PandaProbe
An engineering platform for tracing, evaluating, monitoring, and debugging AI agents.
- Source repo
- chirpz-ai/pandaprobe
- Stars
- ★ 786
- Last updated
- 1d ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 41/100 · Major gaps
At a glance
- Works with
- Portable with changesClaude API (Partial support)
- You'll need
- Typical use
- A LangGraph engineering team that needs centralized trace and span records to investigate agent execution issues.
- Main limitation
- Self-hosting requires Docker and a multi-service stack comprising Next.js, FastAPI, Celery, PostgreSQL, and Redis.
- Source review
- 41/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
PandaProbe is an open-source agent engineering platform from Chirpz AI for collaborative tracing, evaluation, monitoring, and debugging. Its documented stack includes a Next.js dashboard, FastAPI application server, Celery workers and scheduler, PostgreSQL, and Redis. Data-plane clients submit trace or evaluation work with X-API-Key and X-Project-Name, while Redis queues that work for background processing. Workers persist traces and spans to PostgreSQL; evaluation workers use a LiteLLM-backed LLM-as-a-judge flow to store a verdict and score. Teams can use PandaProbe Cloud or self-host the service with Docker.
A client sends POST /traces or POST /evaluations to the FastAPI API. The service resolves organization and project context through the Identity Service, enqueues ingestion or evaluation work in Redis, and returns 202 Accepted. A Celery Worker consumes the job, persists traces and spans in PostgreSQL, or calls the LiteLLM LLM Engine for an LLM-as-a-judge verdict and score before persisting the evaluation result. GET /traces and GET /sessions return filtered, paginated data. Management-plane requests use a Bearer token authenticated through the Auth Service with Supabase or Firebase.
- A LangGraph engineering team that needs centralized trace and span records to investigate agent execution issues.
- A CrewAI team that wants to enqueue evaluations and retain LLM-as-a-judge scores for its multi-agent workflows.
- A team using the Claude Agent SDK that needs one service for observing and evaluating agent runs.
- An organization with internal deployment requirements that wants to run the dashboard, API, workers, PostgreSQL, and Redis under Docker.
- A platform team that needs project-scoped agent data and can identify data-plane requests with X-API-Key and X-Project-Name.
How do you install or deploy this agent?
Docker must be installed and running. Run:
git clone https://github.com/chirpz-ai/pandaprobe.git
cd pandaprobe
./start.shAfter startup, open the dashboard at http://localhost:3000 and the API reference at http://localhost:8000/scalar.
How do you use this agent?
Start the service, then open http://localhost:3000 for the dashboard. For the data plane, provide X-API-Key and X-Project-Name and submit traces to POST /traces or evaluations to POST /evaluations; both are processed asynchronously and return 202 Accepted. Query filtered, paginated records through GET /traces and GET /sessions. Management-plane requests require a Bearer token. The supplied material does not document API-key provisioning, project creation, or copyable request bodies.
What are this agent's strengths and limitations?
- Trace ingestion and evaluations are queued through Redis and handled by Celery, allowing callers to receive 202 Accepted after submission.
- The documented architecture covers trace/span persistence, trace and session querying, and stored LLM-as-a-judge verdicts and scores.
- It offers both a managed cloud option and a documented Docker self-hosting path with named service components.
- The repository explicitly names integrations with LangGraph, CrewAI, and the Claude Agent SDK.
- Self-hosting requires Docker and a multi-service stack comprising Next.js, FastAPI, Celery, PostgreSQL, and Redis.
- Evaluation depends on a LiteLLM-backed LLM-as-a-judge flow, but the supplied material does not specify supported model providers, model configuration, or cost controls.
- Data-plane writes require X-API-Key and X-Project-Name, while management requests require a Bearer token validated through Supabase or Firebase; credential issuance is not documented here.
- The supplied material does not include client SDK initialization, complete API payloads, or integration configuration examples.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| PandaProbe This agent | 41 · Major gaps | ★ 786 | 1d ago | Python | — |
| PenguinHarness | 52 · Major gaps | ★ 2.3k | 5d ago | TypeScript | OpenAI API · Claude API |
| Designing Multi-Agent Systems (PicoAgents) | 59 · Major gaps | ★ 1.3k | 1mo ago | Python | OpenAI API · Claude API |
| Paper-Agent — AI Paper Research & Survey Writing Workbench | 59 · Major gaps | ★ 461 | 5d ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README describes separation of management plane (Bearer token) and data plane (API key), and authentication via Supabase/Firebase, indicating partial least privilege. No evidence of user confirmation mechanisms. Data flow transparency is good with clear architecture diagram. Sensitive data handling: test config uses dummy Stripe key, but production handling not specified. Dependency security: CodeQL workflow present, but no dependency vulnerability scanning or pinned versions. External effects: self-hosting and cloud mentioned, but no explicit external side effects. Rollback not mentioned. Source attribution: author info and contact provided, but publisher unverified.
Evidence shows: README and architecture diagram are consistent in service descriptions, but no detailed error handling or failure messages. Dependency availability: Docker and lock files (uv.lock, yarn.lock) present, but no availability guarantees. Failure messages: no specific error handling strategies or user-visible error information.
Evidence shows: README targets development teams, provides quickstart and integration guides, but does not define capability boundaries (e.g., specific limitations of supported agent frameworks). Trigger precision: no specific trigger conditions or configuration options. Environment fit: Docker self-hosting and cloud options provided, but detailed configuration requirements for different environments not specified.
Evidence shows: README provides clear architecture diagram and service list, good information architecture. Install notes provide self-hosting steps, but no detailed configuration guide. Naming stability: no API versioning or naming conventions. Examples and FAQ: quickstart link provided, but no FAQ. Known limitations not mentioned. License is Apache-2.0 with full text. Versioning and changelog not mentioned. Maintenance responsibility: author and contact provided, but no maintenance policy.
Evidence shows: README describes product purpose and features. Output usability: dashboard and API reference provided. Marginal value: positioned as open-source platform, but no comparison with other tools. Cost-benefit: free tier and self-hosting options mentioned, but no detailed cost analysis.
Evidence shows: Claims in README (e.g., features, integrations) lack specific code or test evidence. Cross-source corroboration: CI badges present, but no independent verification. Fact-inference separation: README descriptions are mostly promotional, not clearly distinguishing facts from inferences.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity is unverified; proceed with caution.
- No user confirmation mechanisms provided, which may affect operational safety.
- Sensitive data handling details are unclear; evaluate for production use.
- Dependency security measures are limited; consider checking for vulnerabilities.
- Lack of known limitations and changelog may affect upgrade decisions.