Documentation Index
Fetch the complete documentation index at: https://docs.ai-stats.phaseo.app/llms.txt
Use this file to discover all available pages before exploring further.
Use this recipe when a Python workflow needs:
- one local lookup tool
- one bounded agent loop
- one strict JSON output shape
1. Install the SDKs
pip install ai-stats-py-sdk ai-stats-agent-sdk
from ai_stats_agent import AgentTool, define_tool
lookup_policy = define_tool(
AgentTool(
id="lookup-policy",
description="Look up one internal policy by slug.",
parameters={
"type": "object",
"properties": {
"slug": {"type": "string"},
},
"required": ["slug"],
"additionalProperties": False,
},
execute=lambda input, ctx: {
"slug": input["slug"],
"summary": "Presets centralize routing, prompts, and caching defaults.",
},
)
)
3. Create the agent
from ai_stats_agent import create_agent
agent = create_agent(
{
"id": "policy-extractor",
"model": "openai/gpt-5.4-nano",
"instructions": "Use tools when needed and return concise JSON.",
"tools": [lookup_policy],
"parse_output": lambda text: text,
}
)
4. Run through the gateway-backed client
from ai_stats_agent import create_gateway_agent_client
result = agent.run(
input="Look up presets and return JSON with keys summary and recommendation.",
client=create_gateway_agent_client(),
)
print(result.output)
5. When this recipe fits
- one Python worker owns the orchestration
- the tool surface is local and deterministic
- you want strict output without building a larger orchestration layer first