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pandaprobe

provenance:github:chirpz-ai/pandaprobe
WHAT THIS AGENT DOES

PandaProbe is an open-source platform designed to help teams build and improve AI agents. It provides tools for collaborative development, monitoring, evaluation, and debugging of these agents. The platform supports both cloud-based usage and self-hosting options, offering flexibility for different development environments. PandaProbe allows teams to track agent performance, identify areas for improvement, and ensure agents are functioning as expected. It streamlines the agent engineering process, making it easier to create and maintain effective AI solutions. Developers and AI engineers can use PandaProbe to accelerate their workflows and enhance the quality of their agents. The platform aims to simplify the complexities of agent development and deployment.

PROBLEM IT SOLVES

Developing and maintaining AI agents can be a complex and time-consuming process, often lacking standardized tools for monitoring and debugging. PandaProbe solves this by providing a centralized platform to track agent performance, identify issues, and collaborate on improvements, eliminating the need for manual tracking and disparate tools.

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CAPABILITIES & CONSTRAINTS

TECH & STACK
pythonlangchainopenaigithub-actionstypescriptragai-agentsmonitoring

USE CASES

README
<div align="center">
  <a href="https://pandaprobe.com" target="_blank" rel="noopener noreferrer">
    <img alt="PandaProbe Logo" src="docs/assets/PandProbe-1.png" width="100%">
  </a>
</div>

<p align="center">
  PandaProbe is an open source agent engineering platform.<br>
  It helps teams collaboratively develop, monitor, evaluate, and debug AI agents.<br>
  You can use PandaProbe cloud (under dev) or self host the service.
</p>

<p align="center">
  <a href="https://pandaprobe.com/" target="_blank"><img src="https://img.shields.io/badge/PandaProbe_Cloud-0066FF" alt="PandaProbe Cloud"></a>
  <a href="https://pandaprobe.com/" target="_blank"><img src="https://img.shields.io/badge/Docs-0066FF" alt="Docs"></a>
  <a href="https://x.com/PandaProbe" target="_blank"><img src="https://img.shields.io/twitter/follow/PandaProbe?style=social" alt="Follow on X"></a>
</p>

<p align="center">
  <a href="https://github.com/chirpz-ai/pandaprobe/actions/workflows/build.yml"><img src="https://github.com/chirpz-ai/pandaprobe/actions/workflows/build.yml/badge.svg" alt="Build"></a>
  <a href="https://github.com/chirpz-ai/pandaprobe/actions/workflows/lint.yml"><img src="https://github.com/chirpz-ai/pandaprobe/actions/workflows/lint.yml/badge.svg" alt="Lint"></a>
  <a href="https://github.com/chirpz-ai/pandaprobe/actions/workflows/test-unit.yml"><img src="https://github.com/chirpz-ai/pandaprobe/actions/workflows/test-unit.yml/badge.svg" alt="Unit Tests"></a>
  <a href="https://github.com/chirpz-ai/pandaprobe/actions/workflows/test-integration.yml"><img src="https://github.com/chirpz-ai/pandaprobe/actions/workflows/test-integration.yml/badge.svg" alt="Integration Tests"></a>
  <a href="https://github.com/chirpz-ai/pandaprobe/actions/workflows/codeql.yml"><img src="https://github.com/chirpz-ai/pandaprobe/actions/workflows/codeql.yml/badge.svg" alt="CodeQL"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License"></a>
</p>

---

## Quick Start

```bash
# 1. Configure environment
cp backend/.env.example backend/.env.development
# Edit backend/.env.development — add your Supabase credentials and LLM provider keys

# 2. Start all services
make up

# 3. Open http://localhost:8000/scalar for API references
```

## Architecture

```mermaid
sequenceDiagram
    participant Client as 📡 SDK / HTTP Client
    participant API as ⚡ FastAPI
    participant Auth as 🔐 Auth Service
    participant IdP as 🌐 Supabase / Firebase
    participant Identity as 👥 Identity Service
    participant Trace as 🫆 Trace Service
    participant Eval as 🧪 Eval Service
    participant DB as 🗄️ PostgreSQL
    participant Redis as 📮 Redis
    participant Worker as ⚙️ Celery Worker
    participant LLM as 🤖 LLM Engine (LiteLLM)

    Note over Client,API: Management Plane (Bearer token)
    Client->>API: Authorization: Bearer <idp_token>
    API->>Auth: Verify token
    Auth->>IdP: Validate with provider
    IdP-->>Auth: User identity
    Auth-->>API: Authenticated user
    API->>Identity: /user, /organizations, /projects
    Identity->>DB: Read / write
    DB-->>Identity: Result
    Identity-->>Client: Response

    Note over Client,API: Data Plane (API key)
    Client->>API: X-API-Key + X-Project-Name
    API->>Identity: Resolve org & project
    Identity-->>API: Project context

    API->>Trace: POST /traces
    Trace->>Redis: Enqueue ingestion job
    Redis-->>Client: 202 Accepted
    Redis->>Worker: Pick up job
    Worker->>DB: Persist trace + spans

    API->>Trace: GET /traces, /sessions
    Trace->>DB: Query with filters
    DB-->>Trace: Rows
    Trace-->>Client: Paginated response

    API->>Eval: POST /evaluations
    Eval->>Redis: Enqueue eval job
    Redis-->>Client: 202 Accepted
    Redis->>Worker: Pick up job
    Worker->>LLM: LLM-as-a-judge call
    LLM-->>Worker: Verdict + score
    Worker->>DB: Persist evaluation result
```

## Auth

| Route group | Auth method | Header |
|---|---|---|
| Management (`/user`, `/organizations`, `/projects`) | IdP token | `Authorization: Bearer <token>` |
| Data plane (`/traces`, `/evaluations`, `/sessions`) | API key | `X-API-Key` + `X-Project-Name` |

## Services

| Service | Description | Port |
|---|---|---|
| **app** | FastAPI application server | 8000 |
| **worker** | Celery background worker | — |
| **postgres** | PostgreSQL 16 | 5432 |
| **redis** | Redis 7 (broker + cache) | 6379 |

## Development

```bash
make install          # Install backend deps via uv
make up               # Start all services (Docker)
make down             # Stop all services
make dev              # Run API locally with hot-reload
make worker           # Run Celery worker locally

make lint             # Ruff linter
make format           # Auto-format code
make migration msg="" # Generate Alembic migration
make migrate          # Apply migrations

make test-unit        # Run unit tests
make test-integration # Run integration tests (spins up test DB)
make test-all         # Run everything
make help             # Show all available commands
```

> [!NOTE]
> **Database migrations** are auto-applied on `make up` via the Docker entrypoint.
> 
> To generate a new migration after model changes:
> ```bash
> make migration msg="describe change"
> ```
> To manually apply migrations:
> ```bash
> make migrate
> ```

## Contributing

We welcome contributions! Please read the [Contributing Guide](CONTRIBUTING.md) for details on how to set up your environment, run tests, and submit pull requests.

## Authors

Built by the [Chirpz AI](https://pandaprobe.com/about) team. Contact sina@chirpz.ai for enquiries.

## License

PandaProbe is licensed under Apache 2.0 — see [LICENSE](LICENSE) for details.

PUBLIC HISTORY

First discoveredMar 21, 2026

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first seenDec 16, 2025
last updatedMar 19, 2026
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