pandaprobe
PandaProbe is an open-source platform for observing, tracing, and evaluating AI agents. It provides a dashboard and SDK for debugging agent behavior and can be deployed in the cloud or self-hosted.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | chirpz-ai/pandaprobe |
| Owner | chirpz-ai |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 635 |
| Forks | 87 |
| Open issues | 17 |
| Latest release | v0.5.0 (2026-06-10) |
| Last updated | 2026-07-05 |
| Source | https://github.com/chirpz-ai/pandaprobe |
What pandaprobe is
Python-based agent observability stack built on FastAPI, PostgreSQL, Redis, and Celery. Integrates with LangGraph, CrewAI, Claude Agent SDK, and OpenAI Agents SDK via SDK or HTTP; uses LiteLLM for LLM-as-a-judge evaluations.
Get the pandaprobe source
Clone the repository and explore it locally.
git clone https://github.com/chirpz-ai/pandaprobe.gitcd pandaprobe# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- SDK integration requires minimal code changes (decorators/context managers likely); existing LangGraph/CrewAI/Claude projects may plug in with single-line setup.
- Self-hosted deployment depends on maintaining PostgreSQL 16, Redis 7, and Celery workers; ops overhead similar to deploying a small microservice stack.
- API key and project-scoped isolation via Identity Service; confirm RBAC/team permissions model meets internal governance before production use.
- Async trace ingestion (202 Accepted) means queries may show slight staleness; batch processing for evaluations adds workflow latency—assess impact on your iteration cycles.
- LiteLLM backend for evaluations requires API keys for LLM providers (OpenAI, Claude, etc.); cost and rate-limit planning needed at scale.
When to avoid it — and what to weigh
- No Agent or LLM Component — Designed specifically for agentic AI workflows; not a general-purpose APM or observability tool for traditional microservices.
- Real-time Sub-Second Latency Requirement — Async ingestion via Redis/Celery introduces processing latency; not suitable for ultra-low-latency online metrics dashboards.
- Strongly Regulated Environments with Limited OSS Adoption — Project is ~7 months old (created Dec 2025); Apache 2.0 is permissive but adoption in enterprise/compliance-heavy orgs is unproven.
- No Python/Docker Infrastructure — Self-hosting requires Docker, PostgreSQL, Redis, and Python runtime; no pre-built binaries or lightweight deployment options documented.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), an OSI-approved permissive open-source license. Allows commercial use, modification, and distribution with attribution and liability disclaimers.
Apache 2.0 permits commercial use and derivative works. No external commercial restrictions evident from the DATA. However, verify any commercial terms of service separately on pandaprobe.com if using managed cloud offering; self-hosted use is unrestricted by license.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
AUTH: Bearer token + API key dual auth model separates management and data planes. Token validation via external IdP (Supabase/Firebase). No explicit mention of TLS, encryption at rest, audit logging, rate limiting, or secrets management in README excerpt. Self-hosted deployments expose PostgreSQL and Redis; network isolation and credential rotation required. Early-stage project—no third-party security audit or penetration test results visible in DATA. Recommend security review before processing sensitive agent data.
Alternatives to consider
Langsmith (LangChain)
Mature, SaaS-first LLM observability with strong LangChain ecosystem integration; but closed-source and vendor lock-in.
Arize AI
Enterprise ML observability platform supporting agents; larger feature set and compliance posture but higher cost and less developer-friendly for small teams.
OpenTelemetry-based APM (Jaeger, DataDog)
General-purpose distributed tracing; agent-specific features and evals not built-in, requiring custom instrumentation.
Build on pandaprobe with DEV.co software developers
Deploy PandaProbe in minutes—self-host for privacy or use the managed cloud. Gain visibility into agent behavior, automate evaluations, and ship with confidence.
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pandaprobe FAQ
Can I use PandaProbe without an agent framework (e.g., raw LLM calls)?
What are the limits on trace retention and query volume for self-hosted?
Does PandaProbe support on-premise LLM backends for evaluations?
How do I migrate traces from another platform to PandaProbe?
Work with a software development agency
DEV.co helps companies turn open-source tools like pandaprobe into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source observability stack.
Start Observing Your AI Agents Today
Deploy PandaProbe in minutes—self-host for privacy or use the managed cloud. Gain visibility into agent behavior, automate evaluations, and ship with confidence.