NEAR AI Development

Comprehensive guide for building AI agents and AI-powered applications on NEAR Protocol, including NEAR AI integration, agent workflows, and AI model deployment.

When to Apply

Reference these guidelines when:

• Building AI agents on NEAR

• Integrating AI models with NEAR smart contracts

• Creating agent-based workflows

• Implementing AI-powered dApps

• Using NEAR AI infrastructure

• Building with NEAR AI Assistant

Rule Categories by Priority

Priority Category Impact Prefix

1 Agent Architecture CRITICAL arch-

2 AI Integration HIGH ai-

3 Agent Communication HIGH comm-

4 Model Deployment MEDIUM-HIGH model-

5 Agent Workflows MEDIUM workflow-

6 Security & Privacy MEDIUM security-

7 Best Practices MEDIUM best-

Quick Reference

1. Agent Architecture (CRITICAL)

• arch-agent-structure

• Design modular agent architecture

• arch-state-management

• Manage agent state on-chain vs off-chain

• arch-agent-registry

• Register agents in NEAR AI registry

• arch-composability

• Build composable agents

• arch-agent-capabilities

• Define clear agent capabilities

2. AI Integration (HIGH)

• ai-model-selection

• Choose appropriate AI models

• ai-inference-endpoints

• Use NEAR AI inference endpoints

• ai-prompt-engineering

• Design effective prompts for agents

• ai-context-management

• Manage conversation context

• ai-response-validation

• Validate and sanitize AI responses

3. Agent Communication (HIGH)

• comm-agent-protocol

• Implement standard agent communication protocols

• comm-message-format

• Use structured message formats

• comm-async-messaging

• Handle asynchronous agent communication

• comm-multi-agent

• Coordinate multiple agents

• comm-human-in-loop

• Implement human-in-the-loop patterns

4. Model Deployment (MEDIUM-HIGH)

• model-hosting

• Deploy models on NEAR AI infrastructure

• model-versioning

• Version and update AI models

• model-optimization

• Optimize models for inference

• model-monitoring

• Monitor model performance

• model-fallbacks

• Implement fallback strategies

5. Agent Workflows (MEDIUM)

• workflow-task-planning

• Implement agent task planning

• workflow-execution

• Execute multi-step workflows

• workflow-error-handling

• Handle workflow errors gracefully

• workflow-state-persistence

• Persist workflow state

• workflow-composability

• Compose workflows from smaller tasks

6. Security & Privacy (MEDIUM)

• security-input-validation

• Validate user inputs to agents

• security-output-sanitization

• Sanitize agent outputs

• security-access-control

• Implement agent access control

• security-data-privacy

• Protect user data privacy

• security-prompt-injection

• Prevent prompt injection attacks

7. Best Practices (MEDIUM)

• best-error-messages

• Provide clear error messages

• best-logging

• Log agent interactions for debugging

• best-testing

• Test agent behavior comprehensively

• best-documentation

• Document agent capabilities and APIs

• best-user-feedback

• Collect and incorporate user feedback

How to Use

Read individual rule files for detailed explanations and code examples:

rules/arch-agent-structure.md rules/ai-inference-endpoints.md

Each rule file contains:

• Brief explanation of why it matters

• Incorrect code example with explanation

• Correct code example with explanation

• Additional context and NEAR AI-specific patterns

NEAR AI Components

NEAR AI Hub

Central registry for AI agents, models, and datasets on NEAR.

NEAR AI Assistant

Infrastructure for building conversational AI agents.

Agent Registry

On-chain registry for discovering and interacting with agents.

Inference Endpoints

Decentralized inference infrastructure for AI models.

Resources

• NEAR AI Documentation: https://docs.near.ai/

• NEAR AI Hub: https://app.near.ai/

• NEAR AI GitHub: https://github.com/near/nearai

• Agent Examples: https://github.com/near/nearai/tree/main/examples

• NEAR AI Research: https://near.ai/research

Full Compiled Document

For the complete guide with all rules expanded: AGENTS.md