agentlens-xray 0.3.2

Local-first AI agent debugger with fork & replay

AgentLens

Local-first AI agent debugger with fork & replay.

AgentLens captures execution traces from multi-step AI agent workflows, visualizes them in a local web UI, and lets you fork a trace at any step, edit its output, and re-execute downstream steps with real API calls to see what would have happened differently.

Think of it as Chrome DevTools for AI agents — the Network tab + time-travel debugging, but for LLM agent workflows.

Install: pip install agentlens-xray — the import name is agentlens.

Demo

https://github.com/user-attachments/assets/416a853c-4915-4a30-8590-f50fd0a7dc47

The Problem

AI agents are non-deterministic. When a multi-step agent fails at step 7 of 12, developers currently have no way to:

1. See exactly what happened at each step (tool calls, LLM reasoning, intermediate state)
2. Reproduce the failure deterministically
3. Test a fix by replaying from the failure point without re-running the entire chain

Quick Start

pip install agentlens-xray
python examples/basic_agent.py   # creates a sample trace (no API keys needed)
agentlens serve                  # opens the UI at localhost:7600

For a real agent with OpenAI:

export OPENAI_API_KEY="sk-..."
python examples/trip_planner_agent.py
agentlens serve

Usage

1. Instrument Your Code

Add decorators to your existing agent functions — zero logic changes:

import agentlens

@agentlens.trace(name="my_agent")
async def my_agent(query: str):
    data = await fetch_data(query)
    result = await analyze(data)
    return result

@agentlens.wrap_tool(name="fetch_data")
async def fetch_data(query: str) -> dict:
    return await api.search(query)

@agentlens.wrap_llm(name="analyze", model="gpt-4o-mini")
async def analyze(data: dict) -> str:
    response = await openai.chat.completions.create(...)
    return response.choices[0].message.content

There's also a context manager API:

async with agentlens.start_trace_async("my_agent") as t:
    with t.span("fetch_data", kind="tool") as s:
        data = await fetch(...)
        s.record_output(data)
    with t.span("analyze", kind="llm", model="gpt-4o") as s:
        s.record_input({"messages": [...]})
        result = await llm.complete(...)
        s.record_output(result)

2. View Traces

agentlens serve                    # Web UI at localhost:7600
agentlens serve --port 8080        # Custom port
agentlens traces                   # List recent traces in terminal
agentlens traces --last 5          # Show last 5

3. Fork & Replay

In the web UI:

1. Click a trace to see its span timeline
2. Select a span and click Fork & Replay
3. Edit the span's output in the code editor
4. Choose a replay mode
5. Click Replay from here to create a forked trace
6. View the side-by-side comparison with diff highlighting

Replay Modes

Mode	What happens	Cost	Use case
Deterministic	Only the edited span changes. Downstream spans are marked stale.	Free	Quick data annotation, bookmarking bugs
Live	All downstream spans re-execute with real API calls.	Token costs	"What would the LLM say if the tool returned different data?"
Hybrid	LLM spans re-execute live, tool spans return recorded data.	Lower token costs	Test LLM behavior with changed context, no tool side effects

Example: Your weather tool returned "sunny" but the real weather is a blizzard. Fork the weather span, change it to blizzard, select Live mode. The LLM re-generates the itinerary accounting for severe weather — with real API calls, producing genuinely different output.

Framework Integrations

AgentLens works with popular frameworks out of the box — no decorators needed.

OpenAI / Anthropic SDK (wrap your client)

pip install agentlens-xray[openai]    # or agentlens-xray[anthropic]

from openai import OpenAI
from agentlens import wrap_openai

client = wrap_openai(OpenAI())
# All chat.completions.create() calls are now traced automatically
response = client.chat.completions.create(model="gpt-4o", messages=[...])

LangChain / LangGraph

pip install agentlens-xray[langchain]

from agentlens.integrations.langchain import AgentLensCallbackHandler

with AgentLensCallbackHandler(trace_name="my_agent") as handler:
    chain.invoke(input, config={"callbacks": [handler]})
    # Full trace with LLM, tool, retrieval, and chain spans

OpenAI Agents SDK

pip install agentlens-xray[openai-agents]

from agentlens.integrations.openai_agents import install_agentlens_tracing

install_agentlens_tracing()  # One line — all agent runs traced automatically
result = await Runner.run(agent, input="Process this refund")

CrewAI

pip install agentlens-xray[crewai]

from agentlens.integrations.crewai import CrewAIHandler

handler = CrewAIHandler(trace_name="my_crew")
crew = Crew(agents=[...], tasks=[...], callbacks=[handler])
crew.kickoff()  # Crew, agent, and task spans captured

Features

• Zero-config tracing — pip install agentlens-xray and add decorators or use framework integrations
• Framework integrations — LangChain, LangGraph, OpenAI Agents SDK, CrewAI, raw SDKs
• Live replay — re-execute downstream spans with real API calls after editing a span's output
• Hybrid replay — LLM calls go live, tool calls use recorded data (no side effects)
• Async-first — non-blocking trace capture, works in both sync and async code
• Local-first — no cloud accounts, no telemetry, everything stays on your machine
• Keyboard navigable — arrow keys / j/k to browse spans, e to edit

Architecture

Your Agent Code
  --> @trace, @wrap_tool, @wrap_llm decorators capture spans
  --> Async queue --> SQLite (./.agentlens/traces.db)
  --> FastAPI serves JSON API + bundled React frontend
  --> localhost:7600

Fork & Replay (Live mode):
  --> Load original trace, apply mutations
  --> Re-import user's function, set ReplayContext
  --> Decorators intercept each span:
      Before mutation: execute normally
      At mutation: return edited output
      After mutation: execute live (real API calls)
  --> Save new trace for side-by-side comparison

Web UI Pages

• Trace List — all captured traces with status, duration, token count, cost
• Trace Detail — span timeline (left) + selected span I/O (right), keyboard navigable
• Replay Comparison — side-by-side original vs forked trace, with RE-EXECUTED / EDITED / STALE badges

Configuration

Variable	Default	Description
AGENTLENS_DB_PATH	./.agentlens/traces.db	SQLite database path (project-local by default)
AGENTLENS_PORT	7600	Default server port
AGENTLENS_ENABLED	true	Set false to disable tracing (decorators become no-ops)

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Frontend development (hot reload)
cd frontend && npm install && npm run dev

# Build frontend for production
cd frontend && npm run build

# Run tests
pytest

# Type checking
mypy src/agentlens
cd frontend && npx tsc --noEmit

Project Structure

src/agentlens/
  sdk/              # Tracing SDK (decorators, context management, SQLite writer)
  server/           # FastAPI API + static file serving
  replay/
    engine.py       # Replay dispatcher (deterministic vs live)
    live.py         # Live replay engine (re-executes user functions)
    context.py      # ReplayContext (decorators check this at runtime)
  cli.py            # CLI entry point

frontend/           # React + TypeScript + Tailwind UI (Vite)
examples/           # Working examples (basic + OpenAI trip planner)

Tech Stack

• SDK: Python 3.10+, Pydantic v2, aiosqlite, contextvars
• Server: FastAPI, Uvicorn
• Frontend: React 18, TypeScript, Tailwind CSS, CodeMirror, Vite
• Storage: SQLite (WAL mode, zero-config)

License

MIT