langchain-nebeso 0.1.0

LangChain callback handler for NeBeso AI cost tracking

langchain-nebeso

LangChain callback handler for NeBeso — open source AI cost tracking.

Track every LLM call across your LangChain app: tokens, cost, latency, and custom tags — all visible in your NeBeso dashboard in real time.

Installation

pip install langchain-nebeso

Quick start

from langchain_nebeso import NeBesoCallbackHandler
from langchain_openai import ChatOpenAI

handler = NeBesoCallbackHandler(
    api_key="nb_ak_...",                          # or set NEBESO_API_KEY env var
    tags={"feature": "support-chat", "env": "production"},
)

llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])
response = llm.invoke("Summarise our refund policy.")
# Cost is now visible in your NeBeso dashboard.

Supported providers

Works with any LangChain-supported model: OpenAI, Anthropic, Google Gemini, Mistral, Meta Llama, xAI Grok, Cohere, AWS Bedrock, and more.

Features

• Zero prompt access — only token counts, cost, and latency are recorded. Your prompts and completions are never sent to NeBeso.
• Automatic cost calculation — pricing table covers 60+ models across all major providers.
• Custom tags — tag by feature, user, environment, or anything else.
• Batched async delivery — events are queued and flushed in a background thread. Zero latency impact on your app.
• Works with chains and agents — attach the handler at the LLM level or pass it via RunnableConfig.

Usage with chains

from langchain_nebeso import NeBesoCallbackHandler
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

handler = NeBesoCallbackHandler(api_key="nb_ak_...")

llm = ChatOpenAI(model="gpt-4o-mini", callbacks=[handler])
prompt = ChatPromptTemplate.from_template("Answer in one sentence: {question}")
chain = prompt | llm

chain.invoke({"question": "What is the capital of France?"})

Usage with agents

from langchain_nebeso import NeBesoCallbackHandler
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

handler = NeBesoCallbackHandler(
    api_key="nb_ak_...",
    tags={"agent": "research", "user_id": "u_123"},
)

llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])
# ... build your agent normally

Environment variables

Variable	Description
NEBESO_API_KEY	Your NeBeso API key (alternative to passing api_key=)
NEBESO_ENDPOINT	Override API endpoint (default: https://api.nebeso.com)

Get your API key

Sign up at nebeso.com → API Keys → Create Key.

Links

• Full docs
• NeBeso dashboard
• GitHub
• PyPI: nebeso