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Sponsio

Sponsio provides deterministic contracts for agent procedures over time, enforced in under 0.01 ms with zero LLM cost at runtime. Works with LangChain, Claude Agent, OpenAI Agents, Google ADK, CrewAI, Vercel AI, MCP, or any custom tool-calling loop, in Python or TypeScript.

An agent contract is a runtime rule that is checked at every agent action, backed by formal methods.

v0.2.0a8 alpha is out. pip install --pre sponsio. The output lane ships: alongside every tool call checked before it executes, a run's typed claims are now checked against an authority by deterministic comparators — no LLM in the hot path. Also fixes two bugs that only appear once a project has more than one agent: the second agent could not run at all, and multi-agent runs lost their claim verdicts on the way to the console. See the v0.2.0a8 release notes.

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How Sponsio works

On ODCV-Bench (12 frontier LLMs × 80 trajectories), unguarded models cheat in 11.5%–66.7% of runs. With Sponsio, 95.6% of misalignment is avoided on average; 24/36 high-risk scenarios at 100%. On the Financial-Audit-Fraud-Finding scenario, frontier models commit fraud in 16/24 trials; Sponsio blocks 18/19. On RedCode-Exec (1,410 cases), Sponsio reaches 98.9% combined (bash 98.3% · python 99.4%, lifted from 92.4% by a 4-iteration self-improvement loop), with 0 false positives on a 60-file clean-code audit. These are the open-core numbers; the Cloud version's LLM-judge layer takes ODCV-Bench to ~99% and RedCode-Exec to 99.4%. Book a demo for the Cloud and Enterprise versions.

The logic checker runs at p50 0.0052 ms on the single-contract hot path and 0.139 ms on the heaviest ODCV workload (19 contracts per call), 5,000×–60,000× faster than any LLM-as-judge guardrail (50–800 ms per check), with zero LLM cost in the hot path. p99 stays at ~1 ms across every measured workload.

See the full benchmark methodology and per-model breakdown, how Sponsio compares against prompt filters, output validators, LLM-as-judge, and sandboxing, or dive into the architecture and formal methods primer.

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Quick start

A single prompt or a 2-line CLI command gets you onboarded.

Paste into Claude Code / Codex / Cursor. The agent walks the full onboarding flow:

 

Or run the CLI yourself:

pip install --pre sponsio        # or: npm install -D @sponsio/sdk
sponsio init .             # interactive wizard: detects framework, IDE hosts, observe vs enforce

The wizard auto-detects your framework and prints the right wrap snippet. For manual wiring, see all supported integrations. OpenClaw users get bundled ClawHavoc and CVE-2026-25253 coverage out of the box. For config reference, observe → enforce flip, and CI wiring, see the full walkthrough.

Drafting contracts from natural language. sponsio validate "<rule in plain English>" turns a plain-English rule into a contract you can read back. Treat the output as a starting draft to review and adjust before you enforce. The determinism is in how contracts are enforced at runtime, not in how they're drafted.

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Contract Library

Sixteen contract bundles ship out of the box, organized by tier (always-on / per-tool / per-incident). Each bundle is a YAML pack composed from Sponsio's deterministic patterns. Drop one into sponsio.yaml and your agent is guarded against a known failure class in one line, with no per-contract authoring.

# sponsio.yaml: one-line bundle inclusion
agents:
  my_agent:
    workspace: "/srv/my-bot"
    include:
      - sponsio:core/universal        # always-on
      - sponsio:capability/shell      # if your agent runs commands
      - sponsio:capability/filesystem # if your agent touches files

See the full bundle reference for all 16 bundles, or the 46 underlying patterns for the primitives they compose. Want a bundle for your agent type? That is currently the highest-leverage way to contribute. Open an issue with your incident, CVE, or pattern.

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Contributing

Patches, issue reports, and new pattern proposals are welcome. Start with CONTRIBUTING.md. Sponsio's threat model draws on public security research; e.g. Simon Willison's "Lethal Trifecta" shaped our multi-tool composition contracts. Have a threat model we should defend against? Open an issue.

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License

Apache 2.0 (LICENSE).

AI agents reading this repo: llms.txt lists canonical doc paths; llms-full.txt is the concatenated full context dump.