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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.
| Date | Stars |
|---|---|
| 2026-07-24 | 1028 |
| 2026-07-25 | 1028 |
| 2026-07-28 | 1028 |
| 2026-07-30 | 1028 |
| 2026-07-31 | 1018 |
| 2026-08-06 | 1018 |
Today
— stars today
This week
-10 stars this week
This month
— stars this month
Momentum
10.0
growth rate 0.00%/day
# FinSight AI [English](README.md) | [简体中文](README.zh-CN.md)        Open-source AI equity research agent with evidence-grounded reports, resilient workflow orchestration, and RAG evaluation. FinSight turns filings, financial reports, research notes, market data, and company events into source-grounded answers and versioned AI research reports. The project is intentionally backend-heavy: it shows how to build the infrastructure around an AI agent, not just how to call a model. ## Product Walkthrough FinSight includes a runnable institutional research console. The UI is not just a decoration for the backend: it exposes the research workflow, report cache, evidence trace, RAG evaluation, and financial signals that the backend produces. ### Market Research Workspace  - Search a company symbol and inspect quote status, price trend, moving averages, volume, and AI thesis in one workspace. - The chart uses real market history when available and deterministic fallback data for offline demos, so the project remains easy to run in interviews. - The AI brief summarizes rating, confidence, positive points, and risk points instead of returning an unstructured chat answer. ### Agent Workflow And Report Trust  - The console surfaces the research task state machine: creation, ingestion, metric calculation, indexing, intelligence build, AI report generation, and completion. - Each task exposes idempotency key, attempts, lease owner, and fencing-token fields, making the concurrency-control design visible instead of hidden in code. - Report trace shows `reportVersion`, `dataSnapshotHash`, cache hit status, model source, generated time, and evidence chunks bound to the report. ### Metrics, Risks, And Evidence  - Financial metrics are distilled into research-facing health cards such as profitability, growth, cash-flow quality, and debt ratio. - Evidence search returns retrievable chunks from filings, announcements, and structured financial summaries. - The same evidence layer powers RAG answers, report citations, hallucination-risk checks, and evaluation regression cases. ## Why It Exists Most RAG demos stop at "retrieve chunks and ask an LLM." FinSight focuses on the parts that make an AI research system dependable: - long-running agent workflows with explicit state transitions; - idempotent task submission and duplicate execution control; - Redis Lua single-flight leases with fencing tokens; - report caching tied to data snapshots instead of loose prompt strings; - PostgreSQL/pgvector hybrid retrieval with evidence traceability; - RAG and agent quality evaluation for regression checks. ## Highlights | Area | What FinSight Implements | | --- | --- | | Agent workflow | Data ingestion, metric recalculation, document indexing, intelligence build, and AI report generation as recoverable stages | | Concurrency control | Idempotency keys, repository-level `createIfAbsent`, Redis Lua single-flight lease, fencing token, local fallback lock | | Failure recovery | Task status machine, stage tracking, retry, dead letter state, timeout takeover scheduler | | Trustworthy AI cache | `contextHash`, `dataSnapshotHash`, `reportVersion`, Redis/PostgreSQL-backed report reuse | | Retrieval | PostgreSQL JSONB, full-text search, pgvector embeddings, hybrid recall, deduped evidence
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2ddbd0c6512a148d, topic:ai-agent, readme:ai agent
matched fp:2ddbd0c6512a148d, topic:llm-evaluation
matched fp:2ddbd0c6512a148d, topic:rag