# panaversity/learn-agentic-ai

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3,908 stars · 901 forks · Jupyter Notebook · mit

## Links

- GitHub: https://github.com/panaversity/learn-agentic-ai
- awesome-repositories: https://awesome-repositories.com/repository/panaversity-learn-agentic-ai.md

## Topics

`a2a` `agentic-ai` `dapr` `dapr-pub-sub` `dapr-service-invocation` `dapr-sidecar` `dapr-workflow` `docker` `kafka` `kubernetes` `langmem` `mcp` `openai` `openai-agents-sdk` `openai-api` `postgresql-database` `rabbitmq` `rancher-desktop` `redis` `serverless-containers`

## Description

This project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals.

The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and long-term history, as well as evaluator-optimizer loops for iterative output refinement.

The project covers a broad range of technical capabilities, including retrieval augmented generation with knowledge graph grounding, the implementation of safety guardrails and human-in-the-loop oversight, and the use of stateful actor models. It also addresses the operational side of AI, including containerized deployment via Kubernetes and the management of machine-to-machine payments.

The materials are delivered as a series of technical guides and courses, utilizing Jupyter Notebooks for practical implementation.

## Tags

### Artificial Intelligence & ML

- [Autonomous AI Agent Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-ai-agent-frameworks.md) — Provides a framework for building self-directed agents that decompose high-level goals into executable plans. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/xx_real_agentic_ai))
- [Hybrid Short-and-Long Term Memory](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/memory-management-systems/long-term-memory-stores/hybrid-short-and-long-term-memory.md) — Implements a dual-memory architecture separating working memory for planning from long-term historical storage. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/xx_real_agentic_ai))
- [Agentic Workflow Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/orchestration-engines/ai-agent/agentic-workflow-orchestration.md) — Coordinates complex, long-running processes by delegating tasks to autonomous agents. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/07_daca_agent_native_dev))
- [Agent Collaboration Protocols](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-collaboration-protocols.md) — Facilitates direct collaboration and task delegation between agents using standardized messaging protocols. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/12_agent_to_agent))
- [Agent-to-Agent Communication](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-communication-protocols/agent-to-agent-communication.md) — Standardizes interaction and collaboration between diverse agents through dedicated communication protocols. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/README.md))
- [Agent Discovery](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-capabilities-skills-tooling/agent-management/agent-discovery.md) — Locates suitable agents within a deployment environment using intent-based queries and knowledge graphs. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/12_agent_to_agent))
- [Agent Metadata Resolvers](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-capabilities-skills-tooling/agent-management/agent-discovery/agent-metadata-resolvers.md) — Implements metadata resolvers to identify available agent skills and services via standardized endpoints. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/10_agent_discovery))
- [Agent Skill Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-capabilities-skills-tooling/agent-skill-frameworks.md) — Filters and selects agents based on specific capability identifiers to ensure the correct tool is chosen for a task. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/10_agent_discovery))
- [AI Agent Capabilities](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-capabilities-skills-tooling/ai-agent-capabilities.md) — Defines agents with specific instructions, safety guardrails, and capabilities to invoke external functions. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first))
- [AI Agent Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/coordination-and-routing/ai-agent-orchestrators.md) — Decomposes high-level intents into subtasks assigned to a hierarchy of domain-specific specialized agents. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05a_agentic_web))
- [Agent Tool Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/development-runtime-environments/ai-agent-infrastructure/agent-capability-registries/agent-tool-definitions.md) — Provides detailed specifications for agent tools, including required inputs, outputs, and operational constraints. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/18_agentia))
- [Model Context Protocol](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/model-integration-interfaces/model-context-protocol.md) — Implements a standardized protocol for models to access local data sources and external tools for improved context. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols))
- [Agentic Workflow Automation](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-workflow-automation.md) — Structures AI tasks using prompt chaining, routing, and self-correction loops for complex business processes. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/04_building_effective_agents))
- [AI Agent Development](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-development.md) — Provides a framework for developing autonomous software components that manage their own lifecycles and interact via natural language. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/AGENTIA_PROJECTS))
- [AI Guardrails](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-guardrails.md) — Defines policy rules and behavioral constraints to ensure autonomous agent behavior remains safe and unbiased. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05a_agentic_web))
- [Agent Governance](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-security-and-governance/agent-governance.md) — Provides safety guardrails, action whitelists, and human-in-the-loop escalation to ensure operational boundaries. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05c_agentic_org))
- [Cloud-Native Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-workload-orchestration/cloud-native-deployments.md) — Teaches container and Kubernetes orchestration for AI workloads to ensure resilient production environments.
- [Evaluator-Optimizer Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-loops/research-quality-refinement-loops/evaluator-optimizer-loops.md) — Employs evaluator-optimizer loops where one agent generates solutions and another provides iterative feedback for refinement.
- [Composable Agentic Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/composable-agentic-architectures.md) — Constructs complex agentic systems using modular patterns including prompt chaining and parallelization. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/04_building_effective_agents))
- [Conversation State Management](https://awesome-repositories.com/f/artificial-intelligence-ml/conversation-state-management.md) — Maintains interaction history across multiple requests to enable seamless, stateful dialogue without resending full context. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/00_openai_api))
- [Retrieval-Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/conversational-interfaces/retrieval-augmented-generation.md) — Implements retrieval augmented generation to ground model responses in external knowledge bases and factual context. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/agentic_ai_startup_roadmap))
- [Function Calling Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/function-calling-interfaces.md) — Implements systems that enable language models to execute external tools and API functions to extend their capabilities. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/agentic_ai_startup_roadmap))
- [Human-in-the-Loop Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/human-in-the-loop-workflows.md) — Routes low-confidence decisions to a human dashboard for approval before resuming automated workflows. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/comprehensive_guide_daca.md))
- [Multi-Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration.md) — Provides a framework for decomposing and delegating complex tasks across a team of specialized autonomous agents. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/comprehensive_guide_daca.md))
- [Multi-Agent Orchestration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration-frameworks.md) — Provides an architectural framework for coordinating and managing the collaboration of multiple intelligent agents.
- [Multi-Agent Orchestration Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration-systems.md) — Implements platforms for coordinating multiple autonomous agents to execute complex, collaborative workflows using standardized protocols.
- [Multi-Agent Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-systems.md) — Develops scalable AI systems using a distributed architecture where agents function as stateful actors. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/07_daca_agent_native_dev))
- [Tool-Using Agents](https://awesome-repositories.com/f/artificial-intelligence-ml/tool-using-agents.md) — Enables agents to trigger specific functions by matching request parameters with available capabilities through reasoning. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/18_agentia))
- [Client Initializations](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-communication-protocols/agent-client-protocols/client-initializations.md) — Automates the creation of communication clients from discovered URLs to establish managed agent connections. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/10_agent_discovery))
- [Agent Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-deployment.md) — Provisions and deploys new agents using declarative specifications or natural-language prompts. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/raos.md))
- [Operational Self-Healing](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-automation-frameworks/operational-self-healing.md) — Provides autonomous operations capabilities to monitor system health and patch configurations to resolve anomalies. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/raos.md))
- [Agentic Browser Controllers](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-browser-controllers.md) — Bridges autonomous agents to web browser elements using automation APIs as a foundation for reasoning. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05b_agentic_browsers))
- [Agentic Integration Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-integration-interfaces.md) — Develops standardized protocols and hooks to connect agents with external tools through defined parameters. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/04_building_effective_agents))
- [Model Provider Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/ai-model-orchestration/model-provider-integrations.md) — Offers unified interfaces for connecting and configuring various AI model providers supporting the Chat Completions API. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first))
- [Web Workflow Automations](https://awesome-repositories.com/f/artificial-intelligence-ml/audio-transcription/end-to-end-pipelines/web-workflow-automations.md) — Implements automated sequences that translate natural language goals into concrete browser actions like clicking and navigation. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05b_agentic_browsers))
- [Custom AI Assistant Development](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-ai-assistant-development.md) — Enables the creation of personalized AI assistants by combining specific personas with integrated tools and low-code workflows. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05b_agentic_browsers))
- [External Service Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/external-service-integrations.md) — Connects agents to external databases and services using a standardized protocol to maintain interaction context. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/comprehensive_guide_daca.md))
- [External Tool Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/external-service-integrations/external-knowledge-integrators/external-tool-integrations.md) — Provides a uniform server-client interface for agents to discover and invoke external resources and utilities. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05a_agentic_web))
- [Self-Correction Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-step-goal-execution/self-correction-loops.md) — Integrates reflection and monitoring to detect failures and automatically replan or retry tasks. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/xx_real_agentic_ai))
- [Agent Response Streams](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-agent-capabilities/real-time-streaming/agent-response-streams.md) — Streams agent responses and tool results asynchronously in real-time to handle long-running tasks. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/12_agent_to_agent))

### Education & Learning Resources

- [AI Agent Curricula](https://awesome-repositories.com/f/education-learning-resources/ai-agent-curricula.md) — Offers a structured educational curriculum and code samples for learning agentic design and autonomous system development.
- [RAG Implementation Guides](https://awesome-repositories.com/f/education-learning-resources/rag-implementation-guides.md) — Provides educational resources and step-by-step guides for building retrieval-augmented generation systems using graph databases.

### Part of an Awesome List

- [Interaction Protocols](https://awesome-repositories.com/f/awesome-lists/ai/ai-model-and-api-integration/tool-use-integrations/interaction-protocols.md) — Provides open protocols that serve as a universal language for models to interact with external tools. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05b_agentic_browsers))
- [Educational Agent Projects](https://awesome-repositories.com/f/awesome-lists/ai/educational-agent-projects.md) — Comprehensive learning repository for mastering agentic AI concepts.

### Data & Databases

- [Factual Grounding](https://awesome-repositories.com/f/data-databases/knowledge-graph-indexers/factual-grounding.md) — Integrates graph databases with language models to provide factual grounding and reduce hallucinations through structured data. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/16_graph_query_language))
- [Agent Memory Management](https://awesome-repositories.com/f/data-databases/session-management/agent-memory-management.md) — Implements systems for storing and retrieving historical context and reasoning traces to maintain interaction continuity. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/agentic_ai_startup_roadmap))
- [Agent State Persistence](https://awesome-repositories.com/f/data-databases/agent-state-persistence.md) — Implements mechanisms for storing and retrieving agent sessions and internal context across execution turns for persistent memory. ([source](https://github.com/panaversity/learn-agentic-ai#readme))
- [Knowledge Graphs](https://awesome-repositories.com/f/data-databases/entity-relationships/knowledge-graphs.md) — Uses graph-based structures to map semantic relationships between entities for grounding AI agents in structured data. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/16_graph_query_language))
- [Graph Querying](https://awesome-repositories.com/f/data-databases/graph-querying.md) — Provides tools and declarative pattern matching for traversing and querying complex connected data structures. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/16_graph_query_language))
- [LLM-to-Structured Data Converters](https://awesome-repositories.com/f/data-databases/structured-data-extraction/llm-to-structured-data-converters.md) — Transforms free-form AI responses into structured JSON data and function calls using defined explicit schemas. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/00_openai_api))

### DevOps & Infrastructure

- [AI Agent Deployments](https://awesome-repositories.com/f/devops-infrastructure/kubernetes-deployments/ai-agent-deployments.md) — Packaging and deploying multi-modal AI agents to Kubernetes clusters to ensure efficient scaling. ([source](https://github.com/panaversity/learn-agentic-ai#readme))
- [Automated Software Delivery](https://awesome-repositories.com/f/devops-infrastructure/automated-software-delivery.md) — Implements CI/CD pipelines and GitOps workflows to automate the delivery of agent applications. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/08_daca_deployment_guide))
- [Cloud Native Application Development](https://awesome-repositories.com/f/devops-infrastructure/cloud-native-orchestration/cloud-native-application-development.md) — Provides frameworks for building cloud-native applications designed for scalability and resilience within a cluster. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/09_ckad))
- [Containerized Application Runtimes](https://awesome-repositories.com/f/devops-infrastructure/containerized-application-deployments/containerized-application-runtimes.md) — Packages AI applications into portable container images using multi-stage builds for consistent execution environments. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/PROJECTS))
- [Containerized Deployment Orchestration](https://awesome-repositories.com/f/devops-infrastructure/containerized-deployment-orchestration.md) — Utilizes containerized deployment orchestration to ensure scalable and resilient execution of AI agents.
- [Kubernetes Application Deployments](https://awesome-repositories.com/f/devops-infrastructure/kubernetes-deployments/kubernetes-application-deployments.md) — Implements automated workflows for deploying containerized agent workloads and Helm charts into Kubernetes clusters. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/09_ckad))
- [LLM Hosting](https://awesome-repositories.com/f/devops-infrastructure/self-hosted-deployments/llm-hosting.md) — Provides guidelines for hosting and operating large language models on private infrastructure for data control. ([source](https://github.com/panaversity/learn-agentic-ai#readme))

### Security & Cryptography

- [Token Authentication](https://awesome-repositories.com/f/security-cryptography/agent-security/token-authentication.md) — Implements token and API key authentication to verify agent identity and authorize requests. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/15_authentication))
- [Skill Permissions](https://awesome-repositories.com/f/security-cryptography/ai-access-control-policies/permission-management/skill-permissions.md) — Defines authorization levels and security requirements for individual agent capabilities to restrict sensitive access. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/15_authentication))
- [AI Agent Security](https://awesome-repositories.com/f/security-cryptography/ai-agent-security.md) — Secures autonomous agents using transport-layer encryption and role-based access controls to protect data exchange. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/AGENTIA_PROJECTS))

### Software Engineering & Architecture

- [Actor Models](https://awesome-repositories.com/f/software-engineering-architecture/actor-models.md) — Architects agents as stateful actors that manage their own lifecycle and state within a distributed system.
- [Event-Driven AI Runtimes](https://awesome-repositories.com/f/software-engineering-architecture/event-driven-ai-runtimes.md) — Utilizes a publish-subscribe model to enable real-time, asynchronous reactions to agent events. ([source](https://github.com/panaversity/learn-agentic-ai/blob/main/comprehensive_guide_daca.md))
- [Microservices Design Patterns](https://awesome-repositories.com/f/software-engineering-architecture/microservices-design-patterns.md) — Implements architectural patterns for decomposing agent systems into microservices with service discovery and messaging. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/PROJECTS))
- [AI Pipeline Compositions](https://awesome-repositories.com/f/software-engineering-architecture/modular-program-composition/ai-pipeline-compositions.md) — Combines autonomous agents into higher-level services to prototype and deploy complex AI solutions. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/AGENTIA_PROJECTS))

### Development Tools & Productivity

- [Evaluator-Optimizer Loops](https://awesome-repositories.com/f/development-tools-productivity/agent-recipe-editing/ai-feedback-optimizations/evaluator-optimizer-loops.md) — Employs an evaluator-optimizer pattern where separate agents generate and refine solutions iteratively. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/04_building_effective_agents))

### Networking & Communication

- [Agent-to-Agent JSON-RPC](https://awesome-repositories.com/f/networking-communication/json-rpc-implementations/agent-to-agent-json-rpc.md) — Standardizes data exchange between distributed agent systems using JSON-RPC streaming over HTTP. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols))
- [Pub-Sub Messaging](https://awesome-repositories.com/f/networking-communication/pub-sub-messaging.md) — Uses a publish-subscribe messaging model to decouple agent communication for asynchronous real-time interactions.

### System Administration & Monitoring

- [Agent Performance Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/agent-performance-monitoring.md) — Tracks quantitative reliability metrics including task completion rates and tool-use accuracy. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/xx_real_agentic_ai))
- [AI Agent Execution Monitors](https://awesome-repositories.com/f/system-administration-monitoring/agent-performance-monitoring/ai-agent-execution-monitors.md) — Provides integrated tracing to visualize and debug the flow of agent actions for performance optimization. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first))
- [Distributed Tracing](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/distributed-tracing-execution-analysis/distributed-tracing.md) — Uses distributed tracing to track function calls and interactions across networks to diagnose logic flows. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/17_enterprise_features))
- [AI Agent Behavior Monitors](https://awesome-repositories.com/f/system-administration-monitoring/telemetry-and-monitoring-agents/ai-agent-behavior-monitors.md) — Provides mechanisms to measure task performance and reasoning processes using benchmarks and simulated conversations. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/05a_agentic_web))

### Testing & Quality Assurance

- [Agent Testing Suites](https://awesome-repositories.com/f/testing-quality-assurance/software-testing/e2e-integration-testing/end-to-end-testing/agent-testing-suites.md) — Provides automated testing suites to validate agent behavior and the reliability of agentic workflows. ([source](https://github.com/panaversity/learn-agentic-ai/tree/main/agentic_ai_startup_roadmap))