genailit 0.1.0

Open source Python library for multi-agent web interfaces.

GenAILit

GenAILit is a small Python library for building web interfaces for multi-agent systems. It gives you a single-process, single-port runtime with FastAPI, WebSocket streaming, and an embedded DebugPanel for LLMOps-style inspection.

What it is

GenAILit is not just a chat UI. It is a lightweight runtime and adapter layer for agentic applications where you want to:

• expose an agent over the web from pure Python
• stream events and tokens over WebSocket
• inspect execution traces and metrics
• stay compatible with SageMaker Studio and other proxy-based environments

Why it is not only a chat UI

A chat UI only renders messages. GenAILit also gives you:

• a framework-agnostic event model
• telemetry for tokens, latency, provider, model, cost, errors, and retries
• adapters for different backends
• a built-in debug surface for tracing multi-agent execution

That makes it useful for building and auditing multi-agent systems, not only for chatting with them.

Installation

pip install genailit

Optional LangGraph support:

pip install "genailit[langgraph]"

Requirements:

• Python 3.10+
• pydantic>=2
• fastapi>=0.110
• uvicorn[standard]>=0.27

Quickstart

The simplest way to build an app is with @app.agent:

from genailit import GenAILitApp

app = GenAILitApp()


@app.agent
async def demo(input_data, context):
    yield "Hola "
    yield "desde "
    yield "GenAILit"


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8501)

Run it with the CLI:

genailit run app.py --host 0.0.0.0 --port 8501

The UI opens a WebSocket to the same host and port, so it works well behind SageMaker and similar proxies.

Using LangGraph

LangGraph is optional and stays outside the core runtime. Install the extra first:

pip install "genailit[langgraph]"

Then use the adapter:

from genailit import GenAILitApp
from genailit.adapters.langgraph import LangGraphAdapter

graph = ...
adapter = LangGraphAdapter(graph)
app = GenAILitApp(adapter=adapter)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8501)

See examples/langgraph_demo.py for a minimal runnable example.

Examples

• examples/function_agent.py - simplest @app.agent demo
• examples/langgraph_demo.py - minimal LangGraph integration
• examples/sagemaker_quickstart.py - SageMaker-friendly quickstart

Architecture

GenAILit is intentionally split into small pieces:

• core - the FastAPI runtime and WebSocket server
• adapters - bridge layers for agent backends
• events - the framework-agnostic GenAILitEvent contract
• telemetry - in-memory session traces and metrics
• DebugPanel - the built-in execution inspector

The core stays agnostic. Adapters translate backend-specific behavior into GenAILit events.

Event catalog

Every streamed item uses the same shape:

GenAILitEvent(name="event.name", payload={...})

Canonical event names for 0.1.x:

Event	Recommended payload
session.started	{"session_id": str, "run_id": str | None}
session.ended	{"session_id": str, "run_id": str | None}
agent.token	{"delta": str}. Legacy {"token": str} is still accepted by the UI and telemetry.
agent.message	{"content": str}. Legacy {"message": str} is still accepted.
node.started	{"name": str} or {"session_id": str, "run_id": str | None} for root execution nodes.
node.ended	{"name": str} or {"run_id": str | None} for root execution nodes.
tool.started	{"name": str} or {"tool_name": str}.
tool.ended	{"name": str} or {"tool_name": str}.
llm.started	{"provider": str, "model": str, "metadata": {"source": str}, "timing": {"latency_ms": None, "ttft_ms": None}}.
llm.ended	{"provider": str, "model": str, "usage": {...}, "metadata": {"source": str}, "timing": {...}}.
metrics.updated	{"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}, "provider": str, "model": str, "timing": {"latency_ms": float, "ttft_ms": float | None}, "cost": {"cost_usd": float}}.
error	{"message": str, "type": str}.

Payloads may include fewer fields when the backend cannot provide them. Raw backend payloads are not included unless an adapter explicitly enables include_raw.

SageMaker Studio

GenAILit is designed to work in SageMaker Studio with:

• single-process execution
• single-port serving
• no Node or Vite runtime
• host 0.0.0.0
• port 8501

That keeps the app simple to install with pip and avoids frontend build steps in runtime.

Telemetry and LLMOps

GenAILit tracks observability data such as:

• input tokens
• output tokens
• total tokens
• provider
• model
• latency
• TTFT
• cost
• error count
• retry count

The DebugPanel surfaces those metrics alongside execution events and a basic execution tree. When real usage metadata is available, GenAILit prefers it over token estimates.

TelemetryStore.get_session_metrics(session_id) returns:

{
    "total_events": int,
    "input_tokens": int,
    "output_tokens": int,
    "total_tokens": int,
    "model": str | None,
    "provider": str | None,
    "latency_ms": float | None,
    "ttft_ms": float | None,
    "cost_usd": float | None,
    "error_count": int,
    "retry_count": int,
    "estimated": {"tokens": bool, "cost": bool},
}

Telemetry precedence:

1. Real usage wins: payload.usage.input_tokens, payload.usage.output_tokens, and payload.usage.total_tokens.
2. Legacy aliases are used next: prompt_tokens, completion_tokens, tokens_in, and tokens_out.
3. If no token metadata exists, output tokens are estimated from agent.token or agent.message text.

Provider and model are resolved from payload.provider, payload.model, payload.metadata.ls_provider, payload.metadata.ls_model_name, and payload.response_metadata.model_name. Cost is never estimated; cost_usd is only populated from explicit payload.cost.cost_usd or payload.cost_usd. Latency and TTFT prefer explicit payload.timing values and otherwise fall back to in-memory session timestamps.

Public API stability

For 0.1.x, the stable surface is:

• GenAILitEvent(name, payload)
• GenAILitApp(adapter=None), @app.agent, app.asgi_app, and app.run(...)
• AdapterContext and BaseAgentAdapter.stream(input_data, context)
• TelemetryStore.record, extend, snapshot, clear, get_session_trace, and get_session_metrics
• LangGraphAdapter(graph, input_key="messages", stream_mode=None, include_raw=False)
• genailit run app.py --host 0.0.0.0 --port 8501

Privacy defaults

GenAILit avoids storing sensitive content by default:

• raw payloads are not persisted unless explicitly requested
• prompts are not stored in new telemetry structures by default
• message bodies and token text are only shown where needed for the live UI

This keeps the default footprint small while still supporting inspection when you enable it.

Status

GenAILit is experimental. The public API may change as the library grows. The current goal is to keep the runtime small, stable in SageMaker, and easy to reason about.