TypeGuard — Runtime Type Safety for Python Agents

Stop LLM hallucinations from crashing your Python agents in production

TypeGuard intercepts every LLM response in your agentic workflows and validates it against your Python type hints (Pydantic, dataclasses, TypedDict) before execution. When validation fails, it automatically retries with corrected schemas sent back to the LLM, preventing the silent cascading failures that plague multi-step agent pipelines. Built for production ML teams who've learned the hard way that type checkers like mypy catch zero runtime issues from non-deterministic LLM outputs.

Key Benefits:

- Zero code changes required — drop-in decorator validates LLM outputs against existing type hints with automatic coercion for partial matches and nested structures

- Self-healing retry logic sends validation errors + corrected schemas back to LLM, reducing manual intervention by 80% compared to naive retry approaches

- Production-grade observability with structured audit trails showing exactly which type mismatches occurred, retry attempts, and token costs per validation cycle

MVP Scope: Phase 1: Pydantic model validation + basic retry logic for LangChain agents. Phase 2: Observability dashboard showing type failure heatmaps. Phase 3: LLM-powered self-healing with schema suggestions. Launch with 3 pre-built integrations (LangChain tools, CrewAI tasks, OpenAI function calling).

Tech Stack: Pydantic v2 (type validation & coercion), Python typing module (runtime introspection), LangChain/CrewAI/AutoGen (agent framework integrations), OpenTelemetry (structured logging), FastAPI (optional dashboard for monitoring), SQLite/PostgreSQL (audit log storage), pytest (testing framework)

Components:

- {'name': 'Type Validation Engine', 'description': 'Runtime type checker that intercepts LLM outputs and validates against Python type hints (Pydantic models, dataclasses, TypedDict). Handles partial matches, coercion, and nested structures.'}

- {'name': 'Intelligent Retry & Recovery', 'description': 'Automatic retry mechanism with exponential backoff when type validation fails. Sends validation errors back to LLM with corrected schema for self-healing responses.'}

- {'name': 'Structured Logging & Observability', 'description': 'Detailed audit trail of all type mismatches, coercions, and retries. Integrates with Datadog/New Relic for production monitoring. Tracks failure patterns to identify LLM model drift.'}

- {'name': 'Agent Middleware Layer', 'description': 'Drop-in decorator/wrapper for LangChain, CrewAI, and AutoGen agents. Minimal code changes required—wraps existing tool definitions and response handlers.'}

- {'name': 'Schema Inference & Migration', 'description': 'Auto-detects type mismatches across agent workflows and suggests schema updates. Handles empty container inference (list/dict without element types) by learning from historical data.'}

Quality assessment: Addresses a genuine production pain point (LLM output validation in agentic systems) with a concrete technical approach (type hint validation + intelligent retry), but the artifact is incomplete (truncated descriptions) and lacks depth on differentiation from existing validation libraries or novel technical contributions that would elevate it to 0.90+.