Sponsio
AI agents make probabilistic decisions that can fail unpredictably in production.
Runtime guardrail library that enforces deterministic 'agent contracts' — rules checked on every agent action — in under 0.01 ms with zero LLM calls at runtime. Blocks destructive or out-of-policy tool calls (for example, destructive SQL during a declared code freeze) before they execute. Works with LangChain, Claude Agent SDK, OpenAI Agents, CrewAI, Google ADK, MCP, or any custom tool-calling loop, in Python or TypeScript.
- Enforce financial transaction limits before agent execution
- Prevent agent API calls to unauthorized external services
- Validate agent outputs against schema before database writes
AI agents make probabilistic decisions that can fail unpredictably in production. Sponsio adds deterministic safety guardrails, reducing runtime errors and compliance violations without sacrificing agent autonomy.
Teams deploying Claude agents in regulated or high-stakes environments where non-deterministic behavior creates liability or operational risk.
https://github.com/SponsioLabs/Sponsio
By SponsioLabs
How to Get It
pip install sponsio && sponsio init . # TypeScript: npm install -D @sponsio/sdk
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me enforce financial transaction limits before agent execution
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- Was zu einer Sponsion als Begleitung anziehen? — Reddit · 7 pts
- Exciting News for Non-Profit Event Sponsorships - Discover Sponsio and Join Our — Reddit
2 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 436 GitHub stars; 5 contributors; last commit 4d ago; license Apache-2.0.
Things to check
- Alpha software (v0.2.x pre-release): APIs may change, and a recent alpha fixed a fail-open bug in non-LangGraph adapters where an unsafe call ran anyway — pin to the latest pre-release. Benchmarks are vendor-published; validate enforcement against your own agent flows before relying on it in production.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 436 GitHub stars; 5 contributors; last commit 4d ago; license Apache-2.0.