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Eliza Framework

Framework and Platform for Smart Crypto Agents.

Website | Github | Workbook

Features​

  • 🛠️ Full-featured Discord, Twitter and Telegram connectors
  • 🔗 Support for every model (Llama, Grok, OpenAI, Anthropic, etc.)
  • 👥 Multi-agent and room support
  • 📚 Easily ingest and interact with your documents
  • 💾 Retrievable memory and document store
  • 🚀 Highly extensible - create your own actions and clients
  • ☁️ Supports many models (local Llama, OpenAI, Anthropic, Groq, etc.)

Getting Started​

Coordination​

Guide to aligning and coordinating development efforts.

Resources for coordination of contributors.

ResourcePurpose
elizaos.github.io
Discord
GitHub
TelegramNot used?

Perfect use case (demonstrable value proposition) for a team of of Eliza Agents. Every person / organisation suffers from analysis paralysis and decision fatigue. Eliza Agents can sift though the noise and point out best lines of focused attention for maximum value extraction.

Architecture Overview​

Onboarding​

Educational resources on YouTube etc.

Other

Core Concepts​

  • Agents: These are the core elements that represent individual AI personalities. Agents operate within a runtime environment and interact with various platforms.
  • Character Files: These JSON files define the personality, knowledge, and behavior of each AI agent.
  • Clients: Clients act as interfaces between agents and specific platforms, such as Discord, Twitter, and Telegram. They handle platform-specific message formats and communication protocols.
  • Actions: Actions are predefined behaviors that agents can execute in response to messages, enabling them to perform tasks and interact with external systems.
  • Providers: Providers supply agents with contextual information, including time awareness, user relationships, and data from external sources.
  • Evaluators: These modules assess and extract information from conversations, helping agents track goals, build memory, and maintain context awareness.
  • Memory System: Eliza features a sophisticated memory management system that utilizes vector embeddings and relational database storage to store and retrieve information for agents.
  • Plugins: Plugins are modular way to extend the core functionality with additional features by bundling actions, evaluators, and providers. They are self-contained modules that can be easily added or removed to customize your agent's capabilities.

Character Files​

A characterfile implements the Character type and defines the character's:

  • Core identity and behavior
  • Model provider configuration
  • Client settings and capabilities
  • Interaction examples and style guidelines

Agents​

The AgentRuntime class manages the agent's core functions, including:

  • Message and Memory Processing: Storing, retrieving, and managing conversation data and contextual memory.
  • State Management: Composing and updating the agent’s state for a coherent, ongoing interaction.
  • Action Execution: Handling behaviors such as transcribing media, generating images, and following rooms.
  • Evaluation and Response: Assessing responses, managing goals, and extracting relevant information.

Providers​

A provider's primary purpose is to:

  • Supply dynamic contextual information
  • Integrate with the agent runtime
  • Format information for conversation templates
  • Maintain consistent data access

Actions​

Each Action consists of:

  • name: Unique identifier for the action
  • similes: Array of alternative names/variations
  • description: Detailed explanation of the action's purpose
  • validate: Function that checks if action is appropriate
  • handler: Implementation of the action's behavior
  • examples: Array of example usage patterns

Evaluators​

Evaluators enable agents to:

  • Build long-term memory
  • Track goal progress
  • Extract facts and insights
  • Maintain contextual awareness

Potential​

DeFi Potential​

Primary Value Drivers​

  • Barrier Reduction: AI agents can significantly lower the barrier to entry for DeFi protocols by explaining complex concepts and guiding users through transactions.
  • Clear Revenue Potential: DeFi applications offer obvious ways for agents to generate value through arbitrage and liquidity provision.
  • Automation Capabilities: Agents can automate complex DeFi operations like managing liquidity pools, monitoring token ranges, and handling yield strategies.

Specific Applications​

Liquidity Management

  • Automated monitoring of liquidity pool positions
  • Smart rebalancing when tokens go out of range
  • Automatic yield reinvestment and wallet management

Market Intelligence

  • Converting unstructured social data into actionable trading insights
  • Identifying top performers and reliable forecasters
  • Tracking community sentiment for trading signals

Marketing and Sales​

Shift from basic reply bots to product-focused AI agents that actively connect products with their ideal customers.

User Discovery​

  • Agents will actively identify people expressing specific needs or pain points across social platforms
  • Instead of random shilling, agents will match solutions to relevant user problems
  • Focus on helping users arrive at solutions rather than pushing products

Interaction Model​

Direct Problem Solving

  • Agents will respond to user queries with relevant product solutions
  • Act as a "new Google" by connecting users directly to products that solve their problems
  • Provide immediate, contextual support rather than generic marketing

Value-Based Engagement

  • Move beyond basic shilling to creating actual user value
  • Help users understand how products solve their specific challenges
  • Build trust through helpful interactions rather than spam

Effort vs Timeline​

Developers could build basic product-matching agents within a week. The key elements needed are:

  • Social monitoring capabilities
  • Natural language understanding of user needs
  • Product knowledge and recommendation systems
  • Trust-building conversation abilities

Marketplace of Trust​

The Marketplace of Trust is a sophisticated framework being developed by AI16z that creates a self-reinforcing feedback loop for AI agent trading. The system allows agents to gain insights from community members while building trust through transparent performance metrics.

Trust Mechanism​

Scoring System

  • Trust scores range from 0 to 1 (normalized to 100)
  • Scores are publicly displayed on leaderboards
  • Higher-scoring users gain more influence in the system

Validation Process

  • Agents simulate trades based on community suggestions
  • Performance results update trust scores automatically
  • System optimizes strategies through iterative learning

Data Flywheel​

The marketplace creates a powerful feedback loop through:

  • Historical trading performance tracking
  • Community suggestion integration via Discord
  • Quantitative metrics combined with qualitative insights

Safety Features​

Trading Restrictions

  • Minimum $1,000 liquidity requirement
  • $100,000 market cap threshold
  • No single entity can control more than 50% of tokens

Future Vision​

The marketplace aims to evolve into a decentralized mutual fund where:

  • AI agents can autonomously evaluate trading suggestions
  • Trust scores determine influence rather than capital
  • The system becomes increasingly intelligent through collective learning

The ultimate goal is to create a transparent, self-reinforcing system where AI agents can operate autonomously while maintaining accountability through community oversight and performance metrics.

Progress​

Project Plans

Notable Issues and PRs:

Partnerships​

Agent Ecosystem​

Family of Smart Agents spawned from the Eliza Framework.