dexcost 0.1.0

Agent Unit Economics SDK — track end-to-end business-task costs for AI agents.

dexcost

Agent Unit Economics SDK — track end-to-end business-task costs for AI agents.

dexcost attributes LLM calls, non-LLM service fees, and retry waste to customers, projects, and workflows so you can answer "what does each AI task actually cost?"

Install

pip install dexcost

With all LLM provider SDKs:

pip install dexcost[all]

Quick Start

Global API (recommended)

import dexcost

dexcost.init(api_key="dx_live_...")  # or set DEXCOST_API_KEY env var
dexcost.set_context(customer_id="acme-corp")

with dexcost.task(task_type="summarise_doc") as t:
    # LLM calls are auto-captured — just use OpenAI/Anthropic/etc normally
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Summarise this document"}],
    )

    # Record non-LLM costs manually
    t.record_cost(service="pdf_parser", cost_usd="0.002")

dexcost.close()

Instance API (for multi-tracker scenarios)

from dexcost import CostTracker
from dexcost.storage.sqlite import SQLiteStorage

tracker = CostTracker(storage=SQLiteStorage("/tmp/demo.db"))

with tracker.task(task_type="summarise_doc", customer_id="acme") as t:
    t.record_llm_call("openai", "gpt-4o", input_tokens=800, output_tokens=150)
    t.record_cost(service="pdf_parser", cost_usd="0.002")

Auto-Instrumentation

dexcost auto-instruments 6 LLM providers and 5 HTTP libraries.

LLM Providers

Provider	Package	Auto-Patched Method
OpenAI	openai	chat.completions.create (sync + async)
Anthropic	anthropic	messages.create (sync + async)
LiteLLM	litellm	completion / acompletion
Google Gemini	google-genai	models.generate_content
AWS Bedrock	boto3 (botocore)	invoke_model
Cohere	cohere	chat / generate

Every LLM call inside a tracked task is captured automatically — cost, tokens, latency, model, provider. No manual record_llm_call needed.

HTTP Libraries (Non-LLM Cost Capture)

Library	What's Patched
requests	Session.send
httpx	Client.send
aiohttp	ClientSession._request
botocore (boto3)	URLLib3Session.send
urllib3	HTTPConnectionPool.urlopen

HTTP calls to domains in the 163-service catalog (Pinecone, Twilio, SendGrid, Stripe, Firecrawl, Exa, etc.) are automatically captured as external_cost events with cost extracted from the response.

Controlling Instrumentation

# Instrument only specific providers
dexcost.init(auto_instrument=["openai", "gemini"])

# Disable all auto-instrumentation
dexcost.init(auto_instrument=[])

# Disable HTTP tracking
dexcost.init(track_http=False)

Configuration

dexcost.init() Parameters

Parameter	Type	Default	Description
api_key	str	DEXCOST_API_KEY env	API key for cloud push
auto_instrument	list[str]	All 6 providers	Which LLM SDKs to patch
track_http	bool	True	Patch HTTP libraries for non-LLM cost capture
batch_size	int	100	Events per sync batch
flush_interval	float	5.0	Seconds between sync pushes
redact_fields	list[str]	None	Field names to redact from event details
hash_customer_id	bool	False	SHA-256 hash customer_id before storage
environment	str	None	Set to "development" for dev console mode
storage	str	None	Storage mode ("local" or auto-detect)
buffer_path	str	~/.dexcost/buffer.db	Path to local SQLite buffer

Environment Variables

Variable	Description
DEXCOST_API_KEY	API key (if not passed to init())
DEXCOST_ENDPOINT	Control Layer URL (default: https://api.dexcost.io)
DEXCOST_ENV	Set to development for dev console output

Task Tracking

Context Manager

with dexcost.task(task_type="resolve_ticket") as t:
    # All LLM/HTTP calls inside are automatically captured
    pass

Decorator

@tracker.track_task(task_type="generate_report", customer_id="acme")
def generate_report(data):
    # LLM calls here are tracked
    pass

Manual Start/End

t = tracker.start_task(task_type="batch_job", customer_id="acme")
# ... do work ...
t.end(status="success")

TrackedTask Methods

with dexcost.task(task_type="...") as t:
    # Record LLM call manually (usually auto-captured)
    t.record_llm_call("openai", "gpt-4o", input_tokens=800, output_tokens=150)

    # Record non-LLM cost
    t.record_cost(service="pinecone", cost_usd="0.001")

    # Record usage (cost computed from registered rates)
    t.record_usage(service="s3_storage", units=1024)

    # Mark a retry
    t.mark_retry(reason="rate_limit", cost_usd="0.005")

    # Link to external trace
    t.link_trace(provider="datadog", trace_id="abc123")

Customer Attribution

dexcost.set_context(customer_id="acme-corp", project_id="proj-alpha")

# All tasks created after this inherit customer_id and project_id
with dexcost.task(task_type="...") as t:
    pass  # t.task.customer_id == "acme-corp"

Dev Mode

Set DEXCOST_ENV=development or pass environment="development" to init(). In dev mode:

• Cost events are printed to the terminal
• No data is pushed to the cloud
• Useful for local development and debugging

CLI

dexcost status          # DB location, event count, sync status
dexcost rates --list    # Show registered cost rates
dexcost scan .          # Find untracked cost points in your code
dexcost scan . --generate-stubs  # Generate record_cost() stubs for manual points

Development

pip install -e ".[all]"
pip install ruff black mypy pytest

make lint        # ruff
make format      # black
make typecheck   # mypy strict
make test        # pytest

Privacy

When you connect to the Dexcost Control Layer, the SDK transmits usage data subject to our Privacy Policy.

License

MIT — see LICENSE.