juanjuandog/FinSight-AI
AI equity research agent with resilient workflows, evidence-grounded RAG, versioned reports, and automated quality evaluation. observed · 2026-09-03
Health v2 · maintenance only
59/100
- Activity 100
- Release rhythm 35
- Longevity 8
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 114
- days_rel: n/a
- days_push: 0
- n_releases_24m: 0
Adoption not part of the score
1034 stars · 62 forks observed · 2026-09-03
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
FinSight AI is an open-source equity research workspace for A-share companies that turns market data, filings, and financial metrics into structured, evidence-grounded AI research reports. Built on Java 17 and Spring Boot with PostgreSQL/pgvector, RabbitMQ, and Redis, it emphasizes recoverable long-running agent workflows, snapshot-bound versioned reports, and automated LLM output evaluation.
Use cases
- research A-share companies with AI-generated analysis
- build reliable long-running LLM agent workflows
- generate evidence-grounded financial research reports
- run RAG over company filings and announcements
- evaluate LLM output quality automatically
- track company events and risk signals on a timeline
- self-host an AI equity research assistant
When to choose
- you need reproducible, evidence-backed AI research reports rather than raw chatbot answers
- you want a production-grade reference for resilient agent workflow orchestration in Java/Spring Boot
- you research Chinese A-share equities and want filings, quotes, and metrics in one workspace
- you need versioned reports bound to data snapshots with an inspectable evidence path
When to avoid
- you need automated trading or investment execution - it is explicitly a research aid, not advice
- you need coverage of non-A-share markets like US or European equities
- you want a lightweight Python-based LLM stack instead of a JVM/Spring Boot backend
- you need real-time streaming market data
Facets
application · maturity active
agent-framework rag llm-inference workflow-automation search-engine data-visualization llm-training monitoring artificial-intelligence fintech large-language-models jvm self-hosted windows equity-research spring-boot pgvector rabbitmq redis a-share financial-analysis workflow-orchestration evidence-grounded report-generation ai-agents retrieval-augmented-generation data-engineering docker web-server linux macos
1 source
- readme: https://github.com/juanjuandog/FinSight-AI · fetched 2026-09-03 · 314e0388c623
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| juanjuandog/FinSight-AI | main | 59 |
For agents
markdown · JSON · MCP: product_card(name="juanjuandog/FinSight-AI")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem