datagol-lens

Python SDK for DataGOL Lens: traces, token usage, cost and budgets for LLM applications. Wrap your Anthropic or OpenAI client, or attach a LangChain callback, and every call shows up in Lens as a trace with its spans.

pip install datagol-lens
pip install "datagol-lens[langchain]"   # for the LangChain / LangGraph handler

Configure

Both can also be passed to DatagolLensClient(api_url=..., api_key=...).

Trace one call per request

import anthropic
from datagol_lens import DatagolLensClient

sdk = DatagolLensClient()
client = sdk.wrap(anthropic.Anthropic(), project_id="<project-id>", name="support-bot")

resp = client.messages.create(
    model="claude-haiku-4-5-20251001",
    messages=[{"role": "user", "content": "What is Python?"}],
    max_tokens=200,
)

The same works for openai.OpenAI().

Trace a multi-step run

from datagol_lens import DatagolLensClient, TraceContext

sdk = DatagolLensClient()

with TraceContext(sdk, "<project-id>", name="research-agent",
                  session_id="sess-abc", user_id="user-123") as ctx:
    llm = ctx.wrap(anthropic.Anthropic())      # each call becomes a span
    resp = llm.messages.create(model="claude-haiku-4-5-20251001", max_tokens=300,
                               messages=[{"role": "user", "content": "Summarize recent AI news"}])
    ctx.add_span(name="vector_search", span_type="retriever",
                 input={"query": "AI news"}, output={"chunks": 2})
    ctx.set_output({"summary": resp.content[0].text})
# the trace is sent when the block exits

LangChain and LangGraph

from datagol_lens import DatagolLensClient, TraceContext
from datagol_lens.langchain import DatagolLensCallbackHandler

with TraceContext(DatagolLensClient(), "<project-id>", name="langchain-agent") as ctx:
    handler = DatagolLensCallbackHandler(ctx)
    answer = chain.invoke("What is Python?", config={"callbacks": [handler]})

Every chat-model call is recorded as an llm span with its token usage, and every tool call as a tool span. Attach the same handler to a LangGraph agent's config.

Budgets and attribution

Pass company_id, agent_key and user_id to TraceContext and Lens attributes the trace's cost to that company, agent and user. Before a run, check the budgets:

from datagol_lens import BudgetExceeded

result = sdk.check_budget("<project-id>", company_id="acme", user_id="u-1", agent_key="support-bot")
if not result["allowed"]:
    raise BudgetExceeded("over budget", result)

Lens prices each span from its model and token counts, so the SDK only reports usage.

License

MIT