PandaProbe
Why Choose PandaProbe?
If yer team is deep in building ai agents and u r sick of debugging black box behaviors, this is gonna be your go-to. You get solid visibility to trace requests and eval responses directly in dev or prod without drowning in noise. Plus since its open source, you keep full control over the data and dont have to worry about pricetags hikes later on. One thing though, its not exactly a turn key solution. Self-hosting means yall got to handle the infra maintenance yourself, which adds overhead if u prefer managed services. Might be better suited for orgs with dedicated backend folks rather than small startups trying to ship fast. Bottom line, pick this if observability is critical and u want customizablty over convenience. Just prepare for a steeper initial setup compared to standard Llms wrappers.
PandaProbe is an open-source agent engineering platform that gives you deep observability into AI agent applications. Use it to trace, evaluate, monitor and debug your AI agents in development and production.
PandaProbe Introduction
What is PandaProbe?
PandaProbe is an open-source agent engineering platform giving devs deep visibility into their AI apps. It lets you trace, eval, monitor and debug your agents whether ur in dev or production. Basically its for anyone needing to see whats going on under the hood so they dont ship broken logic live. Fits right under developer tools and AI categories for teams who want to trust their autonomous agents but cant afford blind spots.
How to use PandaProbe?
To get started wit PandaProbe, first grab the repo and spin up the backend services. usually involves running docker-compose or setting up the db manually depending on your stack. Once that’s live, you’ll wanna drop the sdk into your actual agent project. install the package via pip and initialize the tracer in your main entry file. Next step is instrumentation. u gotta wrap your llm calls or function invocations with the probe decorators. kinda like opentelemetry but built specifically for agentic workflows. run your app locally while panda probe catches everything in real time. the integration points are straightforward enough for most python devs. Finally check out the dashboard to trace whats going on inside your loops. look for latency spikes or failed reasoning steps in the timeline. its super helpful when stuff breaks in prod. just rememberr to close the session properly so ur data gets saved right without losing context.
Why Choose PandaProbe?
If yer team is deep in building ai agents and u r sick of debugging black box behaviors, this is gonna be your go-to. You get solid visibility to trace requests and eval responses directly in dev or prod without drowning in noise. Plus since its open source, you keep full control over the data and dont have to worry about pricetags hikes later on. One thing though, its not exactly a turn key solution. Self-hosting means yall got to handle the infra maintenance yourself, which adds overhead if u prefer managed services. Might be better suited for orgs with dedicated backend folks rather than small startups trying to ship fast. Bottom line, pick this if observability is critical and u want customizablty over convenience. Just prepare for a steeper initial setup compared to standard Llms wrappers.
PandaProbe Features
Deep Observability
- ✓trace all api reqs instantly
- ✓view full chat history side by side
- ✓map tool dependancies visually
- ✓debug steps way simpler now
Eval & Testing
- ✓run evals directly in local env
- ✓compare result diffs across versions
- ✓spot bad fact generation early
- ✓generate score cards automatically
Prod Monitoring
- ✓alert u on high latency spikes
- ✓track total token spend daily
- ✓watch log streams in real time
- ✓fix glitches faster with context
FAQ?
Pricing
Pricing information not available