Ross ROSS = Recommend OSS · open-source software intelligence for agents

juanjuandog/FinSight-AI

AI equity research agent with resilient workflows, evidence-grounded RAG, versioned reports, and automated quality evaluation. observed · 2026-09-03

github.com/juanjuandog/FinSight-AI · Java · MIT (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
juanjuandog/FinSight-AImain59

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