{"id":28193238,"url":"https://github.com/mjunaidca/ambient-agent-aiactor","last_synced_at":"2025-05-16T12:17:54.067Z","repository":{"id":293210187,"uuid":"983298270","full_name":"mjunaidca/ambient-agent-aiactor","owner":"mjunaidca","description":"Plug-and-Play agent runtime compatible with any agent type, from agent frameworks (OpenAI Agents SDK, LangGraph) to workflows (Temporal, Dapr Workflows).  🔁 Analogy: The BaseActor is a Dapr Virtual Actor - AI agent runtime equipped with event driven features","archived":false,"fork":false,"pushed_at":"2025-05-14T07:16:25.000Z","size":78,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-14T08:27:29.794Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mjunaidca.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-14T07:13:37.000Z","updated_at":"2025-05-14T07:16:28.000Z","dependencies_parsed_at":"2025-05-14T08:38:06.066Z","dependency_job_id":null,"html_url":"https://github.com/mjunaidca/ambient-agent-aiactor","commit_stats":null,"previous_names":["mjunaidca/ambient-agent-aiactor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fambient-agent-aiactor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fambient-agent-aiactor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fambient-agent-aiactor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fambient-agent-aiactor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mjunaidca","download_url":"https://codeload.github.com/mjunaidca/ambient-agent-aiactor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254527077,"owners_count":22085920,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-05-16T12:17:13.763Z","updated_at":"2025-05-16T12:17:54.058Z","avatar_url":"https://github.com/mjunaidca.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ambient AI Actor: Challenge to create AgentCore Core Capabilities\n\nThe is self challenge in progress.\n\n🎯 **End Goal**: A 🔌 Plug-and-Play agent runtime compatible with any agent type, from agent frameworks (OpenAI Agents SDK, LangGraph) to workflows (Temporal, Dapr Workflows).\n\n🔁 **Analogy**: The `BaseActor` is a Dapr Virtual Actor—a fully featured AI agent runtime equipped with:\n\n- Reactive and proactive behaviors\n- Event-driven processing   - Event-driven processing\n- Direct method handling\n- Internal and external system integration\n- Robust memory\n- Inherent resiliency\n- Planning capabilities\n- Task scheduling with reminders and timers\n\n## Prerequisites\n\n- Completed `01_actor_foundation` setup.\n- Python 3.12+, Dapr CLI, Tilt, and Rancher Desktop.\n\n## Clone and Run the Code\n\nClone the `01_actor_foundation` repo or continue from your existing setup:\n```bash\ntilt up\n```\n\nOpen:\n- Tilt UI: `http://localhost:1035`\n- Dapr Dashboard: `http://localhost:8080`\n- DACA Actor Interface: `http://localhost:30080/docs`\n- Metrics Tracing Interface: `http://localhost:9090`\n- Jaeger UI Interface: `http://localhost:16686`\n\n## Detailed Steps of the Challenge:\nStep 1: Foundation Setup (Completed)\nStep 2: Base Actor Interface (Completed)\nStep 3: Base Actor Skeleton (Completed)\nStep 4: Advanced Config (In Progress)\nStep 5: Bindings, PubSub (In Progress)\n\n## Next Steps / In Progress\n\nWith the `BaseActor` foundation firmly in place, the project will now focus on expanding its capabilities and building out the agentic ecosystem:\n\n1.  **Developing Specialized Actors**:\n    *   Implement concrete actor types that inherit from `BaseActor`. Examples include:\n        *   `ChatActor`: For managing conversational AI logic and user interactions.\n        *   `MemoryActor`: For handling persistent and contextual memory for agents.\n        *   `TriageAgent`: For intelligently routing tasks, messages, and information within a multi-agent system.\n        *   `ToolUsingActor`: For integrating and managing external tools and APIs via MCP.\n    *   Define specific interfaces, state models, and business logic for these specialized agents.\n2.  **Enhancing DACA Alignment**:\n    *   Further refine the integration of Model Context Protocol (MCP) for standardized tool use.\n    *   Implement and standardize Agent2Agent (A2A) communication patterns for interoperable agent collaboration.\n    *   Explore and integrate other Dapr components (e.g., Dapr Workflows for complex orchestrations) as needed.\n3.  **Real-World Application \u0026 Use Cases**:\n    *   Develop example applications and proof-of-concepts (PoCs) showcasing the `BaseActor` and specialized actors in action.\n    *   Target use cases like autonomous research agents, collaborative task execution systems, or sophisticated chatbots.\n4.  **Testing, Scalability, and Resilience**:\n    *   Conduct thorough unit, integration, and end-to-end testing for the `BaseActor` and new specialized actors.\n    *   Perform load testing to validate scalability and resilience under various conditions, aiming for DACA's high-concurrency goals.\n    *   Continuously monitor and improve observability using the existing Prometheus and Jaeger setup.\n5.  **Comprehensive Documentation**:\n    *   Provide detailed documentation for developers on how to use the `BaseActor`, create new specialized agents, and deploy the DACA Actor Runtime.\n    *   Include architectural diagrams, sequence diagrams, and best practices.\n\nThis ongoing work aims to leverage the robust `BaseActor` to build a versatile and powerful multi-agent system, pushing towards the Agentia World vision where AI agents collaborate seamlessly and effectively.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Fambient-agent-aiactor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjunaidca%2Fambient-agent-aiactor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Fambient-agent-aiactor/lists"}