Readme changes, docs index changes, removed images (#46)

* readme changes, docs index changes, removed images

Signed-off-by: yaron2 <schneider.yaron@live.com>

* fix links

Signed-off-by: yaron2 <schneider.yaron@live.com>

---------

Signed-off-by: yaron2 <schneider.yaron@live.com>

README.md

@@ -2,6 +2,8 @@
 
 Dapr Agents is a developer framework designed to build production-grade resilient AI agent systems that operate at scale. Built on top of the battle-tested Dapr project, it enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while leveraging built-in observability and stateful workflow execution to guarantee agentic workflows complete successfully, no matter how complex.
 
+![](./docs/img/logo-workflows.png)
+
 ## Key Features
 
 - **Scale and Efficiency**: Run thousands of agents efficiently on a single core. Dapr distributes single and multi-agent apps transparently across fleets of machines and handles their lifecycle.
@@ -24,7 +26,7 @@ Dapr Agents builds on top of Dapr's Workflow API, which under the hood represent
 
 ### Data-Centric AI Agents
 
-With built-in connectivity to over 50 enterprise data sources, Dapr Agents efficiently handle structured and unstructured data. From basic PDF extraction to large-scale database interactions, it enables seamless data-driven AI workflows with minimal code changes. Dapr's [bindings](https://docs.dapr.io/reference/components-reference/supported-bindings/) and [state stores](https://docs.dapr.io/reference/components-reference/supported-state-stores/) provide access to a large number of data sources that can be used to ingest data to an agent. [MCP integration](https://docs.anthropic.com/en/docs/agents-and-tools/mcp) is coming soon.
+With built-in connectivity to over 50 enterprise data sources, Dapr Agents efficiently handles structured and unstructured data. From basic [PDF extraction](./docs/concepts/concepts/arxiv_fetcher.md) to large-scale database interactions, it enables seamless data-driven AI workflows with minimal code changes. Dapr's [bindings](https://docs.dapr.io/reference/components-reference/supported-bindings/) and [state stores](https://docs.dapr.io/reference/components-reference/supported-state-stores/) provide access to a large number of data sources that can be used to ingest data to an agent. [MCP integration](https://docs.anthropic.com/en/docs/agents-and-tools/mcp) is coming soon.
 
 ### Accelerated Development
 
@@ -41,6 +43,14 @@ Dapr Agents provides a set of AI features that give developers a complete API su
 
 By building on top of Dapr, platform and infrastructure teams can apply Dapr's [resiliency policies](https://docs.dapr.io/operations/resiliency/policies/) to the database and/or message broker of their choice that are used by Dapr Agents. These policies include timeouts, retry/backoffs and circuit breakers. When it comes to security, Dapr provides the option to scope access to a given database or message broker to one or more agentic app deployments. In addition, Dapr Agents uses mTLS to encrypt the communication layer of its underlying components. 
 
+### Built-in Messaging and State Infrastructure
+
+* 🎯 **Service-to-Service Invocation**: Facilitates direct communication between agents with built-in service discovery, error handling, and distributed tracing. Agents can leverage this for synchronous messaging in multi-agent workflows.
+* ⚡️ **Publish and Subscribe**: Supports loosely coupled collaboration between agents through a shared message bus. This enables real-time, event-driven interactions critical for task distribution and coordination.
+* 🔄 **Durable Workflow**: Defines long-running, persistent workflows that combine deterministic processes with LLM-based decision-making. Dapr Agents uses this to orchestrate complex multi-step agentic workflows seamlessly.
+* 🧠 **State Management**: Provides a flexible key-value store for agents to retain context across interactions, ensuring continuity and adaptability during workflows.
+* 🤖 **Actors**: Implements the Virtual Actor pattern, allowing agents to operate as self-contained, stateful units that handle messages sequentially. This eliminates concurrency concerns and enhances scalability in agentic systems.
+
 ### Vendor-Neutral and Open Source
 
 As a part of **CNCF**, Dapr Agents is vendor-neutral, eliminating concerns about lock-in, intellectual property risks, or proprietary restrictions. Organizations gain full flexibility and control over their AI applications using open-source software they can audit and contribute to.
@@ -56,9 +66,6 @@ Prerequisites:
 
 ```bash
 dapr init
-```
-
-```bash
 pip install dapr-agents
 ```
 
@@ -76,4 +83,4 @@ Dapr Agents is an open-source project under the CNCF umbrella, and we welcome co
 
 ## Code of Conduct
 
-Please refer to our [Dapr Community Code of Conduct](https://github.com/dapr/community/blob/master/CODE-OF-CONDUCT.md)
+Please refer to our [Dapr Community Code of Conduct](https://github.com/dapr/community/blob/master/CODE-OF-CONDUCT.md)

docs/concepts/agents.md

@@ -8,7 +8,7 @@ Agents in `Dapr Agents` are autonomous systems powered by Large Language Models
 
 ### 1. LLM Integration
 
-Dapr Agents provides a unified interface to connect with LLM inference APIs, starting with [OpenAI models](https://platform.openai.com/docs/models). This abstraction allows developers to seamlessly integrate their agents with cutting-edge language models for reasoning and decision-making.
+Dapr Agents provides a unified interface to connect with LLM inference APIs. This abstraction allows developers to seamlessly integrate their agents with cutting-edge language models for reasoning and decision-making.
 
 ### 2. Structured Outputs
 

docs/index.md

@@ -1,25 +1,60 @@
-# Dapr Agents: Agentic Workflows Made Simple
+# Dapr Agents: A Framework for Agentic AI Systems
 
-[![pypi](https://img.shields.io/pypi/v/floki-ai.svg)](https://pypi.python.org/pypi/floki-ai)
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/floki-ai)](https://pypi.org/project/floki-ai/)
-[![GitHub Repo stars](https://img.shields.io/github/stars/Cyb3rWard0g/floki)](https://github.com/dapr/dapr-agents)
-[![license](https://img.shields.io/github/license/Cyb3rWard0g/floki.svg)](https://github.com/dapr/dapr-agents/blob/main/LICENSE)
+Dapr Agents is a developer framework designed to build production-grade resilient AI agent systems that operate at scale. Built on top of the battle-tested Dapr project, it enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while leveraging built-in observability and stateful workflow execution to guarantee agentic workflows complete successfully, no matter how complex.
 
-![](logo-workflows.png)
+![](./img/logo-workflows.png)
 
-Dapr Agents is an open-source framework for researchers and developers to experiment with and build LLM-based autonomous agents. It provides tools to create, orchestrate, and manage agents while seamlessly connecting to LLM inference APIs. Built on Dapr, the framework leverages a unified programming model that simplifies the creation of both deterministic stateful workflows and event-driven interactions. Using Dapr’s Virtual Actor pattern, Dapr Agents enables agents to function as independent, self-contained units that process messages sequentially, eliminating concurrency concerns while seamlessly integrating into larger workflows. It also facilitates agent collaboration through Dapr’s Pub/Sub integration, where agents communicate via a shared message bus, simplifying the design of workflows where tasks are distributed efficiently, and agents work together to achieve shared goals. By abstracting away infrastructure complexity, Dapr Agents lets developers focus on agent logic rather than deployment concerns, while benefiting from Dapr's integration with widely-adopted operational tools for monitoring, securing, and resiliency. By bringing together these features, Dapr Agents provides a powerful way to explore agentic workflows and the components that enable multi-agent systems to collaborate and scale, all powered by Dapr.
+## Key Features
 
-## Why Build on Dapr 🎩?
+- **Scale and Efficiency**: Run thousands of agents efficiently on a single core. Dapr distributes single and multi-agent apps transparently across fleets of machines and handles their lifecycle.
+- **Workflow Resilience**: Automatically retries agentic workflows and ensures task completion.
+- **Kubernetes-Native**: Easily deploy and manage agents in Kubernetes environments.
+- **Data-Driven Agents**: Directly integrate with databases, documents, and unstructured data by connecting to dozens of different data sources.
+- **Multi-Agent Systems**: Secure and observable by default, enabling collaboration between agents.
+- **Vendor-Neutral & Open Source**: Avoid vendor lock-in and gain flexibility across cloud and on-premises deployments.
+- **Platform-Ready**: Built-in RBAC, access scopes and declarative resources enable platform teams to integrate Dapr agents into their systems. 
 
-Dapr provides Dapr Agents with a unified programming model that simplifies the development of resilient and scalable systems by offering built-in APIs for features such as service invocation, Pub/Sub messaging, workflows, and even state management. These components, essential for defining agentic workflows, allow developers to focus on designing agents and workflows rather than rebuilding foundational features. By leveraging Dapr’s sidecar architecture and portable, event-driven runtime, Dapr Agents also enables agents to collaborate effectively, share tasks, and adapt dynamically across cloud and edge environments. This seamless integration brings together deterministic workflows and LLM-based decision-making into a unified programming model, making it easier to quickly experiment with multi-agent systems and scalable agentic workflows.
+## Why Choose Dapr Agents?
+
+### Scalable Workflows as a First Class Citizen
+
+Dapr Agents uses a [durable-execution workflow engine](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-overview/) that guarantees each agent task executes to completion in the face of network interruptions, node crashes and other types of disruptive failures. Developers do not need to know about the underlying concepts of the workflow engine - simply write an agent that performs any number of tasks and these will get automatically distributed across the cluster. If any task fails, it will be retried and recover its state from where it left off.
+
+### Cost-Effective AI Adoption
+
+Dapr Agents builds on top of Dapr's Workflow API, which under the hood represents each agent as an actor, a single unit of compute and state that is thread-safe and natively distributed, lending itself well to an agentic Scale-To-Zero architecture. This minimizes infrastructure costs, making AI adoption accessible to everyone. The underlying virtual actor model allows thousands of agents to run on demand on a single core machine with double-digit millisecond latency when scaling from zero. When unused, the agents are reclaimed by the system but retain their state until the next time they are needed. With this design, there's no trade-off between performance and resource efficiency.
+
+### Data-Centric AI Agents
+
+With built-in connectivity to over 50 enterprise data sources, Dapr Agents efficiently handles structured and unstructured data. From basic [PDF extraction](./concepts/arxiv_fetcher.md) to large-scale database interactions, it enables seamless data-driven AI workflows with minimal code changes. Dapr's [bindings](https://docs.dapr.io/reference/components-reference/supported-bindings/) and [state stores](https://docs.dapr.io/reference/components-reference/supported-state-stores/) provide access to a large number of data sources that can be used to ingest data to an agent. [MCP integration](https://docs.anthropic.com/en/docs/agents-and-tools/mcp) is coming soon.
+
+### Accelerated Development
+
+Dapr Agents provides a set of AI features that give developers a complete API surface to tackle common problems. Some of these include:
+
+- Multi-agent communications
+- Structured outputs
+- Multiple LLM providers
+- Contextual memory
+- Flexible prompting
+- Intelligent tool selection
+
+### Integrated Security and Reliability
+
+By building on top of Dapr, platform and infrastructure teams can apply Dapr's [resiliency policies](https://docs.dapr.io/operations/resiliency/policies/) to the database and/or message broker of their choice that are used by Dapr Agents. These policies include timeouts, retry/backoffs and circuit breakers. When it comes to security, Dapr provides the option to scope access to a given database or message broker to one or more agentic app deployments. In addition, Dapr Agents uses mTLS to encrypt the communication layer of its underlying components. 
+
+### Built-in Messaging and State Infrastructure
 
-### Key Dapr Features in Dapr Agents:
 * 🎯 **Service-to-Service Invocation**: Facilitates direct communication between agents with built-in service discovery, error handling, and distributed tracing. Agents can leverage this for synchronous messaging in multi-agent workflows.
 * ⚡️ **Publish and Subscribe**: Supports loosely coupled collaboration between agents through a shared message bus. This enables real-time, event-driven interactions critical for task distribution and coordination.
 * 🔄 **Durable Workflow**: Defines long-running, persistent workflows that combine deterministic processes with LLM-based decision-making. Dapr Agents uses this to orchestrate complex multi-step agentic workflows seamlessly.
 * 🧠 **State Management**: Provides a flexible key-value store for agents to retain context across interactions, ensuring continuity and adaptability during workflows.
 * 🤖 **Actors**: Implements the Virtual Actor pattern, allowing agents to operate as self-contained, stateful units that handle messages sequentially. This eliminates concurrency concerns and enhances scalability in agentic systems.
 
+### Vendor-Neutral and Open Source
+
+As a part of **CNCF**, Dapr Agents is vendor-neutral, eliminating concerns about lock-in, intellectual property risks, or proprietary restrictions. Organizations gain full flexibility and control over their AI applications using open-source software they can audit and contribute to.
+
 ## Getting Started
 
 <div class="grid cards" markdown>

quickstarts/README.md

@@ -7,7 +7,7 @@ A collection of examples demonstrating how to use Dapr Agents to build applicati
 To run these quickstarts, you'll need:
 - Python 3.10 or higher
 - An OpenAI API key
-- Dapr CLI and Docker (for workflow examples)
+- Dapr CLI and Docker (for agent workflow examples)
 
 ## Available Quickstarts