Prism core framework
Last updated
Last updated
Prism Framework is designed to address complex tasks through its innovative Prompt Manager and Memory Manager, two foundational components developed to automate prompt generation and manage chat history efficiently. The framework offers a scalable and user-friendly Multi-Agent System with the following features:
1. Agent Planner
Core Role: Decomposes complex tasks into actionable steps, generating efficient action plans.
Four Agent Types:
Markets Agent: Handles trading and market insights.
Image Agent: Focused on image analysis and visual data processing.
Social Media Agent: Monitors and analyzes social platforms like Discord, Telegram, and Twitter.
Analysis Agent: Supports data evaluation, reporting, and decision-making.
Planning Techniques:
Single-Path Strategy: Creates a single pathway for achieving tasks without exploring alternatives.
Multi-Path Strategy: Evaluates and selects optimal paths from multiple generated plans.
2. Connector
Purpose: Provides seamless integration with external systems via APIs and communication tools.
Core Features:
API Integration Layer: Interfaces with blockchain systems (e.g., Ethereum, Solana, Base).
Data Transformation Layer: Converts and processes data to ensure compatibility across systems.
Event-Driven Layer: Synchronizes external data in real-time based on triggers.
Workflow Engine: Defines automated workflows for data processing and system interactions.
UI Management Dashboard: Offers an intuitive interface for managing connectors.
Logging and Monitoring: Tracks data flows, operations, and system performance for debugging and optimization.
3. Memory Manager
Functionality: Supports chat history storage, information compression, and memory retrieval.
Key Components:
Message History: Maintains recent conversations for consistent dialogue context.
Fact Memory: Stores user-specific or context-based facts for personalized responses.
Knowledge Base: Houses static data for answering broader queries.
Relationship Tracking: Monitors user-agent interactions, including frequency and emotional tone, to enhance personalization.
RAG Integration: Employs vector search to retrieve contextually relevant memories or knowledge for dynamic responses.
4. Prompt Manager
Goal: Automates prompt construction for coordinating tasks across multiple LLMs.
Prompt Types:
System Prompt: Defines agent roles, tasks, and responsibilities.
Context Prompt: Incorporates document, code, and tool-related contexts for precise task execution.
Customized Prompt: Tailored prompts for specific tasks, supporting structured outputs like JSON or code snippets.
Automation:
Configures prompts dynamically through role and task definitions.
Provides reusable context strategies, such as session and tool retrieval.
Enables personalized prompts for diverse agent requirements.
5. Component Ecosystem
Retrieval:
Doc Retrieval: Leverages vector databases to manage document-based knowledge.
Code Retrieval: Enhances LLM capabilities with contextual code analysis and generation.
Search Retrieval: Integrates internet searches (e.g., DuckDuckGo) for real-time data augmentation.
Tool Integration: Supports the rapid registration of tools using a Python-based interface (e.g., LangChain).
Action Execution: Defines dynamic actions, including knowledge retrieval, tool usage, and code execution.
6. Memory Systems (Eliza Framework)
Purpose: Enables agents to understand context, maintain long-term interactions, and adapt dynamically.
Memory Types:
Message History: Ensures continuity in ongoing conversations.
Fact Memory: Retains user-specific data for personalized experiences.
Knowledge Base: Provides static references for broader information needs.
Relationship Tracking: Builds rapport through emotional and historical interaction data.
Trend Analysis: Supports temporal analysis for market and transactional changes.
This framework represents a modular and highly adaptable system, empowering agents to perform complex tasks with precision and scalability, catering to the dynamic needs of the crypto ecosystem.
