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by adlumal · Agent Tool · ★ 71
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GoalChain GoalChain is a simple but effective framework for enabling goal-orientated conversation flows for human-LLM and LLM-LLM interaction. Installation Getting started Let's import the , , and classes, which are the basis for the conversation flow. In this example we will create an AI assistant whose goal is to collect information from a customer about their desired product order. We define the information to be collected using objects within the , which is a child of : the product name, the customer's email, and quantity We also define a validator for the quantity (after type casting to an int). is used to pass error messages back to conversation. These messages should be human-readable. is a natural language type hint for the LLM's JSON mode output. py def quantityvalidator(value): try: value = int(value) except (ValueError, TypeError): raise ValidationError("Quantity must be a valid number") if value 100: raise ValidationError("Quantity cannot be greater than 100") return value class ProductOrderGoal(Goal): productname = Field("product to be ordered", formathint="a string") customeremail = Fi
| Stars | 71 |
| Forks | 4 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.5239995617355/100 |
| Open Issues | 1 |
| Last Updated | 2024-12-02 |
| Created | 2024-05-20 |
| Platforms | python |
| Est. Tokens | ~24k |
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GoalChain is GoalChain for goal-orientated LLM conversation flows. It is categorized as a Agent Tool with 71 GitHub stars.
GoalChain is primarily written in Python. It covers topics such as chat-flow, chatbot, chatbot-framework.
You can find installation instructions and usage details in the GoalChain GitHub repository at github.com/adlumal/GoalChain. The project has 71 stars and 4 forks, indicating an active community.
GoalChain is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to GoalChain on Agent Skills Hub include sandbox-conversant-lib, SimplerLLM, palico-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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