Agent Frameworks and Platforms
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 | |
| Clawbot / OpenClaw | TRUE | TypeScript | Open-core + managed agent platform | Non Crypto | |
| 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 | ||
| Manus | API | API-first platform | General-purpose autonomous task execution | Non Crypto | |
| 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
Standardized Capability Criteria (Tight Five)
Use this as the deterministic scoring model for agent packages/frameworks. Score each line 0-5, multiply by weight, and total to 100.
| Dimension | Weight | What to Measure |
|---|---|---|
| 1. Value Transformation + Distribution | 20 | Outcome delta (time/cost/revenue), who captures value (builder/user/network), and proof of repeatability. |
| 2. Performance + Early-Warning Control | 20 | Latency/cost/quality telemetry, evals, alerting, rollback, failure recovery, and drift detection. |
| 3. Platform Base (Ops + Tech Choices) | 25 | API/tooling maturity, security model, deployment options, observability, data/memory design, and interoperability. |
| 4. Know-How Compounding (Moat) | 15 | Reusable patterns, workflow portability, domain adapters, and how fast teams improve after each release. |
| 5. Players + Demand System | 20 | ICP fit, developer ecosystem, partner quality, governance clarity, and evidence of active adoption. |
Hard Gates (Fail Any = No-Go)
- Clear security posture: auth, secrets handling, webhook verification, auditability.
- Production reliability path: retries, idempotency, timeout strategy, incident process.
- Developer viability: working docs, reference implementation, and minimal onboarding path.
- Legal/commercial viability: clear licensing/terms aligned to target deployment model.
Scoring Bands
| Score | Decision |
|---|---|
| 80-100 | Pilot immediately; allocate integration team. |
| 65-79 | Controlled experiment; close specific gaps before scale. |
| 50-64 | Watchlist only; do not anchor roadmap. |
| <50 | Reject for now. |
Evidence Pack Required Per Framework
- Architecture diagram (runtime + trust boundaries).
- Benchmark sheet: latency, cost, success rate, recovery rate under failure.
- Security checklist mapped to your standards.
- Two real use-cases with measurable business outcomes.
- 90-day roadmap risk review.
Framework Scorecard Template
| Attribute | Score | Weight | Weighted Score | Notes |
|---|---|---|---|---|
| Value Transformation + Distribution | ?/5 | 20 | ? | |
| Performance + Early-Warning Control | ?/5 | 20 | ? | |
| Platform Base (Ops + Tech Choices) | ?/5 | 25 | ? | |
| Know-How Compounding (Moat) | ?/5 | 15 | ? | |
| Players + Demand System | ?/5 | 20 | ? |
Research
Agents with Agency
Which platform is best for developing agents with agency to transform the world in a positive direction?
What does it take to develop a mastermind?
- Data (Knowledge)
- Context (Working Memory)
- Prompts (Direction)
- Compute (Energy)
- Crypto (Persuasion)
- Character (Culture)
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 |
tip
Customer Success is all that matters