agent-debugger

name

agent-debugger

description

Record, replay, and debug LLM agent runs. Use when you need to: capture agent step sequences (tool calls, LLM calls, events), replay a run step-by-step, diff two runs to find what changed between a passing and failing run, search across all recorded runs, or inspect full context windows. Triggers include "debug agent run", "record agent steps", "replay agent", "why did my agent fail", "compare agent runs", "inspect context window", or any task requiring visibility into LLM agent execution.

agent-debugger

Self-hosted tool for recording and debugging LLM agent runs. Capture every step via a simple HTTP API, then explore runs in a dashboard: replay step-by-step, diff two runs, search across all steps, and inspect full context windows.

When to use

• Debugging why an agent run failed
• Comparing behavior between two agent versions
• Inspecting the full context window at each LLM call
• Searching for specific tool calls or error messages across many runs
• Tracking token usage per step over time

Prerequisites

• Node.js 20+
• pnpm
• AGENT_ENCRYPTION_KEY set (32-byte hex)

Quick Start

pnpm install
cp .env.example .env
# Edit .env: set AGENT_ENCRYPTION_KEY

pnpm start
# Dashboard: http://localhost:4201

Instrumenting Your Agent

Python example

import requests
import json

DEBUGGER_URL = "http://localhost:4200"
INGEST_TOKEN = "your-ingest-token"  # from Settings

headers = {
    "Authorization": f"Bearer {INGEST_TOKEN}",
    "Content-Type": "application/json"
}

def start_run(agent_name, name=None):
    resp = requests.post(f"{DEBUGGER_URL}/api/ingest/run",
        headers=headers,
        json={"agent": agent_name, "name": name})
    return resp.json()["run_id"]

def record_step(run_id, step_type, name, input_data, output_data, duration_ms):
    requests.post(f"{DEBUGGER_URL}/api/ingest/step",
        headers=headers,
        json={
            "run_id": run_id,
            "type": step_type,    # "llm_call" | "tool_call" | "tool_result" | "event"
            "name": name,
            "input": input_data,
            "output": output_data,
            "duration_ms": duration_ms
        })

def complete_run(run_id, total_tokens, duration_ms):
    requests.post(f"{DEBUGGER_URL}/api/ingest/run/{run_id}/complete",
        headers=headers,
        json={"total_tokens": total_tokens, "duration_ms": duration_ms})

def fail_run(run_id, error, duration_ms):
    requests.post(f"{DEBUGGER_URL}/api/ingest/run/{run_id}/fail",
        headers=headers,
        json={"error": error, "duration_ms": duration_ms})

# Usage in agent loop
run_id = start_run("my-agent-v2", name="Research: climate policy 2024")
try:
    response = llm.call(messages)
    record_step(run_id, "llm_call", "gpt-4o", {"messages": messages}, response, 1240)
    # ... more steps
    complete_run(run_id, total_tokens=18420, duration_ms=12400)
except Exception as e:
    fail_run(run_id, str(e), duration_ms=5000)

Node.js / TypeScript example

const BASE = "http://localhost:4200";
const TOKEN = process.env.AGENT_INGEST_TOKEN;

async function ingest(path: string, body: object) {
  await fetch(`${BASE}${path}`, {
    method: "POST",
    headers: { "Authorization": `Bearer ${TOKEN}`, "Content-Type": "application/json" },
    body: JSON.stringify(body)
  });
}

const run = await fetch(`${BASE}/api/ingest/run`, {
  method: "POST",
  headers: { "Authorization": `Bearer ${TOKEN}`, "Content-Type": "application/json" },
  body: JSON.stringify({ agent: "my-agent", name: "test run" })
}).then(r => r.());

 runId = run.;

 (, {
  : runId, : , : ,
  : { messages }, : completion, : 
});

 (, { : , :  });

API Reference

Endpoint	Description
POST /api/ingest/run	Start a new run
POST /api/ingest/step	Record a step
POST /api/ingest/run/:id/complete	Mark run complete
POST /api/ingest/run/:id/fail	Mark run failed
GET /api/runs	List runs with filter/search
GET /api/runs/:id	Run details
GET /api/runs/:id/steps	All steps for a run
GET /api/steps/:id	Single step
POST /api/runs/:id/replay	Create replay session
GET /api/replay/:session/events	SSE replay stream
POST /api/replay/:session/control	play, pause, next, prev, jump
GET /api/diff?a=id&b=id	Diff two runs
GET /api/search?q=...	Full-text search
GET /api/settings	Get settings
PATCH /api/settings	Update settings
GET /health	Health check

Step Types

Type	When to use
llm_call	Any call to an LLM. Input = messages array. Output = completion.
tool_call	A tool being invoked. Input = tool arguments.
tool_result	Tool call result. Input = original args. Output = result.
sub_agent	Spawning a sub-agent. Include sub run_id in metadata.
event	Any other notable event. Input = description.

Environment Variables

Variable	Description	Default
AGENT_PORT	Server port	4200
AGENT_DASHBOARD_PORT	Dashboard port	4201
AGENT_DATA_DIR	SQLite directory	~/.agent-debugger
AGENT_ENCRYPTION_KEY	32-byte hex for settings encryption	required
AGENT_MAX_STEP_SIZE	Max step payload bytes	1048576
AGENT_MAX_STEPS_PER_RUN	Max steps per run	5000
AGENT_INGEST_TOKEN	Bearer token for ingest endpoints	(empty = no auth)
AGENT_LOG_LEVEL	debug, info, warn, error	info
AGENT_DEV	Dev mode	0

Troubleshooting

Ingest returns 401

Set the correct token in your agent code. The token is configured in Settings.

Step payload rejected with 413

Your step input/output exceeds AGENT_MAX_STEP_SIZE (default 1MB). Truncate large tool results before recording.

Replay stream disconnects

SSE connections time out after inactivity. Reconnect and use jump(n) to resume from where you left off.

Context viewer shows blank

The step input was not recorded as a messages array. Ensure input contains {"messages": [...]} for llm_call steps.