autoflow
AutoFlow is an open-source conversational knowledge base tool that uses graph-based retrieval-augmented generation (GraphRAG) to answer questions about your content. It combines TiDB's vector storage with LlamaIndex and DSPy to build intelligent chatbots that can be deployed standalone or embedded as a JavaScript widget on websites.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | pingcap/autoflow |
| Owner | pingcap |
| Primary language | TypeScript |
| License | Apache-2.0 — OSI-approved |
| Stars | 2.8k |
| Forks | 178 |
| Open issues | 75 |
| Latest release | 0.4.0 (2025-01-03) |
| Last updated | 2026-04-27 |
| Source | https://github.com/pingcap/autoflow |
What autoflow is
Built on TypeScript/Next.js frontend with Python backend, AutoFlow integrates TiDB Serverless vector storage, LlamaIndex RAG framework, and DSPy for LLM orchestration. It features sitemap-based web crawling for knowledge ingestion, conversation history management, and embeddings-based semantic search with graph-based reasoning for multi-hop retrieval.
Get the autoflow source
Clone the repository and explore it locally.
git clone https://github.com/pingcap/autoflow.gitcd autoflow# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Docker Compose setup with minimum 4 CPU cores and 8 GB RAM; validate infrastructure availability and networking before deployment.
- LLM provider integration unspecified in README; clarify which LLM APIs are supported, cost model, and fallback strategies before production use.
- Early-stage project (February 2024 origin, v0.4.0 release) with stated plan to become a Python package; expect potential breaking changes and incomplete feature coverage.
- Web crawler relies on sitemap URL discovery; validate sitemap availability and completeness for intended knowledge sources to avoid gaps in coverage.
- TiDB Serverless dependency requires cloud account and ongoing costs; model cost structure and data egress fees as part of TCO analysis.
When to avoid it — and what to weigh
- Real-time Transactional Systems — Not designed for systems requiring sub-second latency or strict ACID guarantees at scale; optimized for batch ingestion and conversational query patterns, not high-frequency updates.
- Regulated Data with Strict Compliance — Early-stage project (v0.4.0) with limited security hardening documentation; requires thorough vetting before use with PII, PHI, or data subject to HIPAA, SOC2, or GDPR obligations.
- Multi-tenant SaaS at Scale — Project lacks documented multi-tenancy, isolation, or production-grade monitoring; deployment docs recommend minimum 4 CPU cores and 8GB RAM per instance, limiting cost-efficient horizontal scaling.
- Offline or Air-gapped Environments — Requires external LLM APIs (not specified which) and TiDB Serverless cloud connectivity; not suitable for fully offline or air-gapped deployments without significant architectural changes.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license permitting commercial use, modification, and distribution with attribution and liability disclaimer.
Apache-2.0 permits commercial use, but as an early-stage project (v0.4.0, active development, 75 open issues), production deployments carry operational risk. Recommended: thorough testing, code review, and consideration of paid support or commercial alternatives before customer-facing use. Verify all dependencies (LlamaIndex, DSPy, TiDB) also permit your intended commercial model.
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 |
Project maturity and security posture unclear: no documented security audit, vulnerability disclosure policy, or security-focused release notes. Considerations for review before sensitive data use: (1) TiDB Serverless encryption in transit and at rest; (2) LLM API credential management and injection attack surface; (3) web crawler input validation (sitemap URL parsing); (4) embeddable widget XSS/CSRF mitigations; (5) chat history retention and user data segregation in shared TiDB instances. Recommend security review before production deployment.
Alternatives to consider
LangChain + Pinecone + Vercel
More mature ecosystem with extensive tooling, community libraries, and clearer commercial support; Pinecone offers managed vector DB alternative to TiDB Serverless.
Verba (Weaviate-based)
Focused GraphRAG implementation with Weaviate backend; better documentation and established deployment patterns, though smaller community.
Perplexity Labs (commercial) or Custom OpenAI Assistant
Proven production stability and support if you prioritize reliability over customization; managed infrastructure avoids self-hosted operational burden.
Build on autoflow with DEV.co software developers
Start with the live demo at tidb.ai, then review deployment docs and source code for your use case. For enterprise integration or custom development, discuss with a Devco engineer.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
autoflow FAQ
Which LLM providers does AutoFlow support?
Can I use AutoFlow for multi-tenant SaaS?
What happens if the web crawler fails on a particular site?
How much does TiDB Serverless cost?
Software developers & web developers for hire
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 autoflow is part of your vector databases roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Evaluate AutoFlow?
Start with the live demo at tidb.ai, then review deployment docs and source code for your use case. For enterprise integration or custom development, discuss with a Devco engineer.