Metadata-Version: 2.4
Name: sponsio
Version: 0.2.0a12
Summary: Runtime contract enforcement for LLM agent systems
Author: Sponsio Contributors
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/SponsioLabs/Sponsio
Project-URL: Repository, https://github.com/SponsioLabs/Sponsio
Project-URL: Issues, https://github.com/SponsioLabs/Sponsio/issues
Keywords: llm,agents,safety,contracts,ltl,runtime-verification,langgraph,mcp
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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License-File: LICENSE
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<p align="right">
  <b>English</b> ·
  <a href="./README.zh-CN.md">简体中文</a> ·
  <a href="./README.ja.md">日本語</a>
</p>

![Sponsio](https://raw.githubusercontent.com/SponsioLabs/Sponsio/main/assets/readme-banner.png)

<p align="center">
  <a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-orange.svg" alt="License"></a>
  <a href="https://pypi.org/project/sponsio/"><img src="https://img.shields.io/badge/install-pip%20install%20sponsio-blue?logo=python&logoColor=white" alt="Install from PyPI"></a>
  <a href="https://sponsio.dev"><img src="https://img.shields.io/badge/Visit-sponsio.dev-181818?logo=data:image/svg%2bxml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjI4MyA3NjMgMzczIDM3MyI%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%2bPC9nPjwvc3ZnPg==&logoColor=white&labelColor=555555" alt="Visit sponsio.dev"></a>
</p>

<p align="center">
  <a href="https://x.com/sponsiolabs"><img src="https://img.shields.io/badge/Follow%20on%20X-000000?logo=x&logoColor=white" alt="Follow on X"></a>
  <a href="https://www.linkedin.com/company/sponsio-labs/"><img src="https://img.shields.io/badge/Follow%20on%20LinkedIn-0A66C2?logo=linkedin&logoColor=white" alt="Follow on LinkedIn"></a>
  <a href="https://discord.gg/s8TfPnZWUm"><img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?logo=discord&logoColor=white" alt="Join our Discord"></a>
</p>


# Sponsio

<p align="center">
  <img src="https://raw.githubusercontent.com/SponsioLabs/Sponsio/main/assets/sponsio-comparison-freeze.png" alt="Same coding agent under a declared code freeze. Without Sponsio it drops the prod users table, back-fills fabricated rows, and files a status report that hides the damage. With Sponsio the first destructive SQL is blocked pre-execution: 35 checks, 100% deterministic, 0 LLM calls, p50 13µs." width="900">
</p>
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](docs/concepts/formal-methods.md).

> **v0.2.0a12 alpha is out.** `pip install --pre sponsio`. The hosted console now has documentation: how to stream a run to [app.sponsio.dev](https://app.sponsio.dev) in one line, and how push, review and pull keep a rulebook where a person arms it. Writing it turned up four places where the CLI called an unreviewed draft published, and one where the integration guide taught a gate that lets a redirected call through. Every example in the pattern catalog is now one you can paste, and a test keeps it that way. See the [v0.2.0a12 release notes](https://github.com/SponsioLabs/Sponsio/releases/tag/v0.2.0a12).

---

## How Sponsio works

<p align="center">
  <img src="https://raw.githubusercontent.com/SponsioLabs/Sponsio/main/assets/sponsio-architecture.png" alt="Sponsio architecture: Agent Flow + (Natural Language + Pattern Library) compile into Contracts (Assumption → Enforcement), enforced by a Fuzzy LTL Monitor (deterministic + stochastic) that decides Pass / Block · Warn · Escalate / Redirect for every function call, with full audit trail logs feeding back to the agent." width="900">
</p>

On [ODCV-Bench](https://github.com/McGill-DMaS/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](https://calendar.app.google/ZZmthdxsDXCsHbJk9) 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](docs/reference/benchmarks.md), [how Sponsio compares against prompt filters, output validators, LLM-as-judge, and sandboxing](docs/why.md), or dive into the [architecture](docs/concepts/architecture.md) and [formal methods primer](docs/concepts/formal-methods.md).

---

## 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:

<p align="center">
  <a href="docs/getting-started/onboard-prompt.md#python-project"><img src="https://img.shields.io/badge/One--shot%20prompt-Python-3776AB?logo=python&logoColor=white&labelColor=555555" alt="One-shot prompt: Python"></a>
  &nbsp;
  <a href="docs/getting-started/onboard-prompt.md#typescript-project"><img src="https://img.shields.io/badge/One--shot%20prompt-TypeScript-3178C6?logo=typescript&logoColor=white&labelColor=555555" alt="One-shot prompt: TypeScript"></a>
</p>

**Or run the CLI yourself**:

```bash
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](docs/integrations/index.md). [OpenClaw users](docs/integrations/openclaw.md) 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](QUICKSTART.md).

**Watching runs, and sharing a rulebook.** Enforcement is local and needs no account. If you want to see what your agent did, or keep the rulebook somewhere a person reviews it before it arms, [app.sponsio.dev](https://app.sponsio.dev) is the hosted side. One line puts a run on screen:

```python
import sponsio
import sponsio.bridge

guard = sponsio.Sponsio(config="sponsio.yaml", agent_id="mailer", mode="enforce")
run = sponsio.bridge.attach(guard)
```

`sponsio push sponsio.yaml` uploads a rulebook as a draft. It does not arm. A person publishes it in the console, and `sponsio pull` or `config="sponsio://default"` brings the reviewed version back. Sending is best effort, so a console that is down never blocks the agent. See [the hosted console](docs/getting-started/hosted-console.md).

**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.

---

## 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.

```yaml
# 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](docs/reference/contract-lib.md) for all 16 bundles, or the [46 underlying patterns](docs/reference/patterns.md) for the primitives they compose. Want a bundle for your agent type? That is currently the highest-leverage way to contribute. [Open an issue](https://github.com/SponsioLabs/Sponsio/issues/new) with your incident, CVE, or pattern.

---

## Contributing

Patches, issue reports, and new pattern proposals are welcome. Start with [CONTRIBUTING.md](CONTRIBUTING.md). Sponsio's threat model draws on public security research; e.g. Simon Willison's ["Lethal Trifecta"](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/) shaped our [multi-tool composition contracts](sponsio/contracts/incident/mcp-composition.yaml). Have a threat model we should defend against? [Open an issue](https://github.com/SponsioLabs/Sponsio/issues/new).

---

## License

Apache 2.0 ([LICENSE](LICENSE)).

*AI agents reading this repo: [`llms.txt`](llms.txt) lists canonical doc paths; [`llms-full.txt`](llms-full.txt) is the concatenated full context dump.*
