DEV.co
AI Frameworks · arc53

DocsGPT

DocsGPT is an open-source AI platform for building private agents and assistants with document analysis, multi-model support, and enterprise search capabilities. It ingests PDFs, web content, audio, and integrates with major LLMs or local models, deployable on-premise for full privacy control.

Source: GitHub — github.com/arc53/DocsGPT
18k
GitHub stars
2.1k
Forks
Python
Primary language
MIT
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositoryarc53/DocsGPT
Ownerarc53
Primary languagePython
LicenseMIT — OSI-approved
Stars18k
Forks2.1k
Open issues88
Latest release0.18.0 (2026-06-26)
Last updated2026-07-07
Sourcehttps://github.com/arc53/DocsGPT

What DocsGPT is

Python-based RAG platform with Flask backend, React frontend, and support for OpenAI, Google, Anthropic APIs plus local inference (Ollama, llama_cpp). Features agentic workflows, vector search, audio transcription, API tooling, and Kubernetes-ready deployment with PostgreSQL user data storage.

Quickstart

Get the DocsGPT source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/arc53/DocsGPT.gitcd DocsGPT# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Enterprise Knowledge Base & Internal Search

Ingest internal documents (PDFs, Office files, web pages) and provide employees semantic search with source citations. Deploy privately on-premise to maintain data sovereignty.

Autonomous Agent Workflows with Tool Integration

Build conditional agent workflows using the Agent Builder, connect to external APIs and services, schedule execution, and enable LLM-driven actions with enterprise integrations (SharePoint, Confluence).

Multi-Channel AI Assistant Deployment

Expose a single knowledge base and agent logic via API, chat widgets, Discord/Telegram bots, and pre-built HTML/React components for consistent assistant experience across channels.

Implementation considerations

  • Clone repo, run setup.sh/setup.ps1 (Docker required); scripts configure .env and handle dependency installation. Quickstart targets localhost:5173 but requires Docker daemon running.
  • Choose LLM provider upfront: cloud API (OpenAI, Google, Anthropic) vs. local (Ollama, llama_cpp). Local inference adds latency and GPU/memory overhead; cloud API incurs per-query costs.
  • Document ingestion supports 12+ formats (PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, audio). Large document collections may require tuning chunking/embedding strategies and storage scaling.
  • Multi-agent teams, OIDC/SSO, and RBAC are roadmap (June 2026). Current release (0.18.0, July 2026) likely includes these; verify feature availability in your target version.
  • Kubernetes support stated but details unknown. Evaluate networking, persistent volume setup, and PostgreSQL cluster configuration for production deployments.

When to avoid it — and what to weigh

  • Requiring Production SLA Guarantees Without Support Contract — While DocsGPT is production-ready, the README indicates commercial support is a separate offering ('Send Email' or 'Get a Demo' for production deployments). Verify SLA and support terms before committing.
  • Need for Fully Governed Model Access & Audit Trails — Project does not clearly specify fine-grained audit logging, data residency guarantees, or model governance features. Admin dashboard and RBAC are roadmap items (June 2026) and may need verification in current release.
  • Low Tolerance for Dependency on External APIs — While local model support exists, the platform is designed around integration with OpenAI, Google, and Anthropic APIs. Local-only deployments may have capability or inference speed trade-offs.
  • Simple FAQ Bot Without Custom Agent Logic — DocsGPT targets agents and complex workflows. For static FAQ retrieval, lighter alternatives (e.g., embedding-based chatbots) may be more cost-effective and simpler to operate.

License & commercial use

MIT License (permissive, OSI-approved). Allows commercial use, modification, and distribution with attribution and liability disclaimers. No copyleft restrictions.

MIT license permits commercial deployment. However, commercial support (SLA, priority bug fixes, production assistance) is sold separately via [email protected] and 'Get a Demo' contact forms. Terms and pricing are not disclosed in the README; clarify support scope before relying on production commitments.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityModerate
DEV.co fitStrong
Assessment confidenceHigh
Security considerations

Platform claims 'Secure & Scalable' with private on-premise deployment. Considerations: (1) Data residency depends on LLM provider choice (cloud APIs retain query logs; local models do not). (2) OIDC/SSO and RBAC are roadmap items (June 2026), not confirmed in current release. (3) No mention of encryption-at-rest, TLS enforcement, or third-party security audits. (4) API keys are mentioned but key rotation, expiry, and revocation mechanisms are not detailed. (5) Audio transcription and document parsing could expose sensitive data if model provider is untrusted. Recommend security audit before production deployment of sensitive data.

Alternatives to consider

LlamaIndex / LangChain

Frameworks for building custom RAG and agent applications. More flexible and lightweight than DocsGPT but require more engineering effort; better for teams building proprietary workflows.

Anthropic Claude (native Documents API) / OpenAI Assistants

Cloud-native, managed services with document ingestion. Trade privacy for managed SLAs and reduced operational overhead; vendor lock-in; no local deployment option.

Haystack / Weaviate

Open-source vector search and RAG frameworks. Lower-level primitives; require more assembly for full agent/assistant logic but offer fine-grained control and flexibility.

Software development agency

Build on DocsGPT with DEV.co software developers

Contact the DocsGPT team at [email protected] for production support, custom integrations, and enterprise SLA terms. Join the Lighthouse Program for early access to advanced features.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

Related on DEV.co

Explore the category and the services that help you build with it.

DocsGPT FAQ

Can DocsGPT run entirely offline without cloud LLM APIs?
Yes. DocsGPT supports local models via Ollama and llama_cpp. However, inference speed and quality depend on local hardware. Cloud APIs (OpenAI, Google, Anthropic) offer better quality but require internet and API keys.
Is there a managed hosting option or do I have to self-host?
README mentions https://app.docsgpt.cloud/ (Cloud Version) and https://www.docsgpt.cloud/contact (sales contact). Managed hosting likely exists but pricing and feature parity are not documented in the README.
What happens to my documents and queries?
Documents are ingested and stored in your DocsGPT instance (local or cloud). If you use cloud LLMs (OpenAI, Google, Anthropic), queries are sent to their servers and subject to their privacy policies. Local model deployments keep data in-house.
When will RBAC and enterprise features be available?
Roadmap shows Admin dashboard & RBAC, Teams, OIDC/SSO with SCIM, and observability completed by June 2026. Current release (0.18.0) likely includes these; verify in release notes for your version.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If DocsGPT is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Deploy DocsGPT for Your Organization?

Contact the DocsGPT team at [email protected] for production support, custom integrations, and enterprise SLA terms. Join the Lighthouse Program for early access to advanced features.