1
Langfuse
Langfuse is an open-source LLM engineering platform that provides comprehensive observability and evaluation capabilities with the flexibility of self-hosted deployment.
AI Tool
PandaProbe Review
PandaProbe is an open-source agent engineering platform for deep observability, evaluation, monitoring, and debugging of AI agent applications.
shipped May 3, 2026aifreemium
1PandaProbe is an open-source, self-hostable platform developed by Chirpz AI.
2It provides deep observability, tracing, evaluation, and monitoring capabilities for AI agents.
3The platform supports debugging complex multi-step AI agents across LLMs, tools, and custom logic.
4PandaProbe offers a freemium pricing model, including a free tier for users.
PandaProbe at a Glance
About PandaProbe
Pricing Plans
Free Tier
Free / monthly
- • Self-hostable
- • Open source
- • Basic features
Cloud Tier
Varies / monthly
- • Managed infrastructure
- • Advanced features
- • Support
Leadership
Chirpz AI TeamFounding Team
📄 API DocsOpen Source
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Arize Phoenix
Arize Phoenix is an OpenTelemetry-native, open-source observability and evaluation tool specifically designed for LLM applications, emphasizing vendor-neutral instrumentation and local data privacy.
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AgentOps
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overview
What is PandaProbe?
PandaProbe is an agent engineering platform tool developed by Chirpz AI that enables developers, AI engineers, and platform teams to debug, evaluate, and monitor AI agent applications. It provides deep observability to trace, evaluate, and debug AI agents in both development and production environments. The platform is architected for scale and offers a unified solution for the entire AI agent development lifecycle, from initial runs to continuous improvement, ensuring reliability and quality in production.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | Chirpz AI |
| Business Model | Freemium (open-source core) |
| Pricing | Free Tier: Free, Cloud Tier: Varies |
| Platforms | Web, API |
| API Available | Yes |
| HQ | USA |
| Funding | Bootstrapped |
features
Key Features of PandaProbe
PandaProbe offers a comprehensive suite of features designed for the observability and improvement of AI agent applications. Its open-source and self-hostable architecture provides flexibility and control for developers and platform teams. The platform's core functionalities are centered around understanding and enhancing AI agent behavior in complex, multi-step environments.
- 1Open-source architecture allowing for self-hostable deployment and local control.
- 2Deep observability specifically tailored for AI agent applications.
- 3Comprehensive tracing capabilities, capturing full agent executions across LLMs, tools, sub-agents, and custom logic.
- 4Research-grounded evaluation with agent-specific metrics and LLM-as-judge scoring for quality and regression detection.
- 5Automated monitoring through scheduled evaluation runs against production traffic to detect behavioral drift.
- 6Analytics for tracking performance, cost, latency, errors, and quality trends over time.
- 7Debugging tools for complex multi-step AI agents where traditional logs are insufficient.
- 8Session and user tracking to provide context and insights into AI agent application usage.
use cases
Who Should Use PandaProbe?
PandaProbe is primarily designed for technical professionals involved in the development and deployment of AI agents. Its capabilities address the specific challenges faced by engineers and platform teams in ensuring the reliability, quality, and performance of agent-based systems in both development and production environments.
- 1Developers and AI engineers: For debugging complex multi-step AI agents involving LLM calls, tools, APIs, and sub-agents, and for ensuring the reliability and quality of agents in production.
- 2Platform teams: For providing modern observability, evaluation, and monitoring infrastructure that supports AI agent development.
- 3Builders experimenting with agents: For gaining deep understanding of agent behavior and continuously improving their AI agent applications.
- 4Startups: For efficiently building, understanding, and shipping reliable AI agents with confidence, leveraging an open-source and scalable solution.
pricing
PandaProbe Pricing & Plans
PandaProbe operates on a freemium business model, providing accessibility for individual developers and offering scalable solutions for larger teams. The platform includes a free tier, allowing users to explore its core functionalities without initial investment. For advanced features, managed services, or higher usage, a Cloud Tier is available with variable pricing.
- 1Free Tier: Free (includes core open-source functionalities and self-hosting options).
- 2Cloud Tier: Varies (pricing is dependent on usage, features, and support requirements for managed cloud services).
competitors
PandaProbe vs Competitors
PandaProbe positions itself as an open-source agent engineering platform specializing in deep observability for AI agent applications. While it shares some functionalities with broader ML platforms and other LLM observability tools, its focus on agent-specific metrics and full session evaluation distinguishes its offering in the competitive landscape.
1
Langfuse is an open-source LLM engineering platform that provides comprehensive observability and evaluation capabilities with the flexibility of self-hosted deployment.
Like PandaProbe, Langfuse is open-source, self-hostable, and offers tracing and evaluation for AI agents. It provides a freemium model, similar to PandaProbe's pricing structure.
2
MLflow is the largest open-source AI engineering platform, providing a complete suite for debugging, evaluating, monitoring, and optimizing AI agents, LLMs, and ML models across the entire lifecycle.
MLflow is also open-source and offers robust debugging, evaluation, and monitoring for AI agents, aligning with PandaProbe's core features. However, MLflow provides a broader platform for the entire machine learning lifecycle, extending beyond just AI agent observability.
3
Arize Phoenix is an OpenTelemetry-native, open-source observability and evaluation tool specifically designed for LLM applications, emphasizing vendor-neutral instrumentation and local data privacy.
Similar to PandaProbe, Phoenix is open-source and focuses on tracing and evaluation for AI applications. Its strong OpenTelemetry integration offers a vendor-neutral approach, which complements PandaProbe's self-hostable and scalable architecture.
4
AgentOps provides purpose-built observability for autonomous AI agents, featuring unique time-travel debugging, session replay, and multi-agent workflow visualization.
AgentOps directly targets AI agent observability, similar to PandaProbe, by tracking the entire agent lifecycle. Its distinct 'time-travel debugging' and comprehensive multi-agent visualization capabilities offer a different approach to debugging compared to PandaProbe.
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Frequently Asked Questions
+What is PandaProbe?
PandaProbe is an agent engineering platform tool developed by Chirpz AI that enables developers, AI engineers, and platform teams to debug, evaluate, and monitor AI agent applications. It provides deep observability to trace, evaluate, and debug AI agents in both development and production environments.
+Is PandaProbe free?
Yes, PandaProbe offers a Free Tier which includes its core open-source functionalities and self-hosting options. There is also a Cloud Tier with variable pricing for managed services and advanced features.
+What are the main features of PandaProbe?
PandaProbe's main features include open-source and self-hostable architecture, deep observability for AI agents, comprehensive tracing of LLM calls and custom logic, research-grounded evaluation with LLM-as-judge scoring, automated monitoring, analytics for performance and cost, and debugging tools for complex multi-step AI agents.
+Who should use PandaProbe?
PandaProbe is intended for developers, AI engineers, platform teams, and startups who are building, debugging, evaluating, and monitoring AI agent applications. It is particularly useful for those needing deep observability into complex, multi-step agent behaviors.
+How does PandaProbe compare to alternatives?
PandaProbe differentiates itself from competitors like Langfuse by focusing on 'SOTA metrics for agent behavior' and evaluating full sessions for agent uncertainty. Compared to MLflow, PandaProbe specializes in AI agent observability, while MLflow is a broader ML platform. Against Arize Phoenix, PandaProbe emphasizes its self-hostable, scalable architecture for AI agents, and against AgentOps, it offers a distinct approach to tracing, evaluation, and monitoring versus AgentOps' time-travel debugging and session replay.
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