agenticmeter 0.4.1

FinOps for LLM agents: budgets, waste tracking, and caching analysis for OpenAI, Anthropic, and AWS Bedrock

agenticmeter

FinOps for LLM agents. Budget caps that actually stop runaway calls. Waste decomposition that shows where the money went. Caching analysis that tells you what to fix. Agent-loop detection for multi-agent pipelines.

Drop-in for OpenAI, Anthropic, and AWS Bedrock. Works with LangChain. One-line integration for any custom wrapper. Zero runtime dependencies in core. No backend, no account, no telemetry.

from agenticmeter import Ledger
import openai

client = openai.OpenAI()

with Ledger(budget="$2.00") as ledger:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "hi"}],
    )

print(ledger.summary())

---

Why this exists

Every other tool in this space — tokencost, LangSmith, Langfuse, Helicone, LiteLLM's cost tracking — tells you what you spent. None of them tell you:

• That 86% of your spend was waste (retries on the same prompt, truncated outputs, failed calls)
• That the same workload on gpt-4o-mini would have cost 94% less
• That your invoice_agent is stuck in a tool-calling loop, eating $0.30 per run before giving up
• That a 2,500-token system prompt is being sent uncached on every iteration when caching would cut that input cost by 90%

agenticmeter does. It's the layer that sits between your code and your monthly OpenAI/Anthropic bill, and it turns vague "this feels expensive" feelings into specific dollar-denominated decisions.

It also enforces budgets in-process. Other tools warn you tomorrow. agenticmeter raises BudgetExceeded on the next call, the moment you cross the line — so a runaway agent stops at $2.00 instead of $200.

Install

pip install agenticmeter              # core only, zero deps
pip install agenticmeter[openai]      # + OpenAI auto-tracking
pip install agenticmeter[anthropic]   # + Anthropic auto-tracking
pip install agenticmeter[bedrock]     # + AWS Bedrock auto-tracking (new in v0.4)
pip install agenticmeter[langchain]   # + LangChain callback handler
pip install agenticmeter[all]         # everything

Core has zero runtime dependencies. SDKs are optional extras — install only what you use.

Quickstart

OpenAI

from agenticmeter import Ledger
from openai import OpenAI

client = OpenAI()

with Ledger(budget="$1.00", name="my_agent") as ledger:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}],
    )

print(ledger.summary())

Anthropic

from agenticmeter import Ledger
from anthropic import Anthropic

client = Anthropic()

with Ledger(budget="$1.00") as ledger:
    response = client.messages.create(
        model="claude-3-5-haiku-20241022",
        max_tokens=100,
        messages=[{"role": "user", "content": "Hello!"}],
    )

print(ledger.summary())

LangChain (any provider it supports)

LangChain wraps responses differently than the raw SDKs, so it has its own callback handler that extracts tokens correctly and handles per-agent attribution.

from agenticmeter import Ledger
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

with Ledger(budget="$1.00") as ledger:
    cb = ledger.as_langchain_callback()

    with ledger.tag("researcher"):
        result_a = llm.invoke("research topic A", config={"callbacks": [cb]})
    with ledger.tag("writer"):
        result_b = llm.invoke("write about it", config={"callbacks": [cb]})

print(ledger.summary())

AWS Bedrock (new in v0.4)

Any boto3.client('bedrock-runtime') call inside a Ledger is auto-tracked. Works for Claude, Llama, Titan, and Nova.

import boto3
import json
from agenticmeter import Ledger

bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")

with Ledger(budget="$1.00", name="bedrock_test") as ledger:
    response = bedrock.invoke_model(
        modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
        body=json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 200,
            "messages": [{"role": "user", "content": "Hello!"}],
        }),
    )

print(ledger.summary())

Prompt caching fields for Claude on Bedrock (cache_read_input_tokens, cache_creation_input_tokens) are extracted automatically. Response bodies remain readable after tracking — the tracker uses a replay wrapper so downstream code that does response['body'].read() still works.

Custom wrappers (Azure, Vertex, in-house routers)

For providers or internal wrappers not natively supported, use the @track_llm_call decorator. Instrument once, track forever.

from agenticmeter import track_llm_call, Ledger

def extract_usage(response, args, kwargs):
    return {
        "provider": "anthropic",
        "model": kwargs["model"],
        "input_tokens": response["usage"]["input_tokens"],
        "output_tokens": response["usage"]["output_tokens"],
    }

@track_llm_call(extract_usage=extract_usage)
def my_bedrock_wrapper(prompt, model, max_tokens):
    # Your existing wrapper that calls boto3 or any provider
    return call_bedrock_or_whatever(...)

with Ledger(budget="$1.00") as ledger:
    my_bedrock_wrapper("hi", model="claude-3-5-sonnet-20241022-v2:0", max_tokens=100)

print(ledger.summary())

What a report looks like

Real output from a multi-agent customer support pipeline:

Ledger Summary (customer_support_agent)
=======================================
Total spent:      $0.0807 / $5.0000
Total calls:      9  (8 ok, 1 failed)
Total tokens:     13,650 in / 4,352 out

By provider:
  anthropic     $0.0420
  openai        $0.0387

By tag:
  rag_agent     $0.0480  (4 calls)
  writer        $0.0327  (2 calls)
  classifier    $0.0000  (3 calls)

Agent loops detected:
  rag_agent     16 calls, $0.0480  (context grew 22.0x: 500 -> 11,000 tokens, stuck)

Waste:            $0.0697 (86.4% of spend)
  retries       $0.0557  (11,648 tokens)
  truncated     $0.0140  (2,524 tokens)
  Top repeated prompts:
    rag_query    x4  $0.0420
    answer       x2  $0.0382
  Errors:       RateLimitError=1

Caching:          11.7% hit rate, saved $0.0001
  Read tokens:  1,800

Missed caching opportunities: ~$0.0203 potential savings
  rag_query    x4 @ ~2,500 tokens could save $0.0184
  answer       x2 @ ~1,500 tokens could save $0.0019

Cheaper alternatives (same workload):
  openai/gpt-4o-mini                         $0.0047  (save 94%)
  anthropic/claude-3-haiku-20240307          $0.0089  (save 89%)
  openai/gpt-3.5-turbo                       $0.0134  (save 83%)

Five things you can act on immediately, all from one report.

Core features

Hard budget enforcement. BudgetExceeded is raised the moment a tracked operation crosses your cap. Runaway agents fail fast instead of burning credit.

Per-agent attribution via tags. Wrap blocks with ledger.tag("name") and the report shows where the money went per agent. Essential for multi-agent pipelines.

Waste decomposition. Retries, truncations, and failures are tracked separately with dollar amounts and token counts. Top repeated prompts and error types are surfaced automatically.

Counterfactual costs / cheaper alternatives. Every report shows what the same workload would have cost on smaller or different models, sorted by biggest savings first.

Prompt-caching analysis. Realized savings from cache hits are extracted from OpenAI and Anthropic responses. Missed opportunities — repeated long prompts that aren't using caching — are flagged with dollar estimates of what caching would save.

Agent-loop detection. When a tagged agent makes many LLM calls in one ledger run, the package flags it as a likely tool-calling loop, with context-growth ratio as severity.

Three ways to integrate — context manager (recommended), decorator (@track_budget), or manual ledger.record() for providers without auto-tracking.

Three ways to use it

Context manager (recommended)

with Ledger(budget="$5.00", name="nightly_batch") as ledger:
    run_my_agent()

print(ledger.summary())

Decorator

from agenticmeter import track_budget

@track_budget("$0.50", on_complete=lambda l: print(l.summary()))
def answer_question(q):
    ...

Manual recording

For providers we don't auto-track yet (Bedrock, Azure, Vertex AI, custom wrappers):

with Ledger(budget="$1.00") as ledger:
    response = my_bedrock_call(...)
    ledger.record(
        provider="anthropic",
        model="claude-3-5-sonnet-20241022",
        input_tokens=response["usage"]["input_tokens"],
        output_tokens=response["usage"]["output_tokens"],
    )

Provider support

Provider	Auto-tracked	LangChain	Custom wrapper	Manual record
OpenAI	✅	✅	via @track_llm_call	✅
Anthropic	✅	✅	via @track_llm_call	✅
AWS Bedrock	✅ (v0.4)	✅	via @track_llm_call	✅
Azure OpenAI	via OpenAI tracker	✅	via @track_llm_call	✅
Google Vertex / Gemini	—	✅	via @track_llm_call	✅
Cohere, Mistral, everything else	—	✅	via @track_llm_call	✅

Bedrock covers Anthropic Claude, Meta Llama, Amazon Titan, and Amazon Nova via invoke_model and converse. Use pip install "agenticmeter[bedrock]" to install boto3 as well.

Native Vertex / Gemini trackers planned for v0.5. Until then, the @track_llm_call decorator handles them in one line.

How it compares

	agenticmeter	tokencost	LangSmith	Langfuse	LiteLLM
Cost calculation	✅	✅	✅	✅	✅
Budget enforcement in-process	✅	—	—	—	proxy only
Counterfactual costs (savings on other models)	✅	—	—	—	—
Waste decomposition (retries, truncations, failed)	✅	—	partial	partial	—
Missed caching detection	✅	—	—	—	—
Realized cache savings	✅	—	—	—	—
Agent-loop detection	✅	—	—	—	—
Per-agent tagging	✅	—	✅	✅	✅
Works offline / no account	✅	✅	—	self-host	✅
Zero core dependencies	✅	—	—	—	—

Limitations (v0.4)

• Streaming responses aren't auto-tracked yet. Non-streaming create() / invoke_model() calls only. Streaming planned for v0.5.
• Native Vertex / Gemini trackers still pending. Use @track_llm_call for now.
• Prompt retry detection is text-hash based. Semantically equivalent prompts with different wording are missed.
• Tool-use costs (web search, code sandboxes) aren't tracked separately from base LLM calls.

Programmatic API

Everything in summary() is also available as data:

ledger.total_cost                       # float, USD
ledger.remaining_budget                 # float or None
ledger.total_calls                      # int
ledger.successful_calls                 # int
ledger.failed_calls                     # int
ledger.total_input_tokens               # int
ledger.total_output_tokens              # int

ledger.by_tag()                         # dict: tag -> cost
ledger.calls_by_tag()                   # dict: tag -> count
ledger.wasted_cost                      # float
ledger.wasted_cost_by_category()        # dict: retries/truncated/failed -> cost
ledger.wasted_tokens()                  # dict: same shape, tokens
ledger.most_retried_prompts(n=5)        # list of {prompt_hash, calls, cost}
ledger.errors_by_type()                 # dict: ErrorClass -> count

ledger.cache_hit_rate()                 # 0.0 to 1.0
ledger.realized_cache_savings()         # float, USD
ledger.caching_summary()                # dict
ledger.missed_caching_opportunities()   # list of actionable opportunities

ledger.detect_agent_loops()             # list of {tag, calls, growth_ratio, ...}
ledger.savings_opportunities()          # list of cheaper alternatives
ledger.counterfactuals()                # dict: model -> cost for this workload
ledger.cheapest_alternative()           # (provider, model, cost) tuple

ledger.to_dict()                        # full structured data
ledger.to_json()                        # JSON string

Threading and async

Async works out of the box — contextvars propagate through awaited code and asyncio.create_task.

Threads need one line. ThreadPoolExecutor doesn't propagate contextvars to worker threads by default, so LLM calls inside workers would see get_current_ledger() == None and go untracked. Wrap the executor:

from concurrent.futures import ThreadPoolExecutor
from agenticmeter import Ledger, tracked_executor

with Ledger() as ledger:
    with ledger.tag("parallel_work"):
        with tracked_executor(ThreadPoolExecutor(max_workers=8)) as ex:
            futures = [ex.submit(worker_fn, item) for item in items]
            results = [f.result() for f in futures]

tracked_executor captures the current context at wrap time, so any tags active when you call it propagate to workers. Wrap inside the tag block you want workers to inherit.

Pricing data

Prices live in src/agenticmeter/prices.json. Standard non-batch rates. Cache rates included: OpenAI cached reads at 50% of input, Anthropic at 10% read / 125% write.

To add a model or update a price, edit the JSON — no code changes.

License

MIT.

Status

Alpha (v0.4.0). APIs may shift. Battle-tested on real multi-agent pipelines including OpenAI, Anthropic, and Bedrock. File issues with rough edges — they're useful.

Roadmap

See changelog.md for what's shipped. Coming in v0.5:

• Streaming response auto-tracking
• Native Google Vertex / Gemini tracker
• Native Azure OpenAI tracker with full parity
• CLI for retrospective analysis of saved runs
• Cost regression testing fixture for pytest