AI Agent Frameworks
Frameworks for building swarms of Smart Agents.
Frameworks for developing Smart Agents that combine the best aspects of autonomous operation with human oversight and strategic direction.
| Name | Onboarding | Open Source | Language | Forks | Agents | Tokenomics | DePIN |
|---|---|---|---|---|---|---|---|
| ai16Z Eliza | ?/5 | TRUE | TypeScript | Eliza, SpartenDegen | ?/5 | TRUE | |
| Almanak | ?/5 | ||||||
| ARC RIG | ?/5 | TRUE | |||||
| Based Agent | ?/5 | 1 | |||||
| Griffain | ?/5 | ||||||
| Loria | ?/5 | - | |||||
| Morpheus | ?/5 | ||||||
| Pippin | ?/5 | P | |||||
| Solana Agent Kit | ?/5 | 1 | |||||
| Swarm Node | ?/5 | 0 | |||||
| Vercel | ?/5 | 1 | |||||
| Virtuals | ?/5 | 1 | aiXBT | aiXBT | |||
| Zerebro | ?/5 | 1 |
Context
Comparison Checklist
Evolve a framework to assess technical capabilities while accounting for ecosystem vitality and project sustainability.
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Framework Selection
- Cognitive depth
- Action breadth
- Coordination complexity
- Security posture
- Ecosystem maturity
- Compliance readiness
- Cost structure
- Talent availability
- Upgrade pathway
-
Character Development Process
- Assess knowledge ingestion tools (Eliza's folder2knowledge)
- Evaluate persona templating systems
- Test cross-platform behavior consistency
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Data Pipeline Configuration
- Map supported file formats/APIs
- Benchmark vector DB performance
- Validate context window management
-
Communication Layer Setup
- Implement Twitter/Telegram webhooks
- Configure on-chain interaction modules
- Test multi-channel response coherence
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Special Feature Integration
- Implement DePIN interfaces
- Configure metaverse asset bridges
- Set up DAO governance hooks
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Developer Experience Audit
- Measure setup time for new developers
- Evaluate debugging tool effectiveness
- Test CI/CD pipeline integration
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Trust and Security
- TEE,
-
Roadmap towards decentralised 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.
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documentation and onboarding
- One way flow
- Single source of truth
-
Leadership
- Clarity of North Star
- Consistency
- Alignment
-
Infrastructure Partners
Implementation
- Agent personality profiling (MBTI/NERIS models)
- Cross-chain identity anchoring
- Continuous learning feedback loops
- Governance layer integration
- Agent performance baselining
Differentiators
- Vertical Integration: Tight coupling of specialized hardware (ARC's Rust runtime)
- Social Primacy: Eliza's Twitter-native agent deployment
- Regulatory Preparedness: AutoGen's GDPR-compliant memory systems
- Economic Innovation: Swarm's tokenized compute markets
TACO Model
| Type | Complexity | Coordination | Use Case Examples | Framework Matches |
|---|---|---|---|---|
| Taskers | Low | None | Data entry bots, API callers | SmolAgents, Letta |
| Automators | Medium | Basic | Workflow engines, CRMs | AutoGen, LangChain |
| Collaborators | High | Advanced | Virtual teams, R&D agents | CrewAI, Semantic Kernel |
| Orchestrators | Extreme | Ecosystem | DAO governance | ARC RIG, Swarm |
- Coordination
- Use Cases
Research
- Cygaar
- aiccelerate.org