Skip to content

Peaky Peek

Local-first agent debugger with replay, failure memory, smart highlights, and drift detection.

pip install peaky-peek-server && peaky-peek --open

Why Peaky Peek?

Traditional observability tools weren't built for agent-native debugging:

Tool Focus Problem
LangSmith LLM tracing SaaS-first, your data leaves your machine
OpenTelemetry Infra metrics Blind to reasoning chains and decision trees
Sentry Error tracking No insight into why agents chose specific actions
Peaky Peek Agent-native debugging Local-first, open source, privacy by default

Peaky Peek captures the causal chain behind every action so you can debug agents like distributed systems: trace failures, replay from checkpoints, and search across reasoning paths.

Key Features

Decision Tree Visualization

Navigate agent reasoning as an interactive tree. Click nodes to inspect events, zoom to explore complex flows, and trace the causal chain from policy to tool call to safety check.

Checkpoint Replay

Time-travel through agent execution with checkpoint-aware playback. Play, pause, step, and seek to any point in the trace. Checkpoints are ranked by restore value so you jump to the most useful state.

Find specific events across all sessions. Search by keyword, filter by event type, and jump directly to results.

Failure Clustering & Multi-Agent Coordination

Adaptive analysis groups similar failures. Inspect planner/critic debates, speaker topology, and prompt policy parameters across multi-agent systems.

Session Comparison

Compare two agent runs side-by-side. See diffs in turn count, speaker topology, policies, stance shifts, and grounded decisions.

Quick Start

pip install peaky-peek-server
peaky-peek --open

This starts the server at http://localhost:8000 and opens your browser.

Three Ways to Instrument

1. Decorator (Simplest)

from agent_debugger_sdk import trace

@trace
async def my_agent(prompt: str) -> str:
    # Your agent logic here — traces are captured automatically
    return await llm_call(prompt)

2. Context Manager

from agent_debugger_sdk import trace_session

async with trace_session("weather_agent") as ctx:
    await ctx.record_decision(
        reasoning="User asked for weather",
        confidence=0.9,
        chosen_action="call_weather_api",
        evidence=[{"source": "user_input", "content": "What's the weather?"}],
    )
    await ctx.record_tool_call("weather_api", {"city": "Seattle"})
    await ctx.record_tool_result("weather_api", result={"temp": 52, "forecast": "rain"})

3. Zero-Config Auto-Patch (No Code Changes)

PEAKY_PEEK_AUTO_PATCH=true python my_agent.py

Works with PydanticAI, LangChain, OpenAI SDK, CrewAI, AutoGen, LlamaIndex, and Anthropic — no imports or decorators needed.

Project Status

  • Core debugger — local path end-to-end, stable
  • SDK — @trace, trace_session(), auto-patch for 7 frameworks
  • API — 11 routers: sessions, traces, replay, search, analytics, cost, comparison
  • Frontend — 8 specialized panels (decision tree, replay, checkpoints, search)
  • Tests — 365+ passing, CI on Python 3.10/3.11/3.12

Next Steps