Vendor-neutral LLM observability — instrument once, observe anywhere.

Warning

This project is under active development. APIs may change between releases and it is not yet production-ready. Use with that in mind.

TraceCraft is a Python observability SDK with a built-in Terminal UI (TUI) that lets you visually explore, debug, and analyze agent traces right in your terminal — no browser, no cloud dashboard, no waiting.

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The fastest path: zero code changes

If your app already uses OpenAI, Anthropic, LangChain, LlamaIndex, or any OpenTelemetry-compatible framework, TraceCraft can observe it without touching a single line of application code.

Step 1 — Install and set one environment variable:

pip install "tracecraft[receiver,tui]"

export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318

Step 2 — Start the receiver and TUI together:

tracecraft serve --tui

Step 3 — Run your existing app unchanged:

python your_app.py

Traces from any OTLP-compatible framework (OpenLLMetry, LangChain, LlamaIndex, DSPy, or any standard OpenTelemetry SDK) stream live into the TUI the moment they arrive. No init() call. No decorators. No code changes.

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All your agent runs at a glance — name, duration, token usage, and status.

Hierarchical waterfall view with timing bars. See exactly where your agent spends its time. Navigate to any LLM step and press i for the prompt, o for the response, or a for attributes.

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Path 2 — Config file + one line

When you want a persistent local setup — custom service name, JSONL export, PII redaction — drop a config file into your project and add one line to your app:

.tracecraft/config.yaml:

# .tracecraft/config.yaml
default:
  exporters:
    receiver: true         # stream to tracecraft serve --tui
  instrumentation:
    auto_instrument: true  # patches OpenAI, Anthropic, LangChain, LlamaIndex

Your app:

import tracecraft

tracecraft.init()  # reads .tracecraft/config.yaml automatically

Then start the TUI:

tracecraft serve --tui

Or, if you prefer to write traces to a file and open the TUI separately:

tracecraft tui

Note: Call tracecraft.init() before importing any LLM SDK. TraceCraft patches SDKs at import time — importing first means the patch won't apply.

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SDK decorators

For fine-grained control — custom span names, explicit inputs/outputs, structured step hierarchies — TraceCraft provides @trace_agent, @trace_tool, @trace_llm, and @trace_retrieval decorators, plus a step() context manager for inline instrumentation. See the SDK Guide for details.

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TUI Keyboard Shortcuts

Key	Action
↑ / ↓	Navigate traces
Enter	Expand trace — shows waterfall
i	View input/prompt
o	View output/response
a	View attributes and metadata
/	Filter traces
Tab	Cycle view modes
m + C	Mark and compare two traces
p	Open playground
q	Quit

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Why TraceCraft?

Feature	TraceCraft	LangSmith	Langfuse	Phoenix
Terminal UI	Yes — built-in	No	No	No
Zero-Code Instrumentation	Yes	No	No	No
Vendor Lock-in	None	LangChain	Langfuse	Arize
Local Development	Full offline	Cloud required	Self-host	Self-host
OpenTelemetry Native	Built on OTel	Proprietary	Proprietary	OTel compatible
PII Redaction	SDK-level	Backend only	Backend only	Backend only
Cost	Free & Open Source	Paid tiers	Paid tiers	Paid tiers

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Features

• Built-in Terminal UI: Explore, filter, compare, and debug traces without leaving your terminal
• Local-First: All traces stay on your machine — the TUI is fully offline
• Zero-Code OTLP Receiver: Set one env var, run tracecraft serve --tui, observe any OTLP app
• Auto-Instrumentation: Two lines capture all OpenAI, Anthropic, LangChain, and LlamaIndex calls automatically
• Decorators: @trace_agent, @trace_tool, @trace_llm, @trace_retrieval for custom tracing
• Dual-Dialect Schema: OTel GenAI and OpenInference conventions
• PII Redaction: Client-side redaction before data ever leaves your app
• Export Anywhere: Console, JSONL, SQLite, OTLP, MLflow, HTML reports

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Installation

# OTLP receiver + TUI (zero code changes path)
pip install "tracecraft[receiver,tui]"

# TUI + auto-instrumentation
pip install "tracecraft[auto,tui]"

# With specific frameworks
pip install "tracecraft[langchain,tui]"
pip install "tracecraft[llamaindex,tui]"

# All features
pip install "tracecraft[all]"

Or with uv:

uv add "tracecraft[auto,tui]"

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Framework Support

Framework	Status	Installation
OpenAI	Stable (auto)	tracecraft[auto]
Anthropic	Stable (auto)	tracecraft[auto]
LangChain	Beta	tracecraft[langchain]
LlamaIndex	Beta	tracecraft[llamaindex]
PydanticAI	Beta	tracecraft[pydantic-ai]
Claude SDK	Beta	tracecraft[claude-sdk]
Custom Code	Stable	Base package

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Configuration

import tracecraft

tracecraft.init(
    service_name="my-agent-service",
    jsonl=True,              # Enable JSONL output for the TUI
    console=True,            # Pretty-print to console
    auto_instrument=True,    # Auto-capture OpenAI/Anthropic calls
    enable_pii_redaction=True,
    sampling_rate=1.0,
)

Environment variables:

export TRACECRAFT_SERVICE_NAME=my-service
export TRACECRAFT_ENVIRONMENT=production
export TRACECRAFT_SAMPLING_RATE=0.1
export TRACECRAFT_OTLP_ENDPOINT=http://localhost:4317

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Export to Any Backend

from tracecraft.exporters import OTLPExporter

tracecraft.init(
    jsonl=True,  # Keep local TUI access
    exporters=[
        OTLPExporter(endpoint="http://localhost:4317"),  # Jaeger, Grafana, etc.
    ],
)

Supported backends: Langfuse, Datadog, Phoenix (Arize), Jaeger, Grafana Tempo, Honeycomb, any OTLP system.

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Documentation

• Full Documentation
• Terminal UI Guide
• API Reference
• Migration Guides
• Deployment Guides

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

git clone https://github.com/LocalAI/tracecraft.git
cd tracecraft
uv sync --all-extras
uv run pre-commit install
uv run pytest

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Security

See SECURITY.md for security concerns.

License

Apache-2.0 — See LICENSE for details.

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Made with care by the TraceCraft Contributors