tracerazor 1.0.1

Token efficiency auditing and adaptive sampling for production AI agents

TraceRazor

Token efficiency auditing and adaptive sampling for production AI agents.

TraceRazor does two things:

Audit your agent's traces to find wasted tokens, detect tool misfires and reasoning loops, generate fix patches, and estimate cost savings.

Sample more reliably by running K parallel LLM candidates per step and picking the consensus winner. Improves task pass rates without changing your agent's logic.

Both features are independent. Use one, the other, or both.

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Install

pip install tracerazor

Install with optional dependencies as needed:

pip install "tracerazor[openai]"        # OpenAI adapter
pip install "tracerazor[anthropic]"     # Anthropic adapter
pip install "tracerazor[langgraph]"     # LangGraph integration
pip install "tracerazor[http]"          # HTTP mode for remote server
pip install "tracerazor[all]"           # Everything

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Audit quickstart

Record steps manually with Tracer, then call analyse() to get a report:

from tracerazor import Tracer

with Tracer(agent_name="support-agent", framework="openai") as t:
    response = llm.invoke(prompt)
    t.reasoning(response.text, tokens=response.usage.total_tokens)

    result = lookup_order(order_id="ORD-123")
    t.tool("lookup_order", params={"order_id": "ORD-123"},
           output=str(result), success=True, tokens=80)

report = t.analyse()
print(report.summary())
# TAS 81.4/100 [Good] | 2 steps, 900 tokens | Saved 140 tokens (16%)

report.assert_passes()  # raises AssertionError in CI if TAS < 70

The Tracer submits the trace to the local tracerazor binary (CLI mode) or to a running tracerazor-server (HTTP mode). Build the binary with:

cargo build --release

Or point to an existing binary:

export TRACERAZOR_BIN=/path/to/tracerazor

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Sampling quickstart

AdaptiveKNode is a drop-in replacement for a LangGraph ReAct node. It samples K parallel LLM candidates at each step and picks the consensus winner.

from tracerazor import AdaptiveKNode, openai_llm
from openai import AsyncOpenAI
from langgraph.graph import StateGraph

llm = openai_llm(AsyncOpenAI(), model="gpt-4.1")
node = AdaptiveKNode(llm=llm, tools=my_tools, k_max=5, k_min=2)

graph = StateGraph(AgentState)
graph.add_node("agent", node)
# ... add edges and compile as usual ...

result = await graph.ainvoke({"messages": [HumanMessage(content="...")]})
print(result["consensus_report"].summary())

K adapts automatically: it shrinks toward k_min when all candidates agree (saving tokens), and resets to k_max after a divergent vote or a state-mutating tool call (e.g. booking a flight, cancelling an order).

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Baselines

Use NaiveKEnsemble and SelfConsistencyBaseline to benchmark your setup:

from tracerazor import NaiveKEnsemble, SelfConsistencyBaseline

NaiveKEnsemble runs K independent full-task agents and picks the majority result. SelfConsistencyBaseline uses a single deterministic tool-calling pass, then re-samples the final response K times.

In tau-bench airline benchmarks (50 tasks, gpt-4o):

Strategy	pass^1	mean tokens	vs baseline
K=1 baseline	38%	63k	1.0x
NaiveKEnsemble (K=5)	40%	282k	4.5x
AdaptiveKNode (K=5)	46%	246k	3.9x
SelfConsistency (K=5)	48%	137k	2.2x

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Audit API

Name	Description
Tracer	Context manager for recording steps and submitting for analysis
TraceRazorClient	Lower-level client for submitting trace dicts directly
TraceRazorReport	Parsed audit result with TAS score, metrics, fixes, and savings
TraceStep	Data class for a single recorded step

Sampling API

Name	Description
AdaptiveKNode	LangGraph node with per-step adaptive parallel sampling
ExactMatchConsensus	Aggregates K branch proposals by exact-match comparison
MutationMetadata	Classifies tools as mutating vs read-only
NaiveKEnsemble	K independent full-task agents, majority vote
SelfConsistencyBaseline	K re-samples of the final response only

LLM adapters

Name	Description
openai_llm	Adapter factory for AsyncOpenAI
anthropic_llm	Adapter factory for AsyncAnthropic
mock_llm	Deterministic mock for tests and offline demos

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License

MIT. Copyright (c) 2024 Zulfaqar Hafez.