traceAI
Open-source observability for AI applications - trace every LLM call, prompt, token, retrieval step, and agent decision.
GraphCanon updated 6d · GitHub synced 6d
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 PyPISimilar 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.
Source: README excerpt (regex_v1, Aug 15, 2026)
> **Tip:** Swap `traceai-openai` for any supported framework (e.g., `traceai-langchain`, `traceai-anthropic`)Source link
Source: README excerpt (regex_v1, Aug 15, 2026)
npm install @traceai/openai @traceai/fi-coreSource link
Source: README excerpt (regex_v1, Aug 15, 2026)
- **Python, TypeScript, Java, and C#** with consistent APIsSource 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.
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
| Feature | Description |
|---|---|
| Standardized Tracing | Maps AI workflows to consistent OpenTelemetry spans and attributes |
| Drop-in Setup | Add 3 lines to your existing code - no refactoring needed |
| Multi-Framework | 50+ integrations across Python, TypeScript, Java, and C# |
| Vendor Agnostic | Works with any OpenTelemetry-compatible backend |
| Rich Context | Captures prompts, completions, tokens, model params, tool calls, and more |
| Production-grade | Async 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-openaifor 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.