This directory contains example implementations of various agentic systems using our framework. Each example demonstrates different agent architectures and applications to solve specific business problems.
A Human-in-the-Loop (HITL) agent implementation for customer support. This example demonstrates how human review and intervention can be integrated into an autonomous agent workflow to handle sensitive customer inquiries.
A RAG Router Agent implementation for managing and retrieving information from multiple knowledge domains. This example shows how to route queries to the most relevant knowledge sources based on semantic understanding.
A RAG Orchestrated Multi-Agent System for comprehensive legal analysis. This example demonstrates how multiple specialized agents can collaborate to analyze complex legal cases, with each agent contributing domain-specific expertise.
Each example is designed to work with the local deepseek-r1:14b model through Ollama. To run the examples:
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Install Ollama from https://ollama.com/
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Pull the deepseek-r1:14b model:
ollama pull deepseek-r1:14b -
Navigate to the specific example directory and follow the instructions in its README file.
These examples showcase the three main types of agents implemented in our framework:
- Task-Specific Agents: Specialized agents focused on one type of task (Knowledge Management example)
- Human-Augmented Agents: Agents that incorporate human feedback into their workflow (Customer Support example)
- Multi-Agent Systems: Coordination of multiple agents working together (Legal Analysis example)
Each example includes a detailed README with implementation details, usage instructions, and example interactions.
- Python 3.8+
- Ollama
- All dependencies listed in the project's main requirements.txt file
For specialized dependencies required by specific examples, check the README in each example directory.