Description
Feenion is an open-source AI debugger and observability platform designed to provide developers with comprehensive insights into AI systems. It offers full-DAG execution traces, token costs, latency flamegraphs, and error diagnostics specifically for LLMs, RAG pipelines, and autonomous multi-agent tool loops. Unlike traditional APMs, which only capture raw HTTP status codes or generic server errors, Feenion captures the full causal execution graph, including exact prompts, completions, tokens, calculated costs, tool arguments, and retrieval scores.
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Feenion is built to address the challenges of debugging AI systems, which are often complex distributed systems involving prompts, vector databases, and multi-step tool reasoning. It provides developers with the ability to inspect inputs, outputs, tokens, latency, and costs for each component, offering a complete X-ray vision into every prompt, retrieval chunk, tool call, and token dollar. This is achieved through a lightweight container and a Python library that runs locally, ensuring data privacy and eliminating the need for cloud vendor lock-in.
The platform is equipped with features such as flamegraph timelines to identify slow vector lookups and model latencies, a D3 execution DAG to visualize complex causality graphs, and real-time financials to calculate exact token expenditures. Feenion also supports multi-currency token and cost tracking, allowing users to switch between different currencies and customize exchange rates.
Feenion is licensed under Apache 2.0, allowing users to inspect, modify, and extend any part of the codebase. It is designed to run on your own hardware without per-seat pricing or cloud vendor lock-in, making it an ideal choice for developers seeking a self-hosted, open-source alternative for AI observability.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen Source
- DeploymentLocal
- PlatformsPython
Feenion AI Debugger's Core Features
Full-DAG execution traces
Token cost tracking
Latency flamegraphs
Error diagnostics
Runs locally
Open-source under Apache 2.0
Multi-currency support
D3 execution DAG visualization
Real-time financials
Flamegraph timelines
Zero cloud vendor lock-in
Python SDK integration
Multi-tenant workspace isolation
Semantic error intelligence
Comparative trace regression diff
How to use Feenion AI Debugger?
Configure: Install the Python SDK and set up the client
Use: Run Feenion locally with Docker
Inspect: View execution traces and diagnostics
Optimize: Analyze latency and cost metrics
Feenion AI Debugger's Use Cases
- AI Debugging
- Cost Management
- Latency Analysis
- Data Privacy
- Open-Source Development
