Put a privacy firewall in front of an AI app

Yagami is an open-source, self-hosted context firewall for AI agents. It classifies prompts and inherited context locally, evaluates versioned policy, governs model and tool access, and produces content-free evidence for each decision.

Try it in 60 seconds

The demo needs no API key, provider account, Ollama model, or Node.js runtime.

python -m pip install yagami
yagami demo

Open http://127.0.0.1:8000. Demo mode uses the bundled UI and a local echo backend, disables cloud routing, and still exercises policy, classification, lineage, storage, and audit decisions.

Protect an application

Initialize persistent user configuration, check the host, and start Yagami:

yagami init
yagami doctor
yagami serve

Then change one OpenAI client setting:

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:8000/v1",
    api_key="local-development-key",
)
response = client.chat.completions.create(
    model="yagami-auto",
    messages=[{"role": "user", "content": "Summarize this document."}],
    metadata={"sensitivity": "none", "purpose": "internal-documentation"},
)
print(response.choices[0].message.content)

For headless or remote deployments, configure a scoped API key and follow the deployment guide. For sensitive workflows, callers should declare sensitivity rather than relying only on detection.

The core guarantee

Once context is labeled phi, phi_medical, or secret, the default policy forces it to a local backend. The same request receives a policy passport with the policy version/hash, matched rules, lineage summary, transformations, approval evidence, and output inspection—without copying raw prompt content into the audit record.

Warning

Yagami is an enforcement component, not a compliance certification. Detection can miss sensitive data. Use caller-declared sensitivity, local-only profiles, and organization-specific tests for strict workloads.