[Added] Readme File

README.md

@@ -1 +1,140 @@
-# agent-api
+# Lyzr Agent API
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+
+The Lyzr Agent API provides a modular and universal API framework for creating LLM-based agents. It simplifies the development of intelligent agents by offering three main components: Environment, Agent, and Inference. Each component is designed to streamline the setup, configuration, and execution of tasks and conversations.
+## 📄 Documentation
+
+Full documentation can be found here: [Lyzr Agent API Documentation](https://docs.lyzr.ai/agent-api/introduction)
+
+Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!
+
+# 💻 Example Usage
+## 1. Installation
+
+First, install the `lyzr-agent-api` package:
+```bash
+pip install lyzr-agent-api
+```
+For more detailed installation instructions, refer to the [installation guide on PyPI](https://pypi.org/project/lyzr-agent-api/).
+## 2. Initializing the Client
+
+To start using the API, initialize the `AgentAPI` client with your Lyzr API key:
+
+> **Tip:** You can generate your API key from the [Lyzr Agent API Studio](https://agent.lyzr.app).
+> 
+```python
+from lyzr_agent_api.client import AgentAPI
+
+client = AgentAPI(x_api_key="your-lyzr-api-key")
+```
+
+
+## 3. Creating an Environment
+The environment is a fundamental building block of the Lyzr Agent API. It defines the modules, features, tools available, and other configurations for your agent. Let's break down the components of the environment:
+
+
+
+1. **Features/Modules**: These can be sync modules (which can mutate the message stream) or background modules (which have read-only access).
+
+   - #### Sync Modules
+
+     | Module Type                    | Description                                                                 |
+     |--------------------------------|-----------------------------------------------------------------------------|
+     | `SELF_REFLECTION`              | Enables self-reflection capabilities for the agent, either by using the same model or a different model. |
+     | `OPEN_AI_RETRIEVAL_ASSISTANT`  | Offers retrieval capabilities using OpenAI's retriever.                     |
+     | `TOOL_CALLING`                 | Allows API tool calling. The agent can call any registered OpenAPI schema-supported API. |
+     | `KNOWLEDGE_BASE`               | Provides Lyzr RAG capabilities with fully customizable retriever configurations. |
+     | `LONG_TERM_MEMORY`             | Provides long contextual memory using multiple retrieval and summarization strategies. |
+     | `SHORT_TERM_MEMORY`            | Provides short contextual memory for a configurable number of messages (n), determining how many messages to fetch per inference. |
+     | `HUMANIZER`                    | Humanizes the output of the agent.                                          |
+
+   - #### Background Modules
+
+     | Module Type             | Description                                                                 |
+     |-------------------------|-----------------------------------------------------------------------------|
+     | `STRUCTURED_MEMORY`     | Acts as a structured JSON memory collector, storing structured information during conversations or task processes. |
+     | `LOGGING`               | Acts as a structured JSON memory collector, storing structured information during conversations or task processes. |
+
+2. **Tools**: External APIs that an agent can utilize.
+
+3. **llm_api_key**: The key for the language model being used.
+Define the configuration for your environment. In this example, we're creating an environment with short-term memory capabilities and configuring it to use an OpenAI model:
+
+```python
+from lyzr_agent_api.models.environment import EnvironmentConfig, FeatureConfig
+
+environment_config = EnvironmentConfig(
+    name="Test Environment",
+    features=[
+        FeatureConfig(
+            type="SHORT_TERM_MEMORY",
+            config={},
+            priority=0,
+        )
+    ],
+    tools=[],
+    llm_config={
+        "provider": "openai",
+        "model": "gpt-4o-mini",
+        "config": {
+            "temperature": 0.5,
+            "top_p": 0.9,
+        },
+        "env": {
+            "OPENAI_API_KEY": "your-openai-api-key"
+        }
+    },
+)
+environment = client.create_environment_endpoint(json_body=environment_config) 
+print(environment)
+# The response will include the environment ID, e.g. {'environment_id': '6wjbwhekndjxxxxx'}
+```
+
+## 4. Creating an Agent
+
+Once the environment is set up, create an agent within that environment. The agent uses the environment ID to operate and can be customized with a system prompt and a description:
+
+```python
+from lyzr_agent_api.models.agents import AgentConfig
+
+agent_config = AgentConfig(
+    env_id="environment-id",  # Replace with the actual environment ID
+    system_prompt="This is a system prompt.",
+    name="Test Agent",
+    agent_description="Description of the test agent",
+)
+agent = client.create_agent_endpoint(json_body=agent_config)
+print(agent)
+# The response will include the agent ID, e.g. {'agent_id': '66fcghvhxxxxxx'}
+```
+
+## 5. Interact with the Agent
+
+After creating the agent, you can initiate a chat session with it. Provide the user ID, agent ID, and a message to start the conversation:
+
+```python
+from lyzr_agent_api.models.chat import ChatRequest
+
+response = client.chat_with_agent(
+    json_body=ChatRequest(
+        user_id="user-id",
+        agent_id="agent-id",  # Replace with the actual agent ID
+        message="Hello",
+        session_id="session-id",
+    )
+)
+
+print(response)
+```
+
+## Support
+
+If you have any questions or need further assistance, please open an issue in this repository or contact support.
+
+## Contact
+For queries, reach us at contact@lyzr.ai