proof

smithery.ai/duna-spice-skay/proof↗

# Proof Code-validated pattern intelligence from pydantic-ai's actual source code. ## What It Does Proof extracts working patterns from real library code via AST analysis — not from documentation, blog posts, or human guesses. 391 patterns from pydantic-ai and pydantic, mapped to a knowledge graph of 1,251 nodes and 8,911 edges. When you ask Proof to build an agent, it assembles code from patterns the library's own code structure validates. If a pattern fails, your feedback makes the network smarter. ## Quick Start No API keys. No auth. No configuration needed. Connect via Smithery CLI npx -y @smithery/cli add proof --transport streamable-http https://mcp.aigentys.com Then in your AI assistant: *"Run catalog() to see what's available."* ## Tools (5) | Tool | Description | |------|-------------| | **catalog()** | List all libraries, pattern counts, and available data. Start here. | | **search(query)** | Find patterns by keyword (e.g., "agent", "tool", "validator"). | | **explain(symbol)** | Deep-dive into a class or function — its methods, dependencies, gotchas. | | **build_agent(description)** | Generate working agent code from validated patterns. | | **report(about, worked, details)** | Report whether generated code worked. Feedback drives confidence scores. | ## How It Works 1. **Graph Engine** loads the knowledge graph (SurrealDB, 1,251 nodes, 8,911 edges) 2. **Pattern Extractor** finds validated patterns from AST analysis of pydantic-ai source 3. **Agent Assembler** composes working code from patterns, not hallucinated guesses 4. **Confidence Scores** start at 0.5 (code-derived) and update with user feedback ## What's in the Network - **pydantic-ai v1.77.0** — 751 classes, 369 functions, 98 docs, 33 examples - **pydantic v2.12.4** — type system, validation, serialization patterns - **391 extracted patterns** — reusable code structures validated by the library itself ## Roadmap Cross-library intelligence (langchain, crewai, OpenAI) is next. Right now it's pydantic-ai + pydantic only. Your feedback is what makes the network smart. Use `report()` after every build.