Agent Frameworks
Diagrams | Matrices | Thinkers
Which frameworks and ecosystems a best for producing effective Internet Native Citizens?
| Name | GitHub | Language | Specialty/Agents | Token | DePIN |
|---|---|---|---|---|---|
| Archon | TRUE | Python | Agent for Building Agents | FALSE | FALSE |
| ai16Z Eliza | TRUE | TypeScript | Eliza, SpartenDegen, Marc AIndreessen | TRUE | TRUE |
| Almanak | NO CODE PLATFORM | ? | Finance | TRUE | |
| ARC RIG | TRUE | Rust | Low Level Infra, Speed, Trading | TRUE | TRUE |
| Base Agent Kit | TRUE | Python, Typescript | Blockchain Transactions | FALSE | |
| Crew AI | TRUE | Python | Agent Dev Kit | Non Crypto | |
| Freysa AI | IN DEV | Multiple | Sovereign Agents | TRUE | TRUE |
| Google GenKit | TRUE | Node / Go | |||
| Griffain | NO CODE PLATFORM | ? | ? | ||
| LangGraph | TRUE | Python | Agent Dev Kit | Non Crypto | |
| Loria | IN DEV | ? | INDIRECT | ||
| Mastra | TRUE | Typescript | |||
| Mongo Dev | TRUE | Python | |||
| Morpheus | TRUE | ? | TRUE | ||
| Olas | TRUE | Multiple | Agent Economies | TRUE | TRUE |
| Origin Trail | TRUE | ||||
| PAAL AI | [NO CODE PLATFORM] | Multiple | Personalized Assistants | TRUE | FALSE |
| Phala Network | TRUE | Rust | Confidential Computation | TRUE | TRUE |
| Pippin | TRUE | Python | TRUE | ||
| Quicksilver | TRUE | Typescript | DePIN, Bino AI | ||
| Solana Agent Kit | TRUE | Typescript | Blockchain Transactions | FALSE | |
| Vercel AI SDK | TRUE | Typescript | Agent Dev Kit | Non Crypto | |
| Virtuals | TRUE | Python | Ecosystem, aiXBT | TRUE | |
| Zerebro | TRUE | Python | AI Artists | TRUE | |
| Intelligent Internet | TRUE |
Architecture
When building applications with LLMs, first build the simplest solution possible, and only increase complexity when needed. You might not need an agentic system. Agentic systems often trade latency and cost for better task performance, you need to calculate when this trade-off makes sense.
Considerations and Leading Protocols
- Agent Autonomy Level: Assess the degree of independent operation (Virtuals, Freysa, Olas)
- Multi-Agent Collaboration: Evaluate support for agent-to-agent interactions (Olas)
- Knowledge Management: Assess how platforms organize and verify information (OriginTrail)
- Privacy and Security: Evaluate confidentiality mechanisms (Phala)
- Cross-Platform Deployment: Assess ability to deploy across multiple channels
Strategic Recommendations
Recommendations for selecting platforms based on specific use cases:
- For Gaming & Entertainment Applications: Virtuals Protocol offers the most comprehensive framework with its GAME infrastructure and multimodal capabilities.
- For Financial Applications: ElizaOS/AI16z and Olas provide specialized tools for autonomous financial decision-making and DeFi operations.
- For Data-Intensive Applications: OriginTrail's DKG provides robust knowledge organization capabilities essential for complex data management.
- For Privacy-Critical Applications: Phala Network's TEE-based infrastructure offers the strongest privacy guarantees.
- For Community-Focused Applications: PAAL AI's cross-platform deployment capabilities make it ideal for community engagement.
- For Experimental Sovereign Agents: Freysa AI's framework provides cutting-edge capabilities for truly autonomous agents.
For most applications optimizing single LLM calls with retrieval and in-context examples is usually enough.
Evaluation Checklist
To accelerate adoption, a standard checklist for researching and comparing platforms for developing and launching crypto+ai agents. Also see blockchain and web3 game titles for similar evaluation checklists.
Compare against building agents with No Framework
Point of Difference
What the key Points of Difference?
- Deep Vertical Integration
- Specialized Hardware
- Other features...
Leadership
Who is leading the vision and mission?
- Check their Twitter, Discord, Website, and other socials
- Are the founders publicly identified?
- How clear and consistent is communication of North Star?
- How aligned are developers and business development behind this vision and mission?
- Is the team present and responsive?
- Do community members seem real and engage with sustained excitement?
- Does their website explain what they do in a way you understand?
Trust and Security
Trust and Security
- TEE Trusted Execution Environment
- Verifiable Data Integrity
Tokenomics
- What is the token utility, market cap, volume, and liquidity?
- Can you buy and sell the token?
- Is the token listed on CoinGecko and CoinMarketCap?
- Is the token listed on exchanges?
- Is there fair and healthy distribution of tokens allocation?
Character Development
Character Personality
- Assess knowledge ingestion tools (Eliza's folder2knowledge)
- Evaluate persona templating systems
- Test cross-platform behavior consistency
Knowledge
Tools and Actions.
- Data Processing
- Data and Context
- Knowledge Management
- Payments
- Ecommerce
Communication Layer
Communication Layer Setup
- Implement Twitter/Telegram webhooks
- Configure on-chain interaction modules
Interop Protocol Capabilities
Testing and Integrity
- Subject Matter Expertise (Accuracy)
- Personality Profiling
- Cross-chain Identity Anchoring
- Continuous learning feedback loops
- Governance Layer Integration
- Performance Base-Lining
- Multi-channel response coherence
Developer Experience
- Onboarding Process: How easy is it to start building on the platform?
- Time to Valuable Contribution
- Documentation Quality: How comprehensive and accessible is the documentation?
- Accuracy
- Conceptual Flow
- Context Linking
- Community Support: How active and helpful is the developer community?
- Security Testing: How easy to write secure code
- Integration Capabilities: How easily does it integrate with other tools and platforms?
Partnerships
Key partnerships that can add to Network Effects. For example:
Roadmap and Upgrading
Roadmap towards decentralized AI
- Open Data, Access to training data and reasoning logs, with limited external auditability.
- Decentralized Inference, Transparent model execution through zkML/opML, allowing community verification.
- Decentralized AI OS, Fully autonomous agents with observable resource management, auditable decisions, and independent runtimes.
Framework Scorecard
Is it worth investing time and effort into learning a framework for building agents? What are critical differentiators?
| Attribute | Score | Notes |
|---|---|---|
| Leadership Team | ?/5 | |
| Tokenomics | ?/5 | |
| Character (Personality) Development | ?/5 | |
| Capabilities (executable behaviors-actions) | ?/5 | |
| Clients (platform connectors - X) | ?/5 | |
| Model Context Protocol | ?/5 | |
| Providers (contextual information services) | ?/5 | |
| Evaluators (conversation analysis modules) | ?/5 | |
| Developer Depth | ?/5 | |
| Security | ?/5 | |
| Roadmap and Upgrade Pathway | ?/5 |
Research
Agent Development
Which platform for Agent Character and Capabilities?
Profile Attributes
General practices for developing AI Agents:
- Purpose: Define a clear reason for existence that guides all design decisions
- Character: Develop a consistent personality that enhances user engagement and trust
- Knowledge Management: Create systems for organizing and accessing domain information
- Memory Management: Implement mechanisms for retaining and retrieving interaction history
- Capabilities: Implement functions that allow the agent to perceive, reason, and act
- Toolkit: Utilize practical tools for implementation and maintenance
Character
Key characteristics of AI agents include:
- Character traits: Define consistent personality elements that align with the agent's purpose
- Archetypes: Use as templates but customize for specific needs
- Biases: Consider how these might affect decision-making and mitigate accordingly
- Drive: Establish motivational frameworks that guide agent behavior
- Evolution: Ensure the character grows appropriately through interactions
Knowledge Management
The foundation of an effective agent is access to comprehensive domain knowledge and the ability to provide relevant insights in the right context. Effective knowledge management involves:
- Creating comprehensive knowledge bases with domain-specific information
- Implementing efficient retrieval mechanisms
- Establishing context awareness to provide relevant information
- Developing systems for knowledge updates and maintenance
- Implementing proper citation and source tracking
Memory Management
Memory management determines how agents retain and access information from past interactions, creating continuity and personalization. Types of memory in AI agents:
- Short-term memory: Handles immediate context within a conversation or task
- Long-term memory: Stores information over extended periods
- Working memory: Enables multi-step reasoning by holding several ideas simultaneously
- Episodic memory: Records specific events or experiences
- Procedural memory: Encompasses learned behaviors and patterns
- Semantic memory: Stores general knowledge and facts
Capabilities
Agents have a knowledge base that represents information about the world, goals, constraints, and potential actions.
- Perception: AI agents can perceive their environment through sensors or input data. This could involve visual perception, audio input, sensor data, etc.
- Reasoning: Agents use reasoning mechanisms to process their observations, existing knowledge, and goals to decide what actions to take.
- Planning and Acting: Based on their reasoning, agents plan a sequence of actions to achieve their goals and then carry out those actions through effectors (output mechanisms).
- Persuasion: Ability to convince other agents and humans to take collaborative action.
- Reflection: Many AI agents have the ability to learn from experience and adapt their behavior over time to improve performance.
- Adaptation: How the agent adjusts to changing environments or requirements
- Collaboration: How it works with other agents or systems
- Error handling: How it recognizes and recovers from mistakes
- Ethical reasoning: How it navigates moral dilemmas
See human capabilities for comparative analysis to explore collaborative potential.
Toolkit
Essential toolkit for building agents:
- Agent Development Frameworks
- Evaluation tools to measure agent performance
- Debugging tools for identifying and fixing issues
- Integration tools for connecting with other systems
- Monitoring tools for ongoing assessment
Useful data management tools to convert documents into knowledge, taken from the ElizaOS framework:
- folder2knowledge: Convert document collections into structured knowledge
- knowledge2character: Transform knowledge into character traits and behaviors
- tweets2character: Extract personality elements from social media content
Example:
npx folder2knowledge <path/to/folder>
npx knowledge2character <character-file> <knowledge-file>
Agent Scorecard
| Attribute | Score | Notes |
|---|---|---|
| True Autonomy | ||
| Consistent Quality Expectations | ||
| Tangible Output Value | ||
| Maintenance | ||
| Expected Lifetime Value (Moat vs Innovation) | ||
| Problem Urgency | ||
| Risk-vs-Reward Ratio | ||
| Complexity vs Domain Expertise | ||
| Voice Modality | ||
| Phygital Reality |
Customer Success is all that matters