PandaProbe is an open source agent engineering platform for teams running production AI agents. It combines tracing, evaluation, and monitoring so developers can capture full trajectories, score behavior with research-based metrics, and catch regressions before users notice them, across both hosted cloud and free self-hosted deployments.
Key Features:
Full-Agent Tracing: Captures every tool call, LLM hop, and branch so evals score complete trajectories.
Research-Grounded Evals: Offers long-horizon uncertainty metrics and LLM-as-judge scoring to pinpoint where agents drift.
Production Monitoring: Schedules eval runs on production traffic and alerts when metrics regress across versions.
Agent-Native Workflow and Integrations: Ships a Skill file, CLI, and Python SDK that tie coding agents into major LLM providers.
Pros
Open source core: Apache 2.0 licensing and self-hosting give teams control over data and deployment.
Agent-focused workflow: The Skill and CLI let agents and CI scripts manage traces and evals.
Low barrier to entry: A free Hobby cloud tier plus open source edition make starting instrumentation inexpensive.
Cons
Language focus: The main SDK targets Python, so teams in other languages must add custom wiring.
Developer-centric setup: Effective use expects familiarity with tracing, agents, and CLI workflows, which can feel heavy for simple apps.
Who is Using PandaProbe?
Agent platform engineers: Instrument multi-step agents to capture tool usage, branching, and session context for reliability.
AI application developers: Use trajectory-level traces and scores to refine prompts, tools, and control flow.
MLOps and observability teams: Schedule evals on production traffic and track metrics and alerts for regressions.
AI safety and research teams: Apply uncertainty metrics and LLM-as-judge feedback to study failure patterns.
Uncommon Use Cases: Platform teams gating releases in CI on eval scores; educators using real traces in agent engineering courses.
Pricing:
Hobby (Cloud): $0 per month for small projects, with capped traces and eval runs.
Pro (Cloud): $29 per month with higher trace and eval limits and two included seats.
Startup (Cloud): $299 per month for scaling projects, with tens of thousands of traces and more seats.
Enterprise: Custom pricing not listed publicly, adding alternative hosting options, custom SSO, SLAs, and unlimited seats.
Open Source (Self-Hosted): Free under Apache 2.0 to self-host all core features with community support.
Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official PandaProbe website.
What Makes PandaProbe Unique?
PandaProbe pairs long-horizon, research-based agent metrics with an Apache-licensed open source core and an agent-controllable Skill and CLI for running evals. This combination targets long-running, tool-using agents while giving teams full control over where the observability stack runs.
How We Rated It:
Accuracy and Reliability: 4.3/5
Ease of Use: 3.8/5
Functionality and Features: 4.6/5
Performance and Speed: 4.2/5
Customization and Flexibility: 4.4/5
Data Privacy and Security: 4.1/5
Support and Resources: 3.9/5
Cost-Efficiency: 4.8/5
Integration Capabilities: 4.5/5
Overall Score: 4.3/5
PandaProbe As An Open Source Control Tower For Agent Reliability:
Teams that care about full agent trajectories and production reliability gain the most from PandaProbe. Engineering-heavy groups can turn its tracing, evals, and alerts into a control loop for drift management. It is especially well suited to Python-centric organizations ready to invest in agent instrumentation while keeping hosting and data control in their own hands.
PandaProbe AI Reviews: Use Cases, Pricing & Alternatives
