Chirpz
Chirpz AI · Writing · Business
Chirpz is an applied AI lab that builds engineering tools for AI agents. Founded by Sina Tayebati, the team ships products and publishes research in parallel, with its main offering being PandaProbe, an open-source platform for tracing, evaluating, and monitoring agents in production. The company targets developers and teams who need to debug agent behavior that traditional software tooling can't explain.
About Chirpz
What Is Chirpz
Chirpz is an applied AI lab, not a single-purpose app. The team builds products and publishes research, and the two activities feed each other. Its stated thesis is that AI agents are becoming the next layer of software, but the engineering tooling around them hasn't kept pace. Agents fail in unexpected ways at unexpected times, and they behave differently from both traditional software and standalone language models.
That gap is where Chirpz works. AI agent observability is the whole point here, not an afterthought. The lab aims to close the distance between a working prototype and a dependable production system. The public product is PandaProbe, an open-source agent engineering platform that covers traces, evaluations, and monitoring so teams can debug, assess, and improve agents running live.
So who actually pays for this? Teams shipping agents to real users, mostly. If that's you, the free plan is the cheapest way to find out whether the workflow fits.
The most important limit to understand is audience. This is developer infrastructure. If you don't build or operate AI agents, none of it applies to you. The free tier is real but small. Heavier usage moves into paid plans.
Getting Started
- Visit the Chirpz AI site and follow the link to PandaProbe, the lab's main product.
- Create an account on the Hobby plan, which is free and doesn't require a credit card.
- Connect your first agent, keeping your existing orchestration and execution loop intact.
- Let the platform capture traces and run evaluations on your agent's real tasks.
- Review the results, then upgrade to a paid plan once you outgrow the free usage limits.
Product Information
A quick look at Chirpz's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- AI engineers
- Startup dev teams
- Enterprise platform groups
Tasks
- Debugging agent failures
- Evaluating agent output quality
- Monitoring live agents
Scenarios
- Moving an agent from prototype to production
- Running agents on smaller, cheaper models
- Keeping agent data in-house
Key features
Agent Tracing
Chirpz's platform records what an agent does on every run, step by step. Most agent debugging tools leave you rebuilding a failure from raw logs. Here, you get a trace that shows the full sequence of decisions and tool calls that led to a final outcome, which is usually the first thing you need when an agent misbehaves in production. Traces beat guesswork.
Outcome Evaluation
Beyond recording behavior, the platform evaluates whether results were actually good. It scores outcomes against real tasks, and those scores become the basis for deciding which changes are worth keeping. Evaluations run alongside your existing workflow rather than replacing it. You keep your orchestration and execution loop.
A Control Plane Agents Can Use
PandaProbe exposes a structured command-line interface that agents themselves can drive. An agent can inspect traces, run evaluations, and pull scores without a human clicking through a dashboard. That matters because the tooling is designed for agents working autonomously, not just for people watching from the outside.
Memory That Survives the Run
The platform keeps a persistent workspace of learned rules, organized by task or workflow. Agents connected to the same workspace can find and reuse fixes across turns and sessions. A fix sticks. That's the whole idea, and it's what separates this from a plain logging tool.
Validated Fixes, Not Just Logs
Most tools turn a failure into information for a person to read. Chirpz's approach tests candidate fixes against real tasks before they become reusable rules, and restricted tool access keeps execution bounded. Only changes that improve evaluated outcomes get promoted, which keeps bad fixes from spreading.
Deployment in Your Own Environment
For teams that can't send agent data to a third party, the stack runs on your own Kubernetes, in your own AWS, GCP, or Azure account, or on a local workstation for offline work. No data leaves the perimeter you set. That's a meaningful difference from cloud-only observability tools.
Pros and cons
Pros
- Tracing and evaluation live in one platform, so debugging and quality checks don't require two separate tools.
- The Hobby plan is genuinely free and needs no credit card, which lowers the cost of trying it.
- Self-hosting options cover on-prem, your own cloud account, and local machines, useful under strict data rules.
- Because it's open source, teams can inspect and adapt the stack rather than trust a closed black box.
- It keeps your existing agent orchestration, adding tracing and a repair layer around what you already run.
Cons
- It's developer infrastructure, so non-engineers won't get any use from it.
- The free tier caps usage low, which means growing teams will hit paid plans quickly.
- Much of the tooling runs through a CLI, so people expecting a polished point-and-click dashboard may find the learning curve steep.
- Public pricing stops at "custom" for top tiers, so enterprise costs need a sales conversation to pin down.
Frequently asked questions
Chirpz is an applied AI lab that builds products and publishes research on agent engineering. Its public product is PandaProbe, an open-source platform for tracing, evaluating, and monitoring AI agents in production.
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