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Phase 10: HF Space deploy β€” README frontmatter + DEPLOY.md + deploy smoke tests
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---
title: SchemaShift
emoji: πŸ”„
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
license: mit
tags:
- reinforcement-learning
- openenv
- tool-use
- schema-drift
- patronus
- scaler
---
# SchemaShift β€” Adaptive Tool Use Under Schema Drift
> **SchemaShift teaches agents to recover when the tool schema changes under them.**
An OpenEnv-compliant RL environment where workflow agents must complete multi-step SaaS admin tasks across Mail, Calendar, and CRM tools β€” while those tool APIs drift mid-episode. Trains adaptive tool use, a meta-skill that frontier LLMs lack because they're trained on static documentation.
**Team Tripod:** Yashash Sheshagiri (lead), Gajanand V Dhayagode, Likith B S
**Event:** Meta Γ— Hugging Face Γ— PyTorch OpenEnv Hackathon 2026 Β· Round 2 Β· Bangalore
**Themes hit:** Multi-Agent Β· Long-Horizon Β· World Modeling
**Sub-themes:** Patronus (Schema Drift β€” direct hit) Β· Scaler (Multi-App Enterprise)
## What it does
A trained agent handles what frontier LLMs silently fail at: when Gmail renames a field, when Stripe deprecates an endpoint, when Calendar restructures a response. Our env injects these drifts mid-episode and rewards agents that detect-inspect-adapt instead of retrying blindly.
## The claim we're testing
A Qwen 2.5 1.5B model trained with GRPO on SchemaShift beats GPT-4o-mini on drifted tasks. A small cheap model that learned skepticism beats a large expensive model that memorized docs.
## Status
Live environment with 3 scenarios (E1 onboard new hire, E2 meeting invite blast, E3 customer lookup), 3 tools (Mail 3 endpoints, Calendar 4 endpoints, CRM 4 endpoints), 7 drift types, composable rubric grader with dense step shaping, 73 tests passing. Discriminability gap verified (policy-aware heuristic 0.348 shaped vs naive 0.000). See BUILD_LOG.md for phase history and TRAINING_LOG.md for eval/training data.
## Endpoints
- `GET /` β€” metadata
- `GET /health` β€” health check
- `POST /reset` β€” start new episode, body: `{"task_id": "E1_onboard_new_hire", "seed": 0}`
- `POST /step` β€” submit action, body: `{"action": {...}, "tokens_used": 0}`
- `GET /state` β€” debug: current episode state
- `GET /tasks` β€” list available scenarios
- `GET /grader` β€” current grader breakdown
## Repo
https://github.com/Yashash4/SchemaShift
## License
MIT