spanlens 0.5.1

Spanlens SDK for Python. Agent tracing, LLM usage capture, and cost observability.

Spanlens Python SDK

LLM observability for Python. Trace agent runs, capture token usage and cost, and link calls back to your Spanlens dashboard with one line of code.

Spanlens is the open-source LLM observability platform. This is the official Python SDK. For the dashboard, signup, and proxy docs, head to spanlens.io.

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Install

pip install spanlens

# Or with provider integrations:
pip install "spanlens[openai]"
pip install "spanlens[anthropic]"
pip install "spanlens[gemini]"
pip install "spanlens[langchain]"
pip install "spanlens[all]"

Two ways to use it

Mode	Best for	Setup
Proxy	Single-call observability, drop-in for the OpenAI/Anthropic SDK	Replace base_url
SDK tracing	Multi-step agents, RAG, tool calls, manual spans	SpanlensClient(...)

You can mix both. The proxy logs the raw request; the SDK groups multiple requests into a single trace with parent / child spans.

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Mode 1. Proxy (zero-code)

Get a Spanlens API key from your dashboard, then point your provider SDK at the Spanlens proxy:

import os
from spanlens.integrations.openai import create_openai

# Reads SPANLENS_API_KEY from the environment
client = create_openai()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)

Spanlens automatically logs the request, response, latency, token counts, and cost. View them in the dashboard under Requests.

Async (FastAPI, Django async views, asyncio)

Mirror helpers return the async client:

from spanlens.integrations.openai import create_async_openai
from spanlens.integrations.anthropic import create_async_anthropic

async def handler() -> str:
    client = create_async_openai()  # openai.AsyncOpenAI
    resp = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}],
    )
    return resp.choices[0].message.content

The SDK's background ingest pool is thread-safe; you can fan out asyncio.gather of 50+ concurrent spans and trace/span POST ordering is preserved.

Tagging requests with a prompt version

from spanlens.integrations.openai import create_openai, with_prompt_version

client = create_openai()
res = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[...],
    **with_prompt_version("chatbot-system@3"),
)

The same pattern works for Anthropic. See spanlens.integrations.anthropic.

---

Mode 2. SDK tracing (multi-step agents)

Use the SDK when one user request spans multiple LLM calls, retrieval, tool use, etc. Spans appear nested under a single trace in the dashboard.

from spanlens import SpanlensClient

client = SpanlensClient(api_key="sl_live_...")

with client.start_trace("rag_pipeline", metadata={"user_id": "u_42"}) as trace:
    with trace.span("retrieve", span_type="retrieval") as span:
        docs = vector_store.similarity_search(query, k=5)
        span.end(output={"doc_count": len(docs)})

    with trace.span("generate", span_type="llm") as span:
        response = openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=build_prompt(query, docs),
            extra_headers=span.trace_headers(),  # links proxy log to this span
        )
        usage = response.usage
        span.end(
            output=response.choices[0].message.content,
            prompt_tokens=usage.prompt_tokens,
            completion_tokens=usage.completion_tokens,
            total_tokens=usage.total_tokens,
        )

When a span / trace context manager exits with an exception, the span is automatically marked error with the exception message.

Helper: observe_openai

Boilerplate-free version of the LLM span. Auto-injects trace headers, auto-parses usage, and auto-ends the span:

from spanlens import observe_openai

result = observe_openai(trace, "answer", lambda headers:
    openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages,
        extra_headers=headers,
    )
)

The same shape exists for Anthropic (observe_anthropic) and Gemini (observe_gemini).

Async support

observe() and observe_*() detect coroutines automatically. Pass an async callable and await the result:

async def go():
    result = await observe_openai(trace, "answer", lambda h:
        async_openai.chat.completions.create(..., extra_headers=h),
    )

---

Ollama (local LLMs)

observe_ollama() traces calls against a local Ollama instance. Use the OpenAI client pointed at Ollama's OpenAI-compatible endpoint, then wrap with the helper so the dashboard tags the span as provider: "ollama" instead of OpenAI:

from openai import OpenAI
from spanlens import SpanlensClient, observe_ollama

client = SpanlensClient(api_key="sl_live_...")
ollama = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",   # ignored by Ollama; required by the openai SDK
)

with client.start_trace("local_summarize") as trace:
    result = observe_ollama(trace, "llama3_summary", lambda h:
        ollama.chat.completions.create(
            model="llama3.1",
            messages=[{"role": "user", "content": "Summarize: ..."}],
            extra_headers=h,
        ),
    )

Cost is left as None because Ollama is self-hosted, so there is no per-token bill to compute.

---

LangChain / LangGraph

SpanlensCallbackHandler plugs into LangChain's standard BaseCallbackHandler contract, so it works for plain LangChain chains, LCEL pipelines, and LangGraph compiled graphs without code changes. Every LLM / chain / tool / retriever node becomes a span with the run-id tree mirroring the graph topology.

from spanlens import SpanlensClient
from spanlens.integrations.langchain import SpanlensCallbackHandler

client = SpanlensClient(api_key="sl_live_...")
handler = SpanlensCallbackHandler(client=client)

# LangChain / LCEL
result = chain.invoke({"input": "Hello"}, config={"callbacks": [handler]})

# LangGraph
graph = workflow.compile()
result = graph.invoke({"input": "Hello"}, config={"callbacks": [handler]})

Attach to an existing trace to nest the chain under a larger workflow:

with client.start_trace("agent_run") as trace:
    handler = SpanlensCallbackHandler(client=client, trace=trace)
    chain.invoke({"input": "..."}, config={"callbacks": [handler]})
    # ... other steps in the same trace ...

The handler depends on langchain-core at runtime. Either install the spanlens[langchain] extra above, or any LangChain extras you already use will bring it in.

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Configuration reference

SpanlensClient(
    api_key="sl_live_...",        # required
    base_url=None,                 # default: https://spanlens-server.vercel.app
    timeout_ms=3000,               # ingest timeout per call
    silent=True,                   # swallow errors so observability never crashes user code
    on_error=None,                 # callback (err, context) for non-silent monitoring
)

Environment variables:

• SPANLENS_API_KEY is picked up by create_openai(), create_anthropic(), and create_gemini() when api_key= is omitted.

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Why the SDK is non-blocking

Every trace.end() / span.end() call returns immediately. Network I/O runs on a background thread pool with a configurable timeout, so:

• Your hot path (the LLM call itself) is never slowed down.
• The Spanlens server being slow / down does not crash your app.
• Order is still preserved: a span POST always waits for its parent trace POST to finish, because the server's ownership check would otherwise 404 and the span would be silently lost.

For short-lived scripts, call client.close() before exit (or use with SpanlensClient(...) as client:) to drain the queue.

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Compatibility

• Python 3.9, 3.10, 3.11, 3.12, 3.13
• openai >= 1.0
• anthropic >= 0.18
• google-generativeai >= 0.5

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Self-hosting

Point the SDK and proxy helpers at your own deployment:

client = SpanlensClient(
    api_key="...",
    base_url="https://spanlens.mycompany.com",
)

openai = create_openai(base_url="https://spanlens.mycompany.com/proxy/openai/v1")

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License

MIT. See LICENSE.

Links

• Spanlens dashboard
• Proxy docs
• TypeScript SDK
• GitHub