agenttrace-runtime 0.1.0

Open-source runtime tracing and diagnostics for AI agent execution flows.

AgentTrace

Open-source runtime tracing and diagnostics for AI agent execution flows.

AgentTrace helps you understand what your agent actually did at runtime — not just whether the final answer looks good.

It is built for people who want to answer questions like:

• Why did the agent call this tool twice?
• Where did the latency actually come from?
• Which fallback path was triggered?
• What did the LLM see before it made this decision?
• Was the execution flow correct, redundant, or suspicious?

If you want something closer to pprof + tracing + agent diagnostics, AgentTrace is designed for that.

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Why AgentTrace

Most agent tooling focuses on one of two things:

• output evaluation — “was the answer good?”
• framework abstraction — “how do I build the agent?”

AgentTrace focuses on a different question:

What exactly happened during execution, and why did the agent behave that way?

That makes it especially useful for:

• debugging execution flow
• diagnosing redundancy and fallback behavior
• inspecting LLM prompts / responses in context
• understanding tool usage patterns
• tracing runtime state across a run

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Core capabilities

• Trace LLM / Tool / Skill execution flows
• Capture parallel, retry, fallback, and repeated-call patterns
• Record Prompt / Response / Context / Plan / Execution snapshots
• Persist runs locally and inspect them in a built-in dashboard
• Review runs with an LLM after execution
• Generate structured diagnostics: critical path, recovery chains, redundant calls, suspicious decisions

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What you get

Execution tracing

AgentTrace records a runtime trace for each run, including:

• span type
• start / end time
• latency
• status
• input parameters
• grouping and parent-child relationships

Structured state snapshots

For LLM spans, AgentTrace can capture:

• ContextSnapshot
• MemorySnapshot
• PlanSnapshot
• DecisionSnapshot
• ResumeSnapshot
• ExecutionSnapshot

Diagnostics

AgentTrace builds a diagnostics layer on top of the raw trace:

• critical path
• failed tool calls
• recovery chains
• redundant tool clusters
• suspicious decisions
• filtered review findings

LLM review

After each run, AgentTrace can ask an LLM to review the recorded execution flow and flag:

• redundant tool calls
• wrong tool choices
• suspicious fallback behavior
• unnecessary skill execution
• likely execution-flow issues

Review strictness is configurable:

• review_level=1 → tolerant
• review_level=2 → balanced (default)
• review_level=3 → strict

At review_level=1/2, the UI hides low severity findings by default. At review_level=3, all findings are shown.

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Dashboard

AgentTrace includes a local dashboard at:

• http://localhost:3500

Current UI features include:

• session list
• execution timeline
• parallel-lane view
• collapsed repeated-tool clusters
• prompt / response modal for LLM spans
• execution-state tabs
• diagnostics panel
• LLM review panel
• collapsible final agent output

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Quick start

1. Install from source

git clone https://github.com/happli-sys/AgentTrace.git
cd AgentTrace
pip install -e .

2. Patch once, trace every run

import agenttrace
from my_agent import run

agenttrace.patch(
    "my_agent.tools",
    "my_agent.skills",
    "my_agent.llm",
    llm_modules=["my_agent.llm"],
    skill_modules=["my_agent.skills"],
    review_level=2,
)

output = agenttrace.session("查北京天气并计算 1+2")(run)("查北京天气并计算 1+2")
print(output)
print(agenttrace.last_result().summary())

3. Start the dashboard

from agenttrace.dashboard.server import start_server

start_server(port=3500)

Open:

• http://localhost:3500

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Demo agent

This repo includes a demo agent that intentionally exercises multiple tracing scenarios:

• bash
• read
• grep
• calculate
• get_weather
• flaky_weather
• weather_report_skill
• parallel weather queries
• fallback to stable tools

Run it:

python examples/demo_agent/main.py

Stress prompt:

分析当前目录下的项目；bash pwd；read examples/demo_agent/tools.py；grep calculate examples/demo_agent；查北京和西安的天气，并计算1123123123+1283123；生成北京天气播报；最后总结。

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Integration model

AgentTrace works best for:

• custom Python agents with source code
• local development environments
• CLI / hook-based agents
• runtime debugging and diagnostics workflows

The default integration style is intentionally lightweight:

• patch modules once
• wrap runs with session(...)
• inspect results locally

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Project scope

AgentTrace is currently optimized as:

• a runtime tracing tool
• a local-first diagnostics tool
• a developer-facing execution inspector

It is not currently focused on being:

• a hosted eval platform
• a benchmark leaderboard
• a dataset management system
• a full SaaS observability suite

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Who this is for

AgentTrace is especially useful for:

• engineers building custom agents
• teams debugging real runtime behavior
• people who need local-first execution visibility
• anyone who wants to inspect agent decisions beyond final output quality

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Roadmap direction

Current direction is intentionally focused:

• stronger execution tracing
• better diagnostics and issue localization
• cleaner runtime state modeling
• broader integration patterns for source-based agents
• more production-friendly export / observability hooks

The goal is to keep AgentTrace useful as a general execution-flow listener, not to turn it into a bloated all-in-one platform too early.

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Still useful for objective metrics

Although AgentTrace centers on tracing and diagnostics, it still retains objective runtime metrics such as:

• total latency
• avg / p95 step latency
• tool success rate
• token usage
• estimated cost
• step efficiency
• correctness (if expected_output is provided)
• regression tracking
• comparison helpers

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Contributing

Contributions are welcome — especially around:

• new agent integrations
• richer diagnostics
• runtime state capture
• dashboard usability
• packaging and release polish

For local development:

git clone https://github.com/happli-sys/AgentTrace.git
cd AgentTrace
pip install -e ".[dev]"
pytest tests/

If you want to contribute, small focused improvements are preferred over large platform-style expansions.

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License

MIT