v1.0 Production Ready·MCP Protocol Native

HarnessAgent Runtime for DeepSeek

Model + Harness = Agent. Turn DeepSeek LLM into production-grade autonomous agents with MCP protocol, tool loops, memory, retry logic and task orchestration.

$ pip install deepseek-harness
Core Advantages

Everything you need to build production agents

Three pillars that transform a raw language model into a reliable, autonomous execution engine.

Protocol Native

Complete MCP, OpenAI, CLI, and SDK multi-protocol adaptation. Connect any tool, any data source, any system — natively.

MCP ProtocolOpenAI CompatibleCLI & SDKCustom Adapters

Agent Runtime Loop

Task decomposition, tool iteration, memory context, failure retry, and result validation — the full autonomous execution loop.

Task DecompositionTool IterationMemory ContextFailure Retry

DeepSeek Optimized

Dedicated context optimization, long-task stability, and inference acceleration tuned specifically for DeepSeek model architecture.

Context OptimizationLong-Task StableInference AccelerationToken Efficiency
Architecture

How Harness orchestrates your agent

DeepSeek LLM ⟷ Harness Runtime ⟷ MCP Tools — a bidirectional data flow with layered security and intelligent scheduling.

DeepSeek LLMdeepseek-v4-flashreasoning · inferenceHarnessRuntimeAgent LoopMemoryRetrySchedulerMCP Protocol LayerMCP Toolssearch · fetch · apiFile Systemread · write · watchShell / Execbash · subprocesspromptresponseBidirectional Data Flow
Quick Start

From model to agent in seconds

The official minimal example. Install, import, configure, and let your DeepSeek agent autonomously complete complex multi-step tasks.

agent.py
from deepseek_harness import Harness

# Initialize your agent with DeepSeek model
agent = Harness(model="deepseek-v4-flash")

# Register MCP tools and capabilities
agent.register_mcp_server("filesystem", "./workspace")
agent.register_tool("code_search", search_codebase)
agent.register_tool("run_tests", execute_tests)

# Run autonomous task with retry & memory
result = agent.run(
    "分析代码、修复BUG、执行测试、输出报告",
    max_iterations=20,
    retry_on_failure=True,
    memory_enabled=True,
)

print(result.summary)

3 lines to launch

Initialize, configure, and run — no boilerplate, no setup ceremony.

MCP native integration

Register any MCP server or custom tool with a single method call.

Built-in safety nets

Retry logic, result validation, and memory context handled automatically.

Production ready

Structured outputs, error handling, and observability out of the box.

Use Cases

Built for real-world agent scenarios

From single-tool automation to enterprise-scale orchestration, Harness adapts to your production needs.

AI Code Agent

Autonomous code analysis, bug fixing, test execution, and report generation. Build a full-cycle development assistant powered by DeepSeek reasoning.

Code ReviewBug FixingAuto Testing

MCP Protocol Integration

Seamlessly connect Model Context Protocol servers — search, fetch, file operations, databases, and external APIs — all orchestrated by Harness runtime.

MCP ServerTool CallingExternal API

Enterprise Private Agent

Deploy private, secure AI agents on your own infrastructure. Full control over data, tools, memory, and execution boundaries for enterprise compliance.

Self-HostedData PrivacyCompliance

Long-Task Automation

Schedule and execute long-running autonomous tasks with checkpointing, resume-on-failure, and progress tracking. Perfect for batch processing pipelines.

SchedulingCheckpointingBatch Jobs
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GitHub Stars
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Supported Models
v0.0
MCP Protocol

Build Your First DeepSeek Agent Today

Join thousands of developers building production-grade autonomous agents with the Harness runtime framework.

$ pip install deepseek-harness