AtFlows

See what your LLM calls cost. One command. No signup.

AtFlows is a local observability tool for LLM applications. Point your SDK at it, see your costs, tokens, and latency in real-time.

python -m pip install atflows==0.1b3
atflows

AtFlows 0.1b3 is a beta release. AtFlows uses Bun (>=1.1.0) to run its local server. Install Bun before starting the command. The first launch prepares the bundled runtime in ~/.cache/atflows and needs package network access.

Dashboard: localhost:3000 · Proxy: localhost:8080

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Quick Start

1. Start AtFlows

The package, CLI and GitHub repository are named atflows.

python -m pip install atflows==0.1b3
atflows

For development from source:

git clone https://github.com/aetna000/atflows.git
cd atflows
bun install
bun run build
bun run dev

2. Point Your SDK

# Python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1")

// JavaScript
const client = new OpenAI({ baseURL: 'http://localhost:8080/v1' })

// PHP
$client = OpenAI::factory()->withBaseUri('http://localhost:8080/v1')->make();

3. View Dashboard

Open localhost:3000 to see your traces, costs, and token usage.

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Who Is This For?

• Solo developers building with OpenAI, Anthropic, etc.
• Hobbyists who want to see what their AI projects cost
• Anyone who doesn't want to pay for or set up a SaaS observability tool

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Features

Feature	Description
Cost Tracking	Real-time pricing for 2000+ models
Request Logging	See every request/response with latency
Multi-Provider	OpenAI, Anthropic, Gemini, Ollama, Groq, Mistral, and more
OpenTelemetry	Accept OTLP/HTTP traces from LangChain, LlamaIndex, Traceloop, Vercel AI SDK, etc.
Session correlation	Group multi-turn agent runs under one session via session.id (OpenInference / OTel)
Span timeline	Virtualized waterfall view; ~5k spans per trace stays smooth
Zero Config	Just run it, point your SDK, done
Local Storage	SQLite database, no external services

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Supported Providers

Use path prefixes or the X-AtFlows-Provider header:

Provider	URL
OpenAI	http://localhost:8080/v1 (default)
Anthropic	http://localhost:8080/anthropic/v1
Gemini	http://localhost:8080/gemini/v1
Ollama	http://localhost:8080/ollama/v1
Groq	http://localhost:8080/groq/v1
Mistral	http://localhost:8080/mistral/v1
Azure OpenAI	http://localhost:8080/azure/v1
Cohere	http://localhost:8080/cohere/v1
Together	http://localhost:8080/together/v1
OpenRouter	http://localhost:8080/openrouter/v1
Perplexity	http://localhost:8080/perplexity/v1

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OpenTelemetry Support

If you're using LangChain, LlamaIndex, or other instrumented frameworks:

# Python - point OTLP exporter to AtFlows
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(endpoint="http://localhost:3000/v1/traces")

// JavaScript
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http'

new OTLPTraceExporter({ url: 'http://localhost:3000/v1/traces' })

Session correlation

If your spans carry one of these attributes, AtFlows groups multiple traces into a single session and exposes them in the Sessions tab:

Convention	Attribute
OpenInference / Phoenix / Arize	session.id (recommended)
LangSmith	langsmith.trace.session_id
Traceloop / OpenLLMetry	traceloop.association.properties.session_id
Vercel AI SDK	ai.telemetry.metadata.sessionId
OTel resource fallback	service.instance.id resource attribute

For chat-thread correlation, set gen_ai.conversation.id (OTel) or traceloop.association.properties.thread_id.

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Configuration

Variable	Default	Description
PROXY_PORT	8080	Proxy port
DASHBOARD_PORT	3000	Dashboard + OTLP receiver port (npx falls back to 1337)
DATA_DIR	~/.atflows	Data directory
MAX_TRACES	10000	Max traces to retain
VERBOSE	0	Enable verbose logging

Set provider API keys as environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) if you want the proxy to forward requests.

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Development

AtFlows is a Bun workspaces monorepo (apps/server, apps/dashboard, plus six packages under packages/). Bun is required.

# Clone and install (one workspace install at root covers every package)
git clone https://github.com/aetna000/atflows.git
cd atflows && bun install

# Server (dashboard on :3000, proxy on :8080)
bun run dev

# Dashboard dev server with HMR (separate terminal, proxies /api + /ws)
bun run dev:dashboard

# Build dashboard for production (outputs to /public/)
bun run build

# Tests
bun run test                # server unit/integration
bun run --filter @atflows/dashboard test    # viewport vitest suite
bun run test:e2e            # Playwright

The dashboard is Svelte 5 + Vite 8 and builds to /public/ at the repo root. The bin entry bin/atflows.js (for the npm workspace) spawns apps/server/src/server.ts directly.

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Advanced Features

For advanced usage, see the docs/ folder:

• Observability Backends - Export to Jaeger, Langfuse, Phoenix

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License

AtFlows changes by Javad Taghia are licensed under Apache 2.0. The inherited LLMFlow code remains subject to its original MIT license and Helge Sverre copyright notice. Both license texts and the NOTICE are included with the Python package.

Credits

AtFlows is a rebrand and continuation of LLMFlow by Helge Sverre. Original code remains under the MIT license in LICENSE-MIT-LLMFLOW. Rebrand, packaging and dashboard work by Javad Taghia.