XiaoConstantine/dspy-go

每日信息看板 · 2026-03-08
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2026-03-08T01:52:59Z
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AI 总结

XiaoConstantine 发布了 DSPy-Go,一个原生 Go 版 DSPy 框架,将系统化提示工程、智能体编排和优化能力带入 Go 生态,重要性在于它可帮助开发者更可靠地构建复杂 LLM 应用。
#GitHub #repo #开源项目 #Go #DSPy #Agent

内容摘录

DSPy-Go

Go Report Card
codecov
Go Reference
What is DSPy-Go?

DSPy-Go is a native Go implementation of the DSPy framework, bringing systematic prompt engineering and automated reasoning capabilities to Go applications. Build reliable LLM applications through composable modules and workflows.

**Full Documentation** | **API Reference** | **Examples**
Key Features

| Feature | Description |
|---------|-------------|
| **Modular Architecture** | Compose simple, reusable components into complex applications |
| **Multiple LLM Providers** | Anthropic, OpenAI, Google Gemini, Ollama, LlamaCPP, and more |
| **Advanced Modules** | Predict, ChainOfThought, ReAct, RLM, Refine, Parallel |
| **Intelligent Agents** | ReAct patterns, ACE framework for self-improving agents |
| **A2A Protocol** | Multi-agent orchestration with hierarchical composition |
| **Smart Tool Management** | Bayesian selection, chaining, composition, MCP integration |
| **Quality Optimizers** | GEPA, MIPRO, SIMBA, BootstrapFewShot, COPRO |
| **Structured Output** | JSON structured output and XML adapters with security controls |
Installation
Quick Start
CLI (Zero Code)

**CLI Documentation**
Programming
Core Concepts
Signatures
Define input/output contracts for modules:
Modules

| Module | Description |
|--------|-------------|
| Predict | Direct prediction |
| ChainOfThought | Step-by-step reasoning |
| ReAct | Reasoning + tool use |
| RLM | Large context exploration via REPL |
| Refine | Quality improvement through iteration |
| Parallel | Concurrent batch processing |
Structured Output

**Core Concepts Guide**
Documentation

| Guide | Description |
|-------|-------------|
| **Getting Started** | Installation and first program |
| **Core Concepts** | Signatures, Modules, Programs |
| **Building Agents** | ReAct, ACE framework, memory |
| **A2A Protocol** | Multi-agent orchestration |
| **RLM Module** | Large context exploration |
| **XML Adapters** | Structured output parsing |
| **Tool Management** | Smart registry, chaining, MCP |
| **Optimizers** | GEPA, MIPRO, SIMBA, Bootstrap |
Examples
Agent Frameworks
**ace_basic** - Self-improving agents with ACE
**a2a_composition** - Multi-agent deep research
**agents** - ReAct patterns and orchestration
Modules
**rlm** - Large context exploration
**xml_adapter** - XML structured output
**parallel** - Batch processing
**refine** - Quality improvement
Tools
**smart_tool_registry** - Intelligent tool selection
**tool_chaining** - Pipeline building
**tool_composition** - Composite tools
Optimizers
**mipro** - TPE-based optimization
**simba** - Introspective learning
**gepa** - Evolutionary optimization
LLM Providers

**Providers Reference**
Community
**Documentation**: xiaocui.me/dspy-go
**API Reference**: pkg.go.dev
**Example App**: Maestro - Code review agent
License

DSPy-Go is released under the MIT License. See the LICENSE file for details.