peaky-peek 0.1.15

Lightweight tracing SDK for AI agents. Capture decisions, tool calls, and LLM events with one context manager.

Agent Debugger

See why your AI agent did that. This package gives you a tracing SDK for custom agents plus integration points for PydanticAI and LangChain.

What This Package Gives You

• TraceContext for explicit tracing
• decorators for agent, tool, and LLM boundaries
• adapter entry points for supported frameworks
• configuration for local or cloud-oriented transport settings

Quick Start

pip install peaky-peek

import asyncio

from agent_debugger_sdk import TraceContext, init

init()  # Local mode by default


async def main() -> None:
    async with TraceContext(agent_name="demo_agent", framework="custom") as ctx:
        await ctx.record_decision(
            reasoning="Need external information",
            confidence=0.9,
            chosen_action="call_search_tool",
            evidence=[{"source": "user_input", "content": "What is the weather?"}],
        )


asyncio.run(main())

Run the backend locally if you want to receive and inspect events:

pip install peaky-peek-server
uvicorn api.main:app --reload --port 8000

Configuration

from agent_debugger_sdk import init

init(
    api_key="ad_live_...",            # optional
    endpoint="https://api.agentdebugger.dev",
    enabled=True,
    sample_rate=1.0,
    redact_prompts=False,
)

If no API key is set, the SDK stays in local mode and defaults to http://localhost:8000.

Integration Options

TraceContext

Use TraceContext when you want explicit control over recorded events.

import asyncio

from agent_debugger_sdk import TraceContext, init

init()


async def main() -> None:
    async with TraceContext(agent_name="my_agent", framework="custom") as ctx:
        await ctx.record_tool_call("weather_api", {"location": "SF"})
        result = {"forecast": "sunny"}
        await ctx.record_tool_result("weather_api", result=result, duration_ms=150)


asyncio.run(main())

Decorators

Use decorators when your code already has clear boundaries:

from agent_debugger_sdk import init, trace_agent, trace_tool

init()

@trace_tool(name="search_docs")
async def search_docs(query: str) -> list[str]:
    return [query]

@trace_agent(name="docs_agent", framework="custom")
async def docs_agent(query: str) -> list[str]:
    return await search_docs(query)

Adapters

PydanticAI

from pydantic_ai import Agent

from agent_debugger_sdk import init
from agent_debugger_sdk.adapters import PydanticAIAdapter

init()

agent = Agent("openai:gpt-4o")
adapter = PydanticAIAdapter(agent, agent_name="support_agent")

LangChain

from agent_debugger_sdk import TraceContext, init
from agent_debugger_sdk.adapters import LangChainTracingHandler

init()

context = TraceContext(session_id="demo", agent_name="langchain_agent", framework="langchain")
handler = LangChainTracingHandler(session_id="demo")
handler.set_context(context)

Important:

• the current LangChain path is handler-based
• init() does not currently auto-patch LangChain for zero-code instrumentation

Environment Variables

Variable	Default	Description
AGENT_DEBUGGER_API_KEY	-	API key for cloud-oriented mode
AGENT_DEBUGGER_URL	http://localhost:8000	Collector endpoint
AGENT_DEBUGGER_ENABLED	true	Enable or disable tracing
AGENT_DEBUGGER_SAMPLE_RATE	1.0	Sampling rate
AGENT_DEBUGGER_REDACT_PROMPTS	false	Redact prompts before storage
AGENT_DEBUGGER_MAX_PAYLOAD_KB	100	Max payload size for emitted events

More Docs

• Intro
• Integration
• Progress

License

MIT