IETF Draft Analyzer v0.1.0 — track, categorize, and rate AI/agent drafts

Python CLI tool that fetches AI/agent-related Internet-Drafts from the IETF
Datatracker, rates them using Claude, generates embeddings via Ollama for
similarity/clustering, and produces markdown reports.

Features:
- Fetch drafts by keyword from Datatracker API with full text download
- Batch analysis with Claude (token-optimized, responses cached in SQLite)
- Embedding-based similarity search and overlap cluster detection
- Reports: overview, landscape by category, overlap clusters, weekly digest
- SQLite with FTS5 for full-text search across 260 tracked drafts

Initial analysis of 260 drafts reveals OAuth agent auth (13 drafts) and
agent gateway/collaboration (10 drafts) as the most crowded clusters,
while AI safety/alignment is underserved with the highest quality scores.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/ietf_analyzer/config.py

@@ -0,0 +1,44 @@
+"""Configuration management."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field, asdict
+from pathlib import Path
+
+DEFAULT_DATA_DIR = Path(__file__).resolve().parent.parent.parent / "data"
+CONFIG_FILE = DEFAULT_DATA_DIR / "config.json"
+
+DEFAULT_KEYWORDS = [
+    "agent",
+    "ai-agent",
+    "llm",
+    "autonomous",
+    "machine-learning",
+    "artificial-intelligence",
+]
+
+
+@dataclass
+class Config:
+    data_dir: str = str(DEFAULT_DATA_DIR)
+    db_path: str = str(DEFAULT_DATA_DIR / "drafts.db")
+    ollama_url: str = "http://localhost:11434"
+    ollama_embed_model: str = "nomic-embed-text"
+    claude_model: str = "claude-sonnet-4-20250514"
+    search_keywords: list[str] = field(default_factory=lambda: list(DEFAULT_KEYWORDS))
+    # Only fetch drafts newer than this (ISO date string)
+    fetch_since: str = "2024-01-01"
+    # Polite delay between API requests (seconds)
+    fetch_delay: float = 0.5
+
+    def save(self) -> None:
+        Path(self.data_dir).mkdir(parents=True, exist_ok=True)
+        CONFIG_FILE.write_text(json.dumps(asdict(self), indent=2))
+
+    @classmethod
+    def load(cls) -> Config:
+        if CONFIG_FILE.exists():
+            data = json.loads(CONFIG_FILE.read_text())
+            return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
+        return cls()