redlineai-sdk
Records what your AI agent did — every model call, tool call and step — and sends it to your Redline project.
The tracing API is Langfuse's Python SDK (MIT, langfuse 5.x), so an
agent is instrumented exactly as it would be for Langfuse.
pip install redlineai-sdk
import redline
from redline import observe, propagate_attributes
redline.init(api_key="rl_…", url="https://your-redline-server")
@observe(as_type="agent") # a step per call: input, output, timing, errors, nesting
def answer(question: str) -> str:
... # model calls inside are recorded too (see below)
@observe(as_type="tool")
def lookup_order(order_id: str) -> dict:
...
with propagate_attributes(session_id="chat-42", user_id="customer-7", tags=["support"]):
answer("Where is my order?") # every call in the same session_id is one conversation
api_key and url can come from REDLINE_API_KEY and REDLINE_URL instead. init() returns the client
(also get_client()); call redline.flush() before a short script exits.
What Langfuse's SDK gives you here
@observe(name=, as_type=, capture_input=, capture_output=) |
a span per call; as_type: span, generation, agent, tool, chain, retriever, embedding, evaluator, guardrail, event |
propagate_attributes(session_id=, user_id=, tags=, metadata=, version=, trace_name=) |
trace fields for everything inside the block |
get_client().start_as_current_observation(...), start_observation(...) |
spans without a decorator |
get_client().update_current_span(...), update_current_generation(model=, usage_details=, cost_details=, ...) |
fill in the current step |
LangfuseMedia(content_bytes=, content_type=) |
images, audio and files, uploaded to Redline and shown in the app; set them as an observation's input, output or metadata (update_current_generation(input=...)) — Langfuse's SDK uploads media only from those fields |
redline.openai |
Langfuse's OpenAI drop-in: from redline.openai import OpenAI |
redline.langchain.CallbackHandler |
Langfuse's LangChain handler (see LangChain below) |
Recorded without code
init() records the libraries your agent uses, each only when it is installed, the way Langfuse's integration
page for that library records it (langfuse-docs,
src/redline/_integrations.py):
| Library | How (Langfuse's page) |
|---|---|
| OpenAI and every OpenAI-compatible API (DeepSeek, Groq, Together, Ollama, xAI, Qwen, Hugging Face…), Instructor, Ragas | Langfuse's OpenAI drop-in, switched on for every client (model-providers/openai-py) |
| LangChain, LangGraph, Deep Agents | Langfuse's LangChain handler on every run; session, user and tags from propagate_attributes or the run's metadata (langfuse_session_id, langfuse_user_id, langfuse_tags) (frameworks/langchain) |
| Anthropic | OpenLLMetry's opentelemetry-instrumentation-anthropic (model-providers/anthropic) |
| LiteLLM | LiteLLM's langfuse_otel callback (frameworks/litellm-sdk) |
| Pydantic AI | its own spans, Agent.instrument_all() (frameworks/pydantic-ai) |
| Strands, Amazon AgentCore | Strands' own spans (frameworks/strands-agents) |
| Microsoft Agent Framework | its own spans with sensitive data on (frameworks/microsoft-agent-framework) |
| LiveKit Agents | its own spans, all of them sent (frameworks/livekit) |
| Agno | OpenLIT's Agno instrumentor — pip install "redlineai-sdk[agno]" (frameworks/agno-agents) |
| LlamaIndex, CrewAI, OpenAI Agents, DSPy, Haystack, smolagents, Google ADK, Claude Agent SDK, Gemini, Vertex AI, Groq, TypeSafe | OpenInference instrumentors, as on their pages; BeeAI with pip install "redlineai-sdk[beeai]" |
| Bedrock, Mistral, Cohere, Ollama, Together SDKs, MCP | OpenInference instrumentors (Langfuse has no automatic integration for these) |
| Pipecat, Mirascope, Semantic Kernel, AutoGen | as on their Langfuse pages: Pipecat PipelineTask(enable_tracing=True), Mirascope @with_langfuse(), AutoGen's own spans; their model calls through OpenAI are recorded by the drop-in |
Langfuse's pages switch on one integration at a time; init() switches on all installed ones. When one library
runs on another (LangChain, OpenAI Agents or LlamaIndex on the OpenAI client), the model client's record of a call
made inside a model call another integration records is not sent, so each model call is one generation.
Spans are sent with Langfuse's default filter (is_default_export_span), plus LiveKit's.
init(auto_instrument=False) turns all of this off.
If your app already set up OpenTelemetry, init() records into your tracer provider instead of replacing it.
Scores
from redline import get_client
langfuse = get_client()
langfuse.score_current_trace(name="user-feedback", value=1, data_type="BOOLEAN", comment="thumbs up")
langfuse.score_current_span(name="relevance", value=0.7)
langfuse.create_score(trace_id=..., observation_id=..., name="tone", value="friendly") # CATEGORICAL
Already on Langfuse's SDK
Keep your code and point it at Redline:
LANGFUSE_BASE_URL=https://your-redline-server
LANGFUSE_PUBLIC_KEY=pk-bt-<project id> # shown on the Setup page
LANGFUSE_SECRET_KEY=rl_…
Tested with: langfuse 5.0.2, langchain 1.4 / langchain-core 1.6 / langgraph 1.2 (create_agent, invoke,
ainvoke, stream), openai 3.33, openai-agents (Runner.run), scores.
1 maintainer
Metadata
1 maintainer
Release files for redlineai-sdk 0.3.2
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