fluxloop 0.1.5

FluxLoop SDK for agent instrumentation and tracing

FluxLoop SDK

FluxLoop SDK for agent instrumentation and tracing.

Installation

pip install fluxloop

Quick Start

from fluxloop import trace, FluxLoopClient

# Initialize the client
client = FluxLoopClient()

# Use the trace decorator
@trace()
def my_agent_function(prompt: str):
    # Your agent logic here
    return result

Features

• 🔍 Automatic Tracing: Instrument your agent code with simple decorators
• 📊 Rich Context: Capture inputs, outputs, and metadata
• 🔄 Async Support: Works with both sync and async functions
• 🎯 Framework Integration: Built-in support for LangChain and LangGraph

Documentation

For detailed documentation, visit https://docs.fluxloop.dev

License

Apache License 2.0 - see LICENSE file for details

Framework Integration: Decorator Ordering and Safe Instrumentation

When integrating FluxLoop with external agent frameworks (e.g., ChatKit, LangChain), follow these rules to avoid type conflicts and ensure observations are captured reliably.

• Outermost framework wrapper: If a framework provides its own decorator/wrapper that transforms a plain function into a framework-specific object (e.g., a Tool), that decorator MUST be the outermost (top) decorator. This preserves the type the framework expects.
• FluxLoop instrumentation inside: Place FluxLoop decorators inside (below) the framework decorator, or instrument from within the function body using the SDK context APIs.

Two safe patterns:

• Pattern A (safest, framework-agnostic): instrument inside the function body

  • Use get_current_context() and push/pop an ObservationData manually around your logic. This keeps signatures and framework typing unchanged.

  • Example (tool function):

    from fluxloop import get_current_context
    from fluxloop.models import ObservationData, ObservationType
    
    async def my_tool(param: str) -> dict:
        fl_ctx = get_current_context()
        obs = None
        if fl_ctx and fl_ctx.is_enabled():
            obs = ObservationData(
                type=ObservationType.TOOL,
                name="tool.my_tool",
                input={"args": {"param": param}},
            )
            fl_ctx.push_observation(obs)
    
        try:
            result = {"result": do_work(param)}
            if obs:
                obs.output = result
            return result
        except Exception as e:
            if obs:
                obs.error = str(e)
            raise
        finally:
            if fl_ctx and obs:
                fl_ctx.pop_observation()
    

• Pattern B (stacking decorators): framework outermost, FluxLoop inside

  • Example with a framework tool decorator:

    @framework_tool_decorator(...)
    @fluxloop.tool(name="tool.my_tool")
    async def my_tool(...):
        ...
    

  • Important: If you reverse the order (FluxLoop outside), the framework may see a plain function instead of its expected type and raise errors like "Unknown tool type".

LLM/streaming calls:

• For LLM calls (including async generators/streams), either:
  • Wrap the call site in a small helper decorated with @fluxloop.prompt(...), or
  • Use with fluxloop.instrument("prompt.name"): around the portion that produces model output, ensuring it runs inside the current FluxLoop context.

These patterns guarantee observations are captured (tool, generation) while keeping the external framework’s type system intact.