down command shown after running the setup script).

Note

For development environment setup instructions, please refer to the Development Environment Guide.

Contributing

Please refer to the CONTRIBUTING.md file for information about how to get involved. We welcome issues, questions, and pull requests.

Architecture

Architecture chart

Project Structure

  • Application - Backend Flask application.

  • Extensions - Integrations and widgets (e.g., Chatwoot, React widget).

  • Frontend - Web UI built with Vite and React.

  • Scripts - Miscellaneous utility scripts.

Code Of Conduct

We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the CODE_OF_CONDUCT.md file for more information about contributing.

Many Thanks To Our Contributors⚡

Contributors

License

The source code license is MIT, as described in the LICENSE file.

This project is supported by:

color

S
Description
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
Readme MIT
107 MiB
0 Stars 1 Watchers 0 Forks
Languages
Python 73.2%
TypeScript 25.8%
Shell 0.4%
PowerShell 0.3%
JavaScript 0.1%