Coding / Developer

DeepSeek Harness

DeepSeek Harness is a GitHub repository maintained by deepseek-ai that provides ready-made evaluation scripts and task configurations for DeepSeek models based on lm-evaluation-harness.

Product type: Extension / PluginPricing: UnknownAPIMaster integration: Supported

OpenRouter token usage rank

Source: OpenRouter

Website Download / Install Docs GitHub

Overview

This product belongs to the Coding / Developer category and is mainly intended for developers to deploy and use locally or on servers. By cloning the repository, installing dependencies, and running specified commands, users can run standard benchmark tests on DeepSeek series models. Its core strength is that the repository already includes adaptation files and task lists for DeepSeek models, so users do not need to write evaluation code themselves. It is suitable for AI researchers and engineering teams that need consistent evaluation of open-source or proprietary large models. Compared with general evaluation frameworks, it directly ties into DeepSeek model weight loading methods, reducing the model adaptation step.

One-line summary

DeepSeek Harness is a GitHub repository maintained by deepseek-ai that provides ready-made evaluation scripts and task configurations for DeepSeek models based on lm-evaluation-harness.

What people use it for

Users use it to run python scripts, benchmark specific DeepSeek models on datasets such as MMLU and HumanEval, and output scores. Users also use it to compare evaluation results across different model versions or checkpoints in batches.

Best for

Developers, engineering teams, and technical leads

How it works

It typically starts by reading context in an IDE, terminal, or project environment, then the Agent plans steps, executes commands, or modifies files, and the user checks the results.

Quick facts

Product type

Extension / Plugin

Pricing

Unknown

The current enhanced bulk dataset does not maintain real-time pricing for this product. Refer to the official website or official documentation.

APIMaster integration

Supported

DeepSeek Harness → Settings → Models → Add a custom provider

Data confidence

Medium

Last verified: 2026-08-30

Core features and differentiators

Run HumanEval benchmarks

Load the HumanEval dataset, run model code generation, and calculate pass@1, pass@10, and pass@100 metrics

Supports vLLM backend inference

Load models through the vLLM engine for batch inference, with support for tensor parallelism and continuous batching

Custom YAML task configuration

Use YAML files to define task names, dataset paths, prompt templates, and evaluation metrics

Output JSON evaluation reports

Generate JSON files containing detailed results for each sample and overall metrics

Integrated Hugging Face model loading

Load DeepSeek-Coder series model weights directly from Hugging Face Hub

Supports MBPP and APPS datasets

Built-in MBPP and APPS code generation tasks with support for automatic download and preprocessing

What is DeepSeek Harness best for?

Evaluate DeepSeek-Coder-6.7B on HumanEval

Run python main.py --model deepseek-ai/deepseek-coder-6.7b-instruct --tasks humaneval

Best for：Model developers

Compare v1 and v2 model performance

Run the same benchmark tests on deepseek-coder-6.7b-base and deepseek-coder-v2-lite-base respectively and compare pass@k

Best for：Researchers

Add a custom code dataset

Write a YAML configuration pointing to an internal code repository, then run evaluation to verify model performance on a specific programming language

Best for：Enterprise developers

Use vLLM to accelerate batch evaluation

Configure the vLLM backend to run MBPP and HumanEval on multiple models at the same time and generate comparison reports

Best for：AI engineers

Third-party key setup for DeepSeek Harness (APIMaster integration)

Supported

Setup steps

1Open Settings → Models and click Add a custom provider.
2Fill in the Provider ID, Display name, Base URL, API protocol, and API key.
3Add available models either manually or through Fetch available models.
4After saving, return to the conversation model selector and choose a model under that custom provider for minimal verification.

Config values

Base URL

https://apimaster.ai/v1

API key environment variable

API key（Custom provider 配置项）

Model

gpt-5.6-sol or claude-sonnet-4-6 or deepseek-v4-pro

Create my key

Official source Official source Official source APIMaster API docs

Community discussion

Discussion summary

Users usually understand DeepSeek Harness as a highly modular AI coding agent orchestration framework built around the core idea that "everything is a plugin," allowing components such as models, tools, session logs, agent loops, and sub-agents to be freely replaced through configuration or custom plugins. Discussion focuses on how to use its plugin architecture to build personalized workflows, integrate with local models or third-party agents such as Claude Code and Codex, and balance flexibility with stability in real coding tasks. Users also often discuss its architectural differences from similar tools such as Pi and Hermes, with attention on how plugin-based extensions can adapt functionality to different scenarios.

Most discussed topics

1
How does the plugin architecture enable component replacement?
Users discuss how core parts such as model adapters, tool registration, session logs, and agent loops can be seamlessly replaced through plugin-declared dependencies, event listeners, and reversible registration without modifying the framework source code.
2
How to choose between it and tools like Pi or Claude Code?
Users compare DeepSeek Harness's fully plugin-based Lego-style design with Pi's minimalist foundation or Claude Code's CLI experience, exploring tradeoffs in context management, cost control, and customization freedom, as well as which scenarios fit different workflows.
3
Sub-agent support and task routing?
Users focus on how to connect tools such as Claude Code or Codex as native sub-agents, routing subtasks and checking status and progress through configuration to orchestrate multi-agent collaboration.
4
Local model and tool integration practices?
Users share installation and configuration experience for connecting local models such as Ollama and Qwen to Harness, adding tool plugins such as web tools and iOS automation, and running and extending it in a local Web UI.
5
Plugin ecosystem and custom development?
Users discuss using the community plugin directory, writing new plugins to extend memory, sandbox, or UI capabilities, and how plugin API stability affects long-term custom agent development.

FAQ

What type of product is DeepSeek Harness?

We currently classify it under the "Coding / Developer" category, and the page description is based on public sources such as the official website and OpenRouter.

What is DeepSeek Harness suitable for?

The enhanced page for DeepSeek Harness prioritizes the core tasks and use cases that have already been collected, helping you quickly judge whether it matches your current needs.

Can DeepSeek Harness connect directly to APIMaster now?

Current materials have confirmed that the product supports third-party keys or custom compatible endpoints, so you can continue verification directly according to the page configuration instructions.

Last verified: 2026-08-30 · Report a correction