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Staso Python SDK

Observe, guard, and control your AI agents. Every tool call, LLM interaction, token count, latency, and error -- on your Staso dashboard. Guard agent actions before they execute -- block dangerous operations, modify risky inputs, escalate to humans.

pip install staso
import staso as st
from staso import guard

st.init(api_key="ak_...", agent_name="my-agent")

@st.agent(name="support-agent")
def handle_request(message: str) -> str:
    context = search_kb(message)
    return call_llm(context, message)

@st.tool(name="search_kb")
def search_kb(query: str) -> str:
    return db.search(query)

@st.tool(name="process_refund")
def process_refund(customer_id: str, amount: float) -> str:
    result = guard(tool_name="process_refund", tool_input={"amount": amount})
    if result.action == "block":
        return f"Blocked: {result.reason}"
    return do_refund(customer_id, amount)

with st.conversation("conversation-123"):
    handle_request("How do I reset my password?")

st.shutdown()

Open your dashboard. Full execution tree -- agents, tools, LLM calls, tokens, timing, errors. Guard evaluations on every tool call.

Auto-Instrument LLM Calls

pip install "staso[anthropic]"  # or "staso[openai]" or "staso[all]"

st.init() detects installed provider SDKs (OpenAI, Anthropic, LiteLLM) and patches them automatically -- every LLM call is traced with tokens, model, latency, and errors. The whole integration:

import staso as st
st.init(agent_name="my-agent")

Prefer explicit control? Set STASO_AUTO_PATCH=false and patch yourself:

st.integrations.patch_anthropic()
st.integrations.patch_openai()

Both sync and async clients. Streaming fully supported.

Guard Agent Actions

Evaluate tool calls before they execute. Four actions: allow, block, modify, escalate.

from staso import guard

result = guard(tool_name="delete_records", tool_input={"table": "users"})
if result.action == "block":
    raise PermissionError(result.reason)

Guards run automatically in Claude Code and Codex sessions -- blocking dangerous tools and modifying risky inputs in real-time. In patched LLM SDK integrations (Anthropic, OpenAI), a tool call that trips an enforce rule raises staso.GuardBlocked -- catch it in your agent loop:

try:
    response = client.chat.completions.create(model="gpt-4o", messages=messages, tools=tools)
except st.GuardBlocked as blocked:
    handle_blocked_tool(blocked.tool_name, blocked.reason)

Score Agent Runs

Evaluate completed traces against built-in or custom rules -- task completion, output quality, drift across prompt versions.

run = st.evals.run(
    scope={"session_id": "run-2026-05-10-001"},
    zero_configs=["task_completion_rate", "hallucination", "output_artifact_quality"],
    name="run-001 eval",
)
summary = st.evals.summary(run.id)
print(summary.passed, "/", summary.total)

A/B compare two runs to gate prompt or model rollouts -- st.evals.compare(v1.id, v2.id) returns per-rule deltas plus the traces that flipped.

CLI Agent Integrations

Trace Claude Code (39 tool types) and Codex (32 tool types) sessions automatically. Every tool call renders as a human-readable card on your dashboard -- diffs, terminal output, agent trees, plan steps. Not raw JSON. Guards evaluate each tool before execution.

Run the interactive wizard:

staso setup

Or pass flags directly:

staso setup --target claude-code --api-key ak_... --workspace my-workspace
staso setup --target codex --api-key ak_... --workspace my-workspace

CLI Commands

staso setup                                              # interactive setup wizard
staso setup     --target claude-code --api-key ak_...    # scripted setup
staso status                                             # show what's configured
staso sync                                               # sync hooks after upgrade
staso update                                             # upgrade package + sync
staso uninstall --target codex                           # remove hooks, clean up
staso version                                            # show version info

Docs

Python 3.11+ · Apache 2.0

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