Lore Agent

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AI that gets smarter in YOUR domain — every question compounds.

MindPulse Academic Suite

Scholar Agent is part of the MindPulse Academic Suite, forming a powerful synergy between local open-source tools and fully-managed cloud services:

• 🌌 Scholar Agent (Open Source & Local): A local-first knowledge flywheel that integrates with your IDE via MCP (Model Context Protocol). It saves research answers as local Markdown knowledge cards, building your custom personal LLM-Wiki.
• ⚡ PaperPulse (Cloud SaaS): A fully-managed daily academic digest SaaS that monitors arXiv/Semantic Scholar, scores papers based on your personalized research preferences, and delivers distilled summaries straight to your WeChat or Email.

💡 Synergy (One-Click Local Sync):

• One-Click Sync: When your Scholar Agent MCP server is running locally, simply click the "Import to Local Scholar Agent" button on the PaperPulse web interface. The note will be instantly synced and written to your local knowledge/ directory via a secure local loopback interface, bypass browser sandbox constraints and rebuild your search index automatically!
• Manual Export: You can also click "Export Markdown" to download the standard Markdown note and place it into your knowledge base directory manually.

Why

Every AI conversation generates knowledge — research findings, technical explanations, citations. But LLMs are stateless: each new session starts from zero. The research your AI completed yesterday is not available today.

Scholar Agent makes AI knowledge persistent. It saves research and answers as local knowledge cards — structured, citable, and interconnected. Before answering, the AI checks existing local knowledge first, building on what it has already learned rather than starting from scratch each time.

The result is a personal LLM-Wiki: structured, traceable, continuously growing — making your AI increasingly accurate in the domains you care about.

Demo

_{Ask → Research → Save as knowledge card → Knowledge compounds over time}

What It Does

Architecture & Data Flow

When you ask a question, the agent routes the query through a local-first retrieval loop before falling back to external sources:

sequenceDiagram
    actor User
    participant Host as Claude Code / VS Code
    participant MCP as Scholar Agent (MCP Server)
    participant Local as Local Index (BM25)
    participant Web as arXiv / Semantic Scholar

    User->>Host: Prompt: "Explain MoE"
    Host->>MCP: query_knowledge("MoE")
    MCP->>Local: BM25 Query
    alt Local Hit (BM25 Score >= Threshold)
        Local-->>MCP: Match (e.g. mixture-of-experts.md)
        MCP-->>Host: Local Note Context
    else Local Miss
        MCP->>Web: API Fallback (arxiv + web search)
        Web-->>MCP: Raw Papers & Metadata
        MCP->>MCP: Synthesize & Distill
        MCP->>Local: Save Card (Staging -> Validate -> Promote)
        MCP-->>Host: Synthesized Answer + Citations
    end
    Host->>User: Natural Language Response

Knowledge Persistence

Each conversation can produce a knowledge card — a structured record with:

• The question asked
• Evidence-backed answer with citations
• Confidence scores and uncertainty flags
• Source references you can trace back

These cards accumulate into a searchable local knowledge base. Next time a similar question comes up, the AI draws from what's already been researched.

Knowledge Network

Cards aren't isolated files. Scholar Agent:

• Maintains a quality lifecycle for each card: draft → reviewed → trusted → stale → deprecated
• Auto-generates [[wiki-links]] between related cards
• Tracks provenance — every claim links back to its source evidence
• Outputs Obsidian-compatible Markdown (YAML frontmatter + wiki-links)
• Obsidian Graph Ready — Open your data directory (e.g. ~/scholar/) directly as an Obsidian Vault to navigate your visual knowledge graph.

Evidence-Based Answers

When researching a question, Scholar Agent:

1. Searches local knowledge (BM25 keyword index)
2. Falls back to web and academic APIs when local knowledge is insufficient
3. Synthesizes answers where every claim cites its source
4. Flags claims that lack supporting evidence
5. Returns structured results with confidence levels and suggested next steps

Academic Research Pipeline

For paper research, Scholar Agent provides:

• Paper Search — arXiv, DBLP, Semantic Scholar with 10+ top-conference filters
• Smart Scoring — 4-dimensional ranking: relevance, recency, popularity, quality
• Deep Analysis — 20+ section structured notes with AI-assisted completion
• Figure Extraction — From arXiv source archives and PDFs
• Daily Recommendations — Dual-track: 2 top-conference papers + 2 arXiv innovation papers
• Paper → Knowledge Card — Feed analyses back into the knowledge base

Quick Start

Install

pip install py-scholar-agent

Or with pipx (isolated environment):

pipx install py-scholar-agent

Or from source:

git clone https://github.com/zfy465914233/scholar-agent.git
cd scholar-agent
pip install -e .

Setup

scholar-agent init

One command creates data directories, writes config, and registers MCP with Claude Code. Done.

Modes

MCP Integration

Scholar Agent runs as an MCP server, integrating directly into your tools:

• Claude Code — scholar-agent install claude --write
• VS Code Copilot — scholar-agent install vscode --write
• OpenCode — scholar-agent install opencode --write

Core tools (always available): query_knowledge · save_research · list_knowledge · capture_answer · ingest_source · build_graph

Academic tools (set SCHOLAR_ACADEMIC=1): search_papers · search_conf_papers · download_paper · analyze_paper · extract_paper_images · paper_to_card · daily_recommend · link_paper_keywords

{
  "mcpServers": {
    "scholar-agent": {
      "command": "scholar-agent",
      "args": ["serve-mcp"],
      "env": {
        "SCHOLAR_ACADEMIC": "1"
      }
    }
  }
}

Local Retrieval

Knowledge is indexed with BM25 for fast keyword search — no external dependencies required. An optional embedding layer can be enabled for semantic similarity with scholar-agent index --build-embedding-index.

CLI Reference

Configuration

Environment Variables

Config File

See .scholar.example.json for a full example. Key sections:

• knowledge_dir — Knowledge cards directory
• index_path — BM25 search index
• academic.research_interests — Your domains, keywords, arXiv categories
• academic.scoring — Paper scoring weights

Data Directory

scholar/
├── config/         # Configuration files
├── knowledge/      # Knowledge cards
├── paper-notes/    # Paper analysis notes
├── daily-notes/    # Daily paper recommendations
├── indexes/        # BM25 search index
├── cache/          # Cached data
└── outputs/        # Generated outputs

Recommended Workflow

Daily research flow

Ask a question (via MCP)
  → Scholar Agent searches local knowledge first
  → Falls back to web/academic APIs when needed
  → Synthesizes answer with citations
  → Saves as a knowledge card
  → Next similar question draws from local knowledge

Paper analysis flow

For best paper analysis quality:

1. Download: download_paper("2510.24701", title="Paper Title", domain="LLM")
2. Extract images: extract_paper_images("2510.24701")
3. Deep analysis: analyze_paper(paper_json)
4. Feed into knowledge base: paper_to_card(paper_json)

Downloading the PDF first enables full-text extraction, producing notes with specific data, formulas, and experimental results.

Development

make dev       # Install with dev dependencies + pre-commit hooks
make lint      # Run ruff + mypy
make test      # Run test suite (1121 tests, ~20s, fully offline)
make coverage  # Run tests with coverage report
make build     # Build distribution package
make docker    # Build Docker image

See CONTRIBUTING.md for detailed guidelines.

Highlights

• Knowledge persistence — Every conversation can produce a reusable knowledge card; the local knowledge base grows over time
• Evidence-based — Every claim cites its source, with confidence scores and uncertainty flags
• Quality lifecycle — Cards are validated, scored, promoted, and deprecated. Full provenance tracking
• Knowledge network — Wiki-links connect related cards into a navigable knowledge graph
• Obsidian compatible — Markdown + YAML frontmatter + [[wiki-links]]. Your data, no lock-in
• Academic pipeline — Search → Score → Analyze → Extract → Recommend, fully automated
• MCP integration — Works with Claude Code, VS Code Copilot, and OpenCode out of the box
• Offline-first — Local BM25 index, graceful degradation when external APIs are unavailable

Comparison

Wondering how Scholar Agent compares to mem0, MemGPT, or Zep? See docs/comparison.md for a detailed breakdown.

License

MIT — see LICENSE.