harbor-framework/harbor — Harbor is a framework for running agent evaluations and creating and using RL environments.

Description and use

**Brief Summary (2‑3 sentences)**
Harbor is an open‑source Python framework that streamlines the creation, evaluation, and orchestration of reinforcement‑learning (RL) agents and environments. It lets developers prototype AI capabilities—such as Retrieval‑Augmented Generation (RAG) pipelines or multi‑agent workflows—without building a model stack from scratch. With strong community interest (2.6 k stars, 1.2 k forks) and recent activity, Harbor is positioned as a viable OSS candidate for pilot projects.

**Value**

• **Accelerated prototyping** – Provides ready‑made abstractions for agents, environments, and evaluation metrics, cutting weeks of engineering effort.
• **Modular workflow composition** – Enables plug‑and‑play of LLMs, retrieval components, and custom policies, supporting RAG and complex agent orchestration use cases.
• **Open ecosystem** – Leverages Python’s ML stack, making it easy to integrate with existing libraries (e.g., LangChain, OpenAI, Hugging Face).

**Practical Adoption Path**

1. **Initial assessment** – Clone the repo, run the example notebooks, and verify that the evaluation APIs align with your target use case.
2. **Sandbox integration** – Wrap your existing model or retrieval service in Harbor’s Agent interface, then use the built‑in Environment classes to simulate interactions.
3. **Pilot validation** – Deploy the prototype in a controlled environment (e.g., a staging Kubernetes namespace) and run automated evaluation suites to benchmark performance and safety.
4. **Production hardening** – Add logging, monitoring, and security hardening (e.g., dependency scanning) before promoting to production workloads.

**Production Readiness**
Harbor scores high on production readiness: it shows recent commits (as of 2026‑06‑23), active community adoption, and a solid codebase in Python. While the metadata around integration points is sparse and a final review of licensing, security posture, and maintainer activity is still required, the overall signals (stars, forks, ecosystem traction) indicate that Harbor is ready for serious pilot deployments and can be hardened for production use.

Problem

harbor-framework/harbor helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

Harbor is a framework for running agent evaluations and creating and using RL environments.

Source