Lore Agent

为了解决通用模型在专业领域知识不够优/新的问题，通过在线研究补充 + 本地知识库沉淀实现知识治理，让 AI 在你的领域越用越强。通过 MCP 接入 Claude Code 与 VS Code Copilot。

A zero-dependency, drop-in knowledge agent that gives any project local retrieval, web research, structured answer synthesis, and a self-improving knowledge loop — all accessible to Claude Code and VS Code Copilot through MCP.

Why Lore Agent?

Quick Start

As a standalone project

# 1. Clone and install
git clone https://github.com/zfy465914233/lore-agent.git
cd lore-agent
pip install -r requirements.txt

# 2. Build the knowledge index
python scripts/local_index.py --output indexes/local/index.json

# 3. (Optional) Start SearXNG for web research
docker compose up -d

# 4. (Optional) Add semantic retrieval
pip install sentence-transformers
python scripts/local_index.py --output indexes/local/index.json --build-embedding-index

Embed into an existing project

# 1. Copy lore-agent into your project
cp -r lore-agent/ your-project/lore-agent/

# 2. Run the setup script (from your project root)
cd your-project
python lore-agent/setup_mcp.py

This automatically:

• Injects MCP config into .mcp.json (Claude Code) and .vscode/mcp.json (VS Code Copilot)
• Adds a CLAUDE.md snippet instructing the AI to prioritize Lore tools

After restarting Claude Code or VS Code, the AI will automatically discover and use query_knowledge, save_research, and list_knowledge.

MCP Integration

Lore Agent exposes 3 tools to LLM agents:

Claude Code

.mcp.json is pre-configured. cd into the project and start Claude Code.

VS Code Copilot

.vscode/mcp.json is pre-configured. Open the project in VS Code, enable Copilot agent mode.

Both configs run the same mcp_server.py via uv run --with fastmcp.

How It Works

Query → Router (local-led or web-led)
         │                    │
         ▼                    ▼
   Local Retrieval      Web Research
   (BM25 + embed)      (SearXNG + APIs)
         │                    │
         └──────┬─────────────┘
                ▼
        Answer Synthesis
        (structured JSON schema)
                │
                ▼
        Knowledge Loop ──► distill → promote → reindex

1. Router classifies queries — definitions go local, fresh topics go web, complex ones mix both
2. Retriever uses BM25 (always) + optional semantic embeddings for hybrid search
3. Synthesizer produces structured answers with claims, inferences, uncertainty, and action items
4. Knowledge Loop saves research as Markdown cards, promotes drafts, and rebuilds the index — the system accumulates knowledge over time

Project Structure

lore-agent/
├── mcp_server.py              # MCP server (Claude Code + VS Code Copilot)
├── .mcp.json                  # Claude Code MCP config
├── .vscode/mcp.json           # VS Code Copilot MCP config
├── docker-compose.yml         # SearXNG for web research
├── requirements.txt           # Core dependencies (zero external deps)
├── schemas/
│   ├── answer.schema.json     # Structured answer schema
│   └── evidence.schema.json   # Evidence schema
├── scripts/
│   ├── local_index.py         # Build BM25 index from knowledge cards
│   ├── local_retrieve.py      # Hybrid retrieval (BM25 + embedding)
│   ├── embedding_retrieve.py  # Semantic embedding (sentence-transformers)
│   ├── bm25.py                # Pure Python BM25 implementation
│   ├── research_harness.py    # Web research (SearXNG + OpenAlex + Semantic Scholar)
│   ├── close_knowledge_loop.py# Save research → knowledge card → reindex
│   ├── synthesize_answer.py   # Answer synthesis (LLM API or --local-answer)
│   ├── agent.py               # Agent control loop (Router/Researcher/Synthesizer/Curator)
│   ├── orchestrate_research.py# Query routing and evidence orchestration
│   └── retry.py               # Exponential backoff for external APIs
├── knowledge/                 # Knowledge cards organized by topic
│   ├── templates/             # Card templates (definition, method, research-note, etc.)
│   └── examples/              # Example cards to get started
├── indexes/                   # Generated (gitignored)
└── tests/                     # 74 tests, ~4s

Adding Knowledge

Option A: Through MCP (recommended)

Ask your LLM agent:

"Search for recent advances in [topic], then save the findings."

The agent calls save_research(query, answer_json) which writes a knowledge card and rebuilds the index.

Option B: Manually

Create a Markdown file in knowledge/<domain>/ following a template from knowledge/templates/. Then rebuild the index:

python scripts/local_index.py --output indexes/local/index.json

Option C: Web Research Pipeline

# Research a topic via SearXNG + academic APIs
python scripts/research_harness.py "your topic" --depth medium --output /tmp/research.json

# Synthesize and save
python scripts/close_knowledge_loop.py \
  --query "your topic" \
  --research /tmp/research.json \
  --answer /tmp/answer.json

Running Tests

python -m pytest tests/ -v    # 74 tests, ~4s

Benchmark

Built-in eval harness with 8 benchmark cases across 4 query categories.

python scripts/run_eval.py --dry-run

Breakdown by category:

Note: Dry-run mode skips LLM calls. answer_present_rate is 0% in dry-run since no LLM generates answers. With a live LLM, answer quality is additionally evaluated.

License

This project is licensed under the MIT License - see the LICENSE file for details.