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AI agents are hard to debug. Requests fly by, context builds up, and when something fails you’re left guessing. SideSeat captures every LLM call, tool call, and agent decision, then displays them in a web UI as they happen.

Quick Start

1. Start the server

   npx sideseat

2. Install the SDK

   Python

   pip install strands-agents sideseat
   # or
   uv add strands-agents sideseat

   JavaScript

   npm install ai @ai-sdk/openai @sideseat/sdk

3. Initialize in your code

   Python

   from strands import Agent
   from sideseat import SideSeat, Frameworks
   
   SideSeat(framework=Frameworks.Strands)   # add this
   
   agent = Agent()  # uses Amazon Bedrock by default
   agent("Analyze this dataset...")

   JavaScript

   import { generateText } from "ai";
   import { openai } from "@ai-sdk/openai";
   import { init } from "@sideseat/sdk";
   
   init();   // add this
   
   const { text } = await generateText({
     model: openai("gpt-5-mini"),
     prompt: "Analyze this dataset...",
     experimental_telemetry: { isEnabled: true },
   });

Open http://localhost:5388. You’ll see a live timeline of each prompt, tool call, and model response.

Framework Examples

Strands

from sideseat import SideSeat, Frameworks
from strands import Agent

SideSeat(framework=Frameworks.Strands)

agent = Agent()
response = agent("What is 2+2?")
print(response)

Vercel AI

import { init } from '@sideseat/sdk';
import { generateText } from 'ai';
import { bedrock } from '@ai-sdk/amazon-bedrock';

init();

const { text } = await generateText({
  model: bedrock('anthropic.claude-sonnet-4-5-20250929-v1:0'),
  prompt: 'What is 2+2?',
  experimental_telemetry: { isEnabled: true },
});

console.log(text);

Google ADK

import asyncio
from sideseat import SideSeat, Frameworks
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

SideSeat(framework=Frameworks.GoogleADK)

agent = Agent(
    model="gemini-2.5-flash",
    name="assistant",
    instruction="You are a helpful assistant.",
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(agent=agent, app_name="my_app", session_service=session_service)
    session = await session_service.create_session(app_name="my_app", user_id="user")
    message = types.Content(role="user", parts=[types.Part(text="What is 2+2?")])
    async for event in runner.run_async(
        session_id=session.id, user_id="user", new_message=message
    ):
        if event.content and event.content.parts:
            for part in event.content.parts:
                if hasattr(part, "text") and part.text:
                    print(part.text)

asyncio.run(main())

LangGraph

from sideseat import SideSeat, Frameworks
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

SideSeat(framework=Frameworks.LangGraph)

llm = ChatOpenAI(model="gpt-5-mini")
agent = create_react_agent(llm, tools=[])
result = agent.invoke({"messages": [("user", "What is 2+2?")]})
print(result["messages"][-1].content)

CrewAI

from sideseat import SideSeat, Frameworks
from crewai import Agent, Task, Crew

SideSeat(framework=Frameworks.CrewAI)

researcher = Agent(
    role="Researcher",
    goal="Find information",
    backstory="Expert researcher",
)

task = Task(
    description="Research AI trends",
    expected_output="Summary of trends",
    agent=researcher,
)

crew = Crew(agents=[researcher], tasks=[task])

result = crew.kickoff()
print(result)

AutoGen

from sideseat import SideSeat, Frameworks
from autogen import AssistantAgent, UserProxyAgent

SideSeat(framework=Frameworks.AutoGen)

llm_config = {"config_list": [{"model": "gpt-5-mini"}]}
assistant = AssistantAgent("assistant", llm_config=llm_config)
user = UserProxyAgent("user", human_input_mode="NEVER")
user.initiate_chat(assistant, message="Hello!")

OpenAI Agents

from sideseat import SideSeat, Frameworks
from agents import Agent, Runner

SideSeat(framework=Frameworks.OpenAIAgents)

agent = Agent(name="Assistant", instructions="You are helpful.")
result = Runner.run_sync(agent, "What is the capital of France?")
print(result.final_output)

See all supported frameworks.

AI Agent Development with MCP

SideSeat includes a built-in MCP server that gives your coding agent direct access to your agent’s traces, conversations, and costs. Connect it and let your coding tool optimize prompts, debug failures, and reduce costs using real data.

# Kiro CLI
kiro-cli mcp add --name sideseat --url http://localhost:5388/api/v1/projects/default/mcp

# Claude Code
claude mcp add --transport http sideseat http://localhost:5388/api/v1/projects/default/mcp

# OpenAI Codex
codex mcp add --transport http sideseat http://localhost:5388/api/v1/projects/default/mcp

See the MCP Server guide for Kiro, Cursor, and other clients.

Choose Your Path

Python SDK

Full Python SDK reference with configuration and examples.

JavaScript SDK

Full JavaScript SDK reference for Node.js apps.

Integrations

Connect your framework — Strands, LangGraph, CrewAI, and more.

Concepts

Understand runs, steps, and messages.