visibe 0.1.4

AI Agent Observability Platform - Track CrewAI, LangChain, LangGraph, and more

Visibe SDK for Python

Observability for AI agents. Track costs, performance, and errors across your entire AI stack — whether you're using CrewAI, LangChain, LangGraph, AutoGen, or direct OpenAI calls.

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📦 Getting Started

Installation

pip install visibe

Setup

Set your API key:

export VISIBE_API_KEY=sk_live_your_api_key_here

Or in a .env file:

VISIBE_API_KEY=sk_live_your_api_key_here

One line to instrument everything

import visibe

visibe.init()

That's it. Every OpenAI, LangChain, LangGraph, CrewAI, AutoGen, and Bedrock client created after this call is automatically traced — no other code changes needed.

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🧩 Integrations

Framework	Auto (visibe.init())	Manual
OpenAI	✅	✅
LangChain	✅	✅
LangGraph	✅	✅
CrewAI	✅	✅
AutoGen	✅	✅
AWS Bedrock	✅	✅

Also works with OpenAI-compatible providers: Azure OpenAI, Groq, Together.ai, DeepSeek, and others.

OpenAI

import visibe
from openai import OpenAI

visibe.init()

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)
# Automatically traced — cost, tokens, duration, and content captured.

LangChain / LangGraph

import visibe
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

visibe.init()

llm = ChatOpenAI(model="gpt-4o-mini")
graph = create_react_agent(llm, tools)

result = graph.invoke({"messages": [("user", "Your prompt here")]})
# Automatically traced — agent steps, LLM calls, and tool calls captured.

Dynamic pipe chains (prompt | llm | parser) are also automatically traced. Nested sub-graphs are tracked with hierarchical agent names.

CrewAI

import visibe
from crewai import Agent, Task, Crew

visibe.init()

architect = Agent(role="Plot Architect", goal="Design mystery plots", backstory="...")
designer = Agent(role="Character Designer", goal="Create characters", backstory="...")
narrator = Agent(role="Narrator", goal="Write the story", backstory="...")

task1 = Task(description="Create a plot outline", agent=architect, expected_output="...")
task2 = Task(description="Design characters", agent=designer, expected_output="...", context=[task1])
task3 = Task(description="Write the story", agent=narrator, expected_output="...", context=[task1, task2])

crew = Crew(agents=[architect, designer, narrator], tasks=[task1, task2, task3])
result = crew.kickoff()
# Single trace with all agents, LLM calls, and per-task cost breakdown.

Training and testing runs (crew.train(), crew.test()) are traced too.

AutoGen

import visibe
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent

visibe.init()

model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")
assistant = AssistantAgent("assistant", model_client=model_client)
result = await assistant.run(task="Help me with this task")
# Automatically traced.

AWS Bedrock

import visibe
import boto3

visibe.init()

bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
response = bedrock.converse(
    modelId="anthropic.claude-3-haiku-20240307-v1:0",
    messages=[{"role": "user", "content": [{"text": "Hello!"}]}]
)
# Automatically traced.

Supports converse, converse_stream, invoke_model, and invoke_model_with_response_stream. Works with all models available via Bedrock — Claude, Nova, Llama, Mistral, and more.

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⚙️ Configuration

import visibe

visibe.init(
    api_key="sk_live_abc123",       # or set VISIBE_API_KEY env var
    frameworks=["openai", "langgraph"],  # limit to specific frameworks
    content_limit=500,              # max chars for LLM content in traces
    debug=True,                     # enable debug logging
)

Environment Variables

Variable	Description	Default
VISIBE_API_KEY	Your API key (required)	—
VISIBE_API_URL	Override API endpoint	https://api.visibe.ai
VISIBE_AUTO_INSTRUMENT	Comma-separated frameworks to auto-instrument	All detected
VISIBE_CONTENT_LIMIT	Max chars for LLM/tool content in spans	1000
VISIBE_DEBUG	Enable debug logging (1 to enable)	0

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📊 What Gets Tracked

Metric	Description
Cost	Total spend + per-agent and per-task cost breakdown
Tokens	Input/output tokens per LLM call
Duration	Total time + time per step
Tools	Which tools were used, duration, success/failure
Errors	When and where things failed
Spans	Full execution timeline with LLM calls, tool calls, and agent events

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🔧 Manual Instrumentation

For cases where you need explicit control — instrumenting a specific client, grouping calls into a named trace, or using Visibe without init().

Instrument a specific client

from visibe import Visibe

tracer = Visibe(api_key="sk_live_abc123")
tracer.instrument(graph, name="my-agent")

result = graph.invoke({"messages": [("user", "Hello")]})

Group multiple calls into one trace

from visibe import Visibe

tracer = Visibe()

with tracer.track(client, name="my-conversation"):
    r1 = client.chat.completions.create(model="gpt-4o-mini", messages=[...])
    r2 = client.chat.completions.create(model="gpt-4o-mini", messages=[...])
# Both calls sent as one grouped trace.

Remove instrumentation

tracer.uninstrument(client)

# Or use as a context manager for automatic cleanup:
with tracer.instrument(graph, name="my-agent"):
    graph.invoke(...)
# Instrumentation removed automatically on exit.

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📚 Documentation

• OpenAI integration
• LangChain integration
• CrewAI integration
• AutoGen integration
• AWS Bedrock integration

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🔗 Resources

• PyPI Package — Install the latest version
• Visibe Dashboard — View your traces and analytics

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📃 License

MIT — see LICENSE for details.