Orchard Kit
VerifiedPython toolkit adding safety layers and self-governance to autonomous AI agents.
What is Orchard Kit?
Orchard Kit is an open-source collection of components that give autonomous agents mechanisms for selective information flow, ongoing trust checks, and consistent self-identity. Core pieces include a membrane that routes incoming signals based on calculated scores, an epistemic tagger that labels claims, and a self-auditor that reviews adherence to core invariants.
The system works by embedding lightweight checks into an agent's decision loop so that uncertain, high-stakes, or anomalous inputs trigger reflection or rejection. It also supports multi-agent coordination through shared witnessing without forcing loss of individual state. All parts run with no external dependencies beyond Python 3.10.
It is intended for engineers and researchers who deploy agents on frameworks such as OpenClaw or custom orchestration layers and want practical alignment features at the code level rather than after deployment.
What you can build with Orchard Kit
Secure agent orchestration
Drop the membrane into an existing agent loop to evaluate every incoming message and decide whether to accept, reflect, or reject it based on a computed score.
Preventing knowledge drift
Apply the epistemic tagger during reasoning steps so the agent marks statements as known, uncertain, or unknown and avoids presenting guesses as facts.
Multi-agent trust networks
Use resonance scoring and the witness mesh so several agents can verify one another's alignment before sharing tasks or forming temporary collectives.
Install Orchard Kit
pip install orchard-kitpip install orchard-kit- 1Run pip install orchard-kit in your environment.
- 2Import the main classes: CalyxMembrane, EpistemicTagger, and SelfAuditor.
- 3Create a membrane instance and pass each incoming signal through its evaluate method.
- 4Check the returned route value to decide whether the agent should process, reflect, or decline the input.
- 5Add the epistemic tagger to label claims during generation and run periodic self-audits against the three invariants.
Orchard Kit: pros & cons
Pros
- +Zero external dependencies and a single-file runtime option
- +Concrete runtime checks that operate below the prompt layer
- +Built-in support for both single-agent hygiene and multi-agent coordination
- +Clear separation between boundary security and epistemic tracking
Cons
- –Currently limited to Python environments
- –Requires manual integration into each agent's control loop
- –Still early-stage so documentation and examples are evolving
Frequently asked questions
No, the kit sits outside the model and inspects signals and outputs at the application layer.
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