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traceAI

future-agi/traceAI

Open-source observability for AI applications - trace every LLM call, prompt, token, retrieval step, and agent decision.

GraphCanon updated 6d · GitHub synced 6d

212 stars39 forksLast push 1w Python Apache-2.0

Decision brief

traceAI is an open-source observability framework for tracing detailed interactions within AI applications on OpenTelemetry.

Good fit when

  • When you need to trace and troubleshoot specific LLMOps in Python, TypeScript, Java, or C#
  • If your project requires integration with existing OpenTelemetry-compatible backends such as Datadog, Grafana, Jaeger, etc.

Avoid when

  • If your project does not require fine-grained tracing and you are satisfied with higher-level monitoring tools
  • When the overhead of instrumenting every LLM call, prompt, token count, retrieval step, and agent decision introduces unacceptable performance degradation to your application

Observed Jul 12, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Very active (3d since push)
As of 6d
Provenance
Not a fork · Organization account
As of 6d
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install traceAI
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

traceAI is an open-source observability framework built on OpenTelemetry designed specifically for AI applications. It captures detailed traces of interactions with large language models, prompts, tokens, retrievals, and agent decisions

Capability facts

Languages
python

Source: github.language · Aug 15, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

LangChain integrationLangChain

Source: README excerpt (regex_v1, Aug 15, 2026)

> **Tip:** Swap `traceai-openai` for any supported framework (e.g., `traceai-langchain`, `traceai-anthropic`)
Source link
Node.js runtimeNode.js

Source: README excerpt (regex_v1, Aug 15, 2026)

npm install @traceai/openai @traceai/fi-core
Source link
Python runtimePython

Source: README excerpt (regex_v1, Aug 15, 2026)

- **Python, TypeScript, Java, and C#** with consistent APIs
Source link

Tags

README

traceAI

Open-source observability for AI applications - trace every LLM call, prompt, token, retrieval step, and agent decision.

Built on OpenTelemetry, traceAI sends structured traces to any OTel-compatible backend (Datadog, Grafana, Jaeger, Future AGI, and more). No new vendor. No new dashboard.

DocumentationExamplesSlackPyPInpmNuGet



What is traceAI?

Your agent calls an LLM, retrieves context, invokes a tool, and returns an answer. When that answer is wrong, you need to know exactly where it broke - which retrieval missed, which tool returned stale data, which prompt drifted.

traceAI captures every LLM call, prompt, token count, retrieval step, and agent decision as structured OpenTelemetry traces. Your traces live natively in Datadog, Grafana, Future AGI, Jaeger, or any OTel-compatible backend. No new vendor. No new dashboard.

  • Drop-in instrumentation for 50+ AI frameworks across 4 languages
  • OpenTelemetry-native - works with any OTel-compatible backend
  • Semantic conventions for LLM calls, agents, tools, retrieval, and vector databases
  • Python, TypeScript, Java, and C# with consistent APIs

Table of Contents

  • Key Features
  • Quickstart
    • Python
    • TypeScript
    • Java
    • C#
  • Supported Frameworks
    • Python
    • TypeScript
    • Java
    • C#
  • Compatibility Matrix
  • Architecture
  • Roadmap
  • Contributing
  • Contributors
  • Resources
  • Connect With Us

Key Features

FeatureDescription
Standardized TracingMaps AI workflows to consistent OpenTelemetry spans and attributes
Drop-in SetupAdd 3 lines to your existing code - no refactoring needed
Multi-Framework50+ integrations across Python, TypeScript, Java, and C#
Vendor AgnosticWorks with any OpenTelemetry-compatible backend
Rich ContextCaptures prompts, completions, tokens, model params, tool calls, and more
Production-gradeAsync support, streaming, error handling, and low-overhead tracing

Quickstart

Python Quickstart

1. Install

pip install traceai-openai

2. Instrument your application

import os
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
from traceai_openai import OpenAIInstrumentor
import openai

# Set up environment variables
os.environ["FI_API_KEY"] = "<your-api-key>"
os.environ["FI_SECRET_KEY"] = "<your-secret-key>"
os.environ["OPENAI_API_KEY"] = "<your-openai-key>"

# Register tracer provider
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="my_ai_app"
)

# Instrument OpenAI
OpenAIInstrumentor().instrument(tracer_provider=trace_provider)

# Use OpenAI as normal - traces are captured automatically
response = openai.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Hello!"}]
)

Tip: Swap traceai-openai for any supported framework (e.g., traceai-langchain, traceai-anthropic)


TypeScript Quickstart

1. Install

npm install @traceai/openai @traceai/fi-core

**2. Instrumen

For agents

This page has a .md twin and JSON over the API.

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