MaxKB
MaxKB is an open-source Python platform for building enterprise agents with built-in RAG pipelines, workflow orchestration, and MCP tool support. It integrates with multiple LLMs (OpenAI, DeepSeek, Llama, etc.) and supports document ingestion, vector search via PostgreSQL+pgvector, and multi-modal I/O.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | 1Panel-dev/MaxKB |
| Owner | 1Panel-dev |
| Primary language | Python |
| License | GPL-3.0 — OSI-approved |
| Stars | 22k |
| Forks | 3k |
| Open issues | 49 |
| Latest release | v2.10.3-lts (2026-07-03) |
| Last updated | 2026-07-07 |
| Source | https://github.com/1Panel-dev/MaxKB |
What MaxKB is
Django/Python backend with Vue.js frontend, LangChain integration, PostgreSQL+pgvector for semantic search, and workflow engine for agentic orchestration. Supports zero-shot tool use via MCP protocol and custom function libraries. Docker-deployable with REST API surface.
Get the MaxKB source
Clone the repository and explore it locally.
git clone https://github.com/1Panel-dev/MaxKB.gitcd MaxKB# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- PostgreSQL + pgvector must be pre-configured and accessible; vector index dimensionality and refresh strategy depend on embedding model choice.
- Default admin credentials (admin/MaxKB@123) must be rotated immediately in production; enable TLS/reverse proxy for secure deployment.
- LLM endpoint integration (OpenAI, DeepSeek, local Ollama, etc.) requires API key management and rate-limit handling; test fallback strategies.
- Workflow engine complexity scales with business logic; start with simple RAG, advance to multi-step agentic loops with testing.
- Document upload pipeline (text splitting, vectorization) is automatic but may require tuning chunk size and overlap for domain-specific accuracy.
When to avoid it — and what to weigh
- Proprietary/Closed-Source Software Requirements — GPL-3.0 license requires derivative works to remain open-source. Cannot be relicensed or shipped as proprietary SaaS without upstream compliance.
- Ultra-Low-Latency Real-Time Inference — Django/Python stack adds serialization overhead; not optimized for sub-100ms response times in high-throughput scenarios.
- Zero Infrastructure Complexity — Requires PostgreSQL + pgvector setup, Docker orchestration, LLM endpoint configuration, and vector index tuning. Not a simple plug-and-play SaaS.
- Minimal Code Footprint or Edge Deployment — Full Python/Django application; not suitable for embedded or resource-constrained environments.
License & commercial use
Licensed under GNU General Public License v3.0. Source code must remain public; any modifications or derivative works must also be GPL-3.0. Commercial use (e.g., internal tools, custom deployments) is permitted as long as you do not distribute the modified software externally.
Internal commercial use (deploying as a private tool for your organization) is generally permitted under GPL-3.0. However, if you modify the code and distribute it (even as a hosted service to customers), you must release source under GPL-3.0 and allow downstream commercial use. Consult legal counsel before offering MaxKB-based services to third parties. Dual-licensing (proprietary) from upstream maintainers is unknown.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Default credentials must be changed immediately. No mention of input validation for document uploads (malicious PDFs, XXE) or prompt-injection mitigations in RAG context. API authentication method (JWT, API key, OAuth) unknown. PostgreSQL and LLM endpoints must be network-isolated. No security audit or vulnerability disclosure policy stated.
Alternatives to consider
LangChain + custom Flask/FastAPI
Offers fine-grained control over RAG, agents, and integrations without GPL constraints. Requires more engineering effort but supports proprietary licensing.
Anthropic's Claude API + custom orchestration
Managed LLM service with strong tool-use and vision; no self-hosting overhead. Requires cloud dependency and per-token costs; suitable if you accept vendor lock-in.
Vercel's AI SDK + Supabase (pgvector)
Lightweight alternative for document QA and simple agents; hosted vector DB reduces DevOps burden. Less agentic workflow power than MaxKB.
Build on MaxKB with DEV.co software developers
MaxKB accelerates intelligent document QA, customer service bots, and agentic workflows. Start with Docker, integrate your LLM, and iterate. Check our deployment guides and community examples on GitHub.
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MaxKB FAQ
Can we use MaxKB in a commercial product we sell to customers?
What LLMs are supported?
How does RAG reduce hallucinations?
Do we need to pay for vector storage or compute?
Custom software development services
Adopting MaxKB is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.
Ready to Deploy an Enterprise Agent?
MaxKB accelerates intelligent document QA, customer service bots, and agentic workflows. Start with Docker, integrate your LLM, and iterate. Check our deployment guides and community examples on GitHub.