AgentGuard — Python SDK

Runtime guardrails and observability for LLM and agent applications.

One call to init() instruments your provider clients. Every LLM call is then scanned against the guardrails you configure in the AgentGuard console, and traced — prompts, responses, token usage, cost, and any guardrail that fired.

pip install actaclad-agentguard

import agentguard

agentguard.init(
    public_key="pk-...",
    secret_key="sk-...",
    base_url="https://your-agentguard-host",
    project_id="your-project-id",
)

That is the whole setup. Your existing OpenAI, Anthropic, Gemini or LiteLLM calls are now guarded and traced — no call sites change.

Scoping a request

policy() attaches identity to the work inside it and opens the trace, so an agent run is one trace rather than a scatter of unlinked calls.

with agentguard.policy(
    user_id="u-123",
    session_id="s-456",
    feature="support-triage",
    metadata={"tenant": "acme"},
):
    answer = my_agent.invoke(question)

Everything inside — guarded LLM calls, tool calls, agent steps — lands in one trace, with the identity recorded once at the top and filterable in the console.

Agent frameworks

LangChain and LangGraph are traced automatically inside a policy() scope. No callback wiring:

with agentguard.policy(user_id=user_id, feature="triage"):
    agent.invoke({"messages": [{"role": "user", "content": question}]})

Pass the handler yourself only if you want to — agentguard.langchain_handler(). Opt out with policy(autotrace=False).

Other frameworks:

agentguard.instrument_crewai()          # pip install "actaclad-agentguard[crewai]"
agentguard.instrument_openai_agents()   # pip install "actaclad-agentguard[openai-agents]"

Guardrails

Guardrails are configured server-side per project and enforced in the SDK, so a policy change takes effect without a redeploy.

A blocked call raises, so you can handle it explicitly:

try:
    with agentguard.policy(user_id=user_id, on_block="raise"):
        answer = my_agent.invoke(question)
except agentguard.AgentGuardBlocked as blocked:
    answer = "Sorry — I can't help with that."

Use on_block="refuse" to get a safe refusal response instead of an exception.

Configuration

Credentials can come from the environment instead of init() arguments:

Other init() options: environment, on_block, tracing (batch or realtime), streaming, fail, poll_interval.

API

Optional extras

pip install "actaclad-agentguard[gemini]"          # google-genai
pip install "actaclad-agentguard[anthropic]"
pip install "actaclad-agentguard[langchain]"       # LangChain / LangGraph
pip install "actaclad-agentguard[guardrails]"      # ML detectors (PII NER, injection, toxicity)

The base install keeps dependencies small; each extra is pulled in only when you need it.

Notes

• Enforcement runs in your process. Detection and the block decision happen locally, so a guarded call adds no network hop for most guardrails. Budget and rate limits are the exception — they check server-side counters so limits hold across every instance of your service.
• Content capture is off by default. Prompts and responses are only recorded when AGENTGUARD_CAPTURE_CONTENT=true.
• Failures degrade, they don't break. If telemetry or config polling fails, your application call still runs.

© ActaClad Technologies. See the AgentGuard console for documentation and project settings.