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DSPy
#66Framework for programming and optimizing LM pipelines
3.9FreeSemi-Autonomous多代理框架
DSPy by Stanford NLP is a framework for programming — rather than prompting — language models. It provides composable modules and automatic optimizers that tune prompts, few-shot examples, and model weights to maximize pipeline quality.
78%
任务成功率
15s
平均响应时间
35,000
Tokens/Task
详细评分
自主性
60/100
可靠性
78/100
易用性
58/100
成本效益
80/100
集成能力
50/100
支持
55/100
能力
CodeKnowledgeApi
主要特点
优点
- Stanford research backing
- Unique optimization approach
- Composable modules
- Academic rigor
缺点
- Steep learning curve
- Research-oriented
- Limited production use
- Complex concepts
定价方案
Open Source
Free
- Full framework
- MIT license
- Research updates
适用场景
LM OptimizationPrompt EngineeringResearchPipeline Building
成本模拟器
根据使用量估算您的月度成本
105001000
基础订阅$0
Token费用（100个任务）$15.40
月度总计$15.40
Underlying Models
GPT-4.1$2/$8 per 1M tokens
~35,000 tokens/task avg
查看所有代理定价
→Underlying Models
GPT-4.1
OpenAI · $2/$8 per 1M tokens
Claude 4 Sonnet
Anthropic · $3/$15 per 1M tokens
View Model Pricing →测试日期2025-05
最后更新2025-06
自主级别Semi-Autonomous
