https://github.com/mjunaidca/ambient-agent-aiactor
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
https://github.com/mjunaidca/ambient-agent-aiactor
Last synced: about 1 year ago
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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
- Host: GitHub
- URL: https://github.com/mjunaidca/ambient-agent-aiactor
- Owner: mjunaidca
- Created: 2025-05-14T07:13:37.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-14T07:16:25.000Z (about 1 year ago)
- Last Synced: 2025-05-14T08:27:29.794Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 76.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Ambient AI Actor: Challenge to create AgentCore Core Capabilities
The is self challenge in progress.
🎯 **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).
🔁 **Analogy**: The `BaseActor` is a Dapr Virtual Actor—a fully featured AI agent runtime equipped with:
- Reactive and proactive behaviors
- Event-driven processing - Event-driven processing
- Direct method handling
- Internal and external system integration
- Robust memory
- Inherent resiliency
- Planning capabilities
- Task scheduling with reminders and timers
## Prerequisites
- Completed `01_actor_foundation` setup.
- Python 3.12+, Dapr CLI, Tilt, and Rancher Desktop.
## Clone and Run the Code
Clone the `01_actor_foundation` repo or continue from your existing setup:
```bash
tilt up
```
Open:
- Tilt UI: `http://localhost:1035`
- Dapr Dashboard: `http://localhost:8080`
- DACA Actor Interface: `http://localhost:30080/docs`
- Metrics Tracing Interface: `http://localhost:9090`
- Jaeger UI Interface: `http://localhost:16686`
## Detailed Steps of the Challenge:
Step 1: Foundation Setup (Completed)
Step 2: Base Actor Interface (Completed)
Step 3: Base Actor Skeleton (Completed)
Step 4: Advanced Config (In Progress)
Step 5: Bindings, PubSub (In Progress)
## Next Steps / In Progress
With the `BaseActor` foundation firmly in place, the project will now focus on expanding its capabilities and building out the agentic ecosystem:
1. **Developing Specialized Actors**:
* Implement concrete actor types that inherit from `BaseActor`. Examples include:
* `ChatActor`: For managing conversational AI logic and user interactions.
* `MemoryActor`: For handling persistent and contextual memory for agents.
* `TriageAgent`: For intelligently routing tasks, messages, and information within a multi-agent system.
* `ToolUsingActor`: For integrating and managing external tools and APIs via MCP.
* Define specific interfaces, state models, and business logic for these specialized agents.
2. **Enhancing DACA Alignment**:
* Further refine the integration of Model Context Protocol (MCP) for standardized tool use.
* Implement and standardize Agent2Agent (A2A) communication patterns for interoperable agent collaboration.
* Explore and integrate other Dapr components (e.g., Dapr Workflows for complex orchestrations) as needed.
3. **Real-World Application & Use Cases**:
* Develop example applications and proof-of-concepts (PoCs) showcasing the `BaseActor` and specialized actors in action.
* Target use cases like autonomous research agents, collaborative task execution systems, or sophisticated chatbots.
4. **Testing, Scalability, and Resilience**:
* Conduct thorough unit, integration, and end-to-end testing for the `BaseActor` and new specialized actors.
* Perform load testing to validate scalability and resilience under various conditions, aiming for DACA's high-concurrency goals.
* Continuously monitor and improve observability using the existing Prometheus and Jaeger setup.
5. **Comprehensive Documentation**:
* Provide detailed documentation for developers on how to use the `BaseActor`, create new specialized agents, and deploy the DACA Actor Runtime.
* Include architectural diagrams, sequence diagrams, and best practices.
This 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.