From 118ebf3fff3462934edaa02f44accddd84d36e9c Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Sun, 29 Sep 2024 20:41:35 -0400 Subject: [PATCH] doc: v0 of README Signed-off-by: Diwank Singh Tomer --- README.md | 699 +++++++++++++++++++++++++++++++++++++------------ example.py | 107 ++++++++ example.ts | 117 +++++++++ image.png | Bin 426141 -> 0 bytes v0.3_README.md | 19 +- 5 files changed, 767 insertions(+), 175 deletions(-) create mode 100644 example.py create mode 100644 example.ts delete mode 100644 image.png diff --git a/README.md b/README.md index 0f49831ff..973bd63d0 100644 --- a/README.md +++ b/README.md @@ -1,33 +1,134 @@ - English | [中文翻译](/README-CN.md)
- julep + julep
-

-Build powerful AI applications with stateful agents, complex workflows, and integrated tools. -

- -

-
- Explore the docs » -
-
- Report Bug - · - Request Feature - · - Join Our Discord - · - X - · - LinkedIn - +

+
+ Explore Docs + · + Discord + · + 𝕏 + · + LinkedIn

@@ -41,231 +142,503 @@ Build powerful AI applications with stateful agents, complex workflows, and inte GitHub License

---- +## Introduction -## 🚀 Upcoming Release: v0.4 Alpha +Julep is an open-source platform for creating persistent AI agents with customizable workflows. It provides tools to develop, manage, and deploy AI-driven applications, focusing on flexibility and ease of use. -**Release Date**: October 7 (Hacktoberfest Launch) +With Julep, you can: +- Quickly develop AI agents that retain context and state across interactions +- Design and execute sophisticated workflows tailored to your AI agents +- Seamlessly integrate various tools and APIs into your AI workflows +- Effortlessly manage persistent sessions and user interactions -We are thrilled to announce the upcoming release of **Julep v0.4 Alpha** on **October 7**, just in time for **Hacktoberfest**! +Whether you're developing a chatbot, automating tasks, or building a complex AI assistant, Julep provides the flexibility and features you need to turn your ideas into reality swiftly and efficiently. -### **Key Highlights** -#### **New Feature: Tasks** +
+Here's a quick python example: -- **Autonomous Workflows**: Unlock the ability for agents to perform long-term, multi-step tasks autonomously. Define complex workflows that enable your agents to operate with minimal intervention. +

+from julep import Julep, AsyncJulep
 
-- **Harness OpenAI's o1 Models**: Leverage the advanced reasoning and planning capabilities of OpenAI's o1 models. Perfect for orchestrating intricate and prolonged tasks that require sophisticated understanding and execution.
+# 🔑 Initialize the Julep client
+#     Or alternatively, use AsyncJulep for async operations
+client = Julep(api_key="your_api_key")
 
-- **100+ Integrations**: Seamlessly integrate with popular tools and APIs, including **GitHub**, **Salesforce**, **File Manager**, **Code Execution**, and more. Expand your agents' capabilities to perform a wide range of actions.
+##################
+## 🤖 Agent 🤖 ##
+##################
 
-#### **Rewritten from Scratch Based on Developer Feedback**
+# Create a research agent
+agent = client.agents.create(
+    name="Research Agent",
+    model="claude-3.5-sonnet",
+    about="You are a research agent designed to handle research inquiries.",
+)
 
-- **Enhanced Doc Search**: Experience a significant improvement in document retrieval. Agents can now access the most relevant information faster, ensuring more accurate and contextually appropriate interactions.
+# 🔍 Add a web search tool to the agent
+client.agents.tools.create(
+    agent_id=agent.id,
+    name="web_search",  # Should be python valid variable name
+    description="Use this tool to research inquiries.",
+    integration={
+        "provider": "brave",
+        "method": "search",
+        "setup": {
+            "api_key": "your_brave_api_key",
+        },
+    },
+)
 
-- **Easier to Use**: We've streamlined the user experience with simplified APIs and more intuitive interfaces. Building powerful AI applications is now more straightforward than ever.
+#################
+## 💬 Chat 💬 ##
+#################
 
-- **Multi-Agent Sessions**: Support for sessions with multiple agents and users. Enable complex interaction patterns, collaborative problem-solving, and more dynamic conversations within your applications.
+# Start an interactive chat session with the agent
+session = client.sessions.create(
+    agent_id=agent.id,
+    context_overflow="adaptive",  # 🧠 Julep will dynamically compute the context window if needed
+)
 
-- **Extensive Integrations**: Incorporate a multitude of popular tools and services directly into your AI applications, enhancing functionality and providing richer experiences for your users.
+# 🔄 Chat loop
+while (user_input := input("You: ")) != "exit":
+    response = client.sessions.chat(
+        session_id=session.id,
+        message=user_input,
+    )
 
-### **Call to Action**
+    print("Agent: ", response.choices[0].message.content)
 
-- **Join Our Discord Community**: Be among the first to access Julep v0.4 Alpha! [Join our Discord](https://discord.com/invite/JTSBGRZrzj) to get early access and API keys. Engage with other developers, share your projects, and provide feedback.
 
----
+#################
+## 📋 Task 📋 ##
+#################
 
-## Why Julep?
-We've built a lot of AI apps and understand the challenges in creating complex, stateful applications with multiple agents and workflows.
+# Create a recurring research task for the agent
+task = client.tasks.create(
+    agent_id=agent.id,
+    name="Research Task",
+    description="Research the given topic every 24 hours.",
+    #
+    # 🛠️ Task specific tools
+    tools=[
+        {
+            "name": "send_email",
+            "description": "Send an email to the user with the results.",
+            "api_call": {
+                "method": "post",
+                "url": "https://api.sendgrid.com/v3/mail/send",
+                "headers": {"Authorization": "Bearer YOUR_SENDGRID_API_KEY"},
+            },
+        }
+    ],
+    #
+    # 🔢 Task main steps
+    main=[
+        #
+        # Step 1: Research the topic
+        {
+            # `_` (underscore) variable refers to the previous step's output
+            # Here, it points to the topic input from the user
+            "prompt": "Look up topic '{{_.topic}}' and summarize the results.",
+            "tools": [{"ref": {"name": "web_search"}}],  # 🔍 Use the web search tool from the agent
+            "unwrap": True,
+        },
+        #
+        # Step 2: Send email with research results
+        {
+            "tool": "send_email",
+            "arguments": {
+                "subject": "Research Results",
+                "body": "'Here are the research results for today: ' + _.content",
+                "to": "inputs[0].email",  # Reference the email from the user's input
+            },
+        },
+        #
+        # Step 3: Wait for 24 hours before repeating
+        {"sleep": "24 * 60 * 60"},
+    ],
+)
+
+# 🚀 Start the recurring task
+client.executions.create(task_id=task.id, input={"topic": "Python"})
+
+# 🔁 This will run the task every 24 hours,
+#    research for the topic "Python", and
+#    send the results to the user's email
+
+
-**The Problems** -1. Building AI applications with memory, knowledge, and tools is complex and time-consuming. -2. Managing long-running tasks and complex workflows in AI applications is challenging. -3. Integrating multiple tools and services into AI applications requires significant development effort. ---- ## Features -- **Stateful Agents**: Create and manage agents with built-in conversation history and memory. -- **Complex Workflows**: Define and execute multi-step tasks with branching, parallel execution, and error handling. -- **Integrated Tools**: Easily incorporate a wide range of tools and external services into your AI applications. -- **Flexible Session Management**: Support for various interaction patterns like one-to-many and many-to-one between agents and users. -- **Built-in RAG**: Add, delete & update documents to provide context to your agents. -- **Asynchronous Task Execution**: Run long-running tasks in the background with state management and resumability. -- **Multi-Model Support**: Switch between different language models (OpenAI, Anthropic, Ollama) while preserving state. -- **Task System**: Define and execute complex, multi-step workflows with parallel processing and error handling. ---- -## Quickstart -### Option 1: Use the Julep Cloud -Our hosted platform is in Beta! +Julep simplifies the process of building persistent AI agents with customizable workflows. Key features include: -To get access: -- Head over to https://platform.julep.ai -- Generate and add your `JULEP_API_KEY` in `.env` +- **Persistent AI Agents**: Create and manage AI agents that maintain context across interactions. +- **Customizable Workflows**: Design complex, multi-step AI workflows using Tasks. +- **Tool Integration**: Seamlessly integrate various tools and APIs into your AI workflows. +- **Document Management**: Efficiently manage and search through documents for your agents. +- **Session Management**: Handle persistent sessions for continuous interactions. +- **Flexible Execution**: Support for parallel processing, conditional logic, and error handling in workflows. -### Option 2: Install and run Julep locally -Head over to docs on [self-hosting](https://docs.julep.ai/guides/self-hosting) to see how to run Julep locally! +## Installation -### Installation +To get started with Julep, install it using [npm](https://www.npmjs.com/package/@julep/sdk) or [pip](https://pypi.org/project/julep/): + +```bash +npm install @julep/sdk +``` + +or ```bash pip install julep ``` -### Setting up the `client` +> [!TIP] +> ~~Get your API key [here](https://app.julep.ai/api-keys).~~ +> +> While we are in beta, you can reach out on [Discord](https://discord.com/invite/JTSBGRZrzj) to get your API key. -```python -from julep import Client -import os +## Quick Start Guide -base_url = os.environ.get("JULEP_API_URL") -api_key = os.environ.get("JULEP_API_KEY") +### Step 1: Import Julep -client = Client(api_key=api_key, base_url=base_url) +First, import the Julep SDK into your project: + +```javascript +const Julep = require('@julep/sdk'); ``` -### Create an agent -Agent is the object to which LLM settings like model, temperature along with tools are scoped to. +or ```python -agent = client.agents.create( - name="Jessica", - model="gpt-4", - tools=[], # Tools defined here - about="A helpful AI assistant", - instructions=["Be polite", "Be concise"] -) +from julep import AsyncJulep ``` -### Create a user -User is the object which represents the user of the application. +### Step 2: Initialize the Agent -Memories are formed and saved for each user and many users can talk to one agent. +Create a new agent with basic settings: -```python -user = client.users.create( - name="Anon", - about="Average nerdy techbro/girl spending 8 hours a day on a laptop", -) +```javascript +const julep = new Julep({ apiKey: 'your-api-key' }); + +const agent = await julep.agents.create({ + name: 'ResearchAssistant', + model: 'gpt-4-turbo', + about: "You are a creative storytelling agent that can craft engaging stories and generate comic panels based on ideas.", +}); ``` -### Create a session -A "user" and an "agent" communicate in a "session". System prompt goes here. +or ```python -situation_prompt = """You are Jessica, a helpful AI assistant. -You're here to assist the user with any questions or tasks they might have.""" -session = client.sessions.create( - user_id=user.id, - agent_id=agent.id, - situation=situation_prompt +client = AsyncJulep(api_key="your_api_key") + +agent = await client.agents.create( + name="Storytelling Agent", + model="gpt-4-turbo", + about="You are a creative storytelling agent that can craft engaging stories and generate comic panels based on ideas.", ) ``` -### Start a stateful conversation -`session.chat` controls the communication between the "agent" and the "user". +### Step 3: Chat with the Agent + +Start an interactive chat session with the agent: + +```javascript +const session = await julep.sessions.create({ + agentId: agent.id, +}); + +// Send messages to the agent +const response = await julep.sessions.chat({ + sessionId: session.id, + message: 'Hello, can you tell me a story?', +}); -It has two important arguments; -- `recall`: Retrieves the previous conversations and memories. -- `remember`: Saves the current conversation turn into the memory store. +console.log(response); +``` -To keep the session stateful, both need to be `True` +or ```python -user_msg = "Hey Jessica, can you help me with a task?" -response = client.sessions.chat( +session = await client.sessions.create(agent_id=agent.id) + +# Send messages to the agent +response = await client.sessions.chat( session_id=session.id, - messages=[ + message="Hello, can you tell me a story?", +) + +print(response) +``` + + +### Step 4: Create a multi-step Task + +Let's define a multi-step task to create a story and generate a paneled comic strip based on an input idea: + +```python +# 🛠️ Add an image generation tool (DALL·E) to the agent +await client.agents.tools.create( + agent_id=agent.id, + name="image_generator", + description="Use this tool to generate images based on descriptions.", + integration={ + "provider": "dalle", + "method": "generate_image", + "setup": { + "api_key": "your_dalle_api_key", + }, + }, +) + +# 📋 Task +# Create a task that takes an idea and creates a story and a 4-panel comic strip +task = await client.tasks.create( + agent_id=agent.id, + name="Story and Comic Creator", + description="Create a story based on an idea and generate a 4-panel comic strip illustrating the story.", + main=[ + # Step 1: Generate a story and outline into 4 panels { - "role": "user", - "content": user_msg, - "name": "Anon", - } + "prompt": [ + { + "role": "system", + "content": "You are {{agent.name}}. {{agent.about}}" + }, + { + "role": "user", + "content": ( + "Based on the idea '{{_.idea}}', write a short story suitable for a 4-panel comic strip. " + "Provide the story and a numbered list of 4 brief descriptions for each panel illustrating key moments in the story." + ), + }, + ], + "unwrap": True, + }, + # Step 2: Extract the panel descriptions and story + { + "evaluate": { + "story": "_.split('1. ')[0].strip()", + "panels": "re.findall(r'\\d+\\.\\s*(.*?)(?=\\d+\\.\\s*|$)', _)", + } + }, + # Step 3: Generate images for each panel using the image generator tool + { + "foreach": { + "in": "_.panels", + "do": { + "tool": "image_generator", + "arguments": { + "description": "_", + }, + }, + }, + }, + # Step 4: Generate a catchy title for the story + { + "prompt": [ + { + "role": "system", + "content": "You are {{agent.name}}. {{agent.about}}" + }, + { + "role": "user", + "content": "Based on the story below, generate a catchy title.\n\nStory: {{outputs[1].story}}", + }, + ], + "unwrap": True, + }, + # Step 5: Return the story, the generated images, and the title + { + "return": { + "title": "outputs[3]", + "story": "outputs[1].story", + "comic_panels": "[output.image.url for output in outputs[2]]", + } + }, ], - recall=True, - remember=True, +) +``` + +> [!TIP] +> node.js version of this is similar. + +### Step 5: Execute the Task + +```python +# 🚀 Execute the task with an input idea +execution = await client.executions.create( + task_id=task.id, + input={"idea": "A cat who learns to fly"} ) -print(response.response[0][0].content) +# 🎉 Watch as the story and comic panels are generated +await client.executions.stream(execution_id=execution.id) ``` ---- +This example demonstrates how to create an agent with a custom tool, define a complex task with multiple steps, and execute it to generate a creative output. -## Core Concepts + -### Agent -An Agent in Julep is the main orchestrator of your application. It's backed by foundation models like GPT-4 or Claude and can use tools, documents, and execute complex tasks. +> [!TIP] +> You can find another node.js example [here](example.ts) or python example [here](example.py). -### User -Users in Julep represent the end-users of your application. They can be associated with sessions and have their own documents and metadata. +## Understanding Tasks -### Session -Sessions manage the interaction between users and agents. They maintain conversation history and context. +Tasks are the core of Julep's workflow system. They allow you to define complex, multi-step AI workflows that your agents can execute. Here's a brief overview of task components: -### Tool -Tools are functions that agents can use to perform specific actions or retrieve information. +- **Name and Description**: Each task has a unique name and description for easy identification. +- **Main Steps**: The core of a task, defining the sequence of actions to be performed. +- **Tools**: Optional integrations that extend the capabilities of your agent during task execution. -### Doc -Docs are collections of text snippets that can be associated with agents or users and are used for context retrieval. +### Types of Workflow Steps -### Task -Tasks are complex, multi-step workflows that can be defined and executed by agents. +Tasks in Julep can include various types of steps: -### Execution -An Execution is an instance of a Task that has been started with some input. It goes through various states as it progresses. +1. **Prompt**: Send a message to the AI model and receive a response. + ```python + {"prompt": "Analyze the following data: {{data}}"} + ``` ---- +2. **Tool Call**: Execute an integrated tool or API. + ```python + {"tool": "web_search", "arguments": {"query": "Latest AI developments"}} + ``` -## API and SDKs +3. **Evaluate**: Perform calculations or manipulate data. + ```python + {"evaluate": {"average_score": "sum(scores) / len(scores)"}} + ``` -To use the API directly or to take a look at request & response formats, authentication, available endpoints and more, please refer to the [API Documentation](https://docs.julep.ai/api-reference/agents-api/agents-api) +4. **Conditional Logic**: Execute steps based on conditions. + ```python + {"if": "score > 0.8", "then": [...], "else": [...]} + ``` -### Python SDK +5. **Loops**: Iterate over data or repeat steps. + ```python + {"foreach": {"in": "data_list", "do": [...]}} + ``` -To install the Python SDK, run: +| Step Name | Description | Input | +|--------------------|--------------------------------------------------------------------------------------------------|------------------------------------------------------| +| **Prompt** | Send a message to the AI model and receive a response. | Prompt text or template | +| **Tool Call** | Execute an integrated tool or API. | Tool name and arguments | +| **Evaluate** | Perform calculations or manipulate data. | Expressions or variables to evaluate | +| **Wait for Input** | Pause workflow until input is received. | Any required user or system input | +| **Log** | Log a specified value or message. | Message or value to log | +| **Embed** | Embed text into a specific format or system. | Text or content to embed | +| **Search** | Perform a document search based on a query. | Search query | +| **Get** | Retrieve a value from a key-value store. | Key identifier | +| **Set** | Assign a value to a key in a key-value store. | Key and value to assign | +| **Parallel** | Run multiple steps in parallel. | List of steps to execute simultaneously | +| **Foreach** | Iterate over a collection and perform steps for each item. | Collection or list to iterate over | +| **MapReduce** | Map over a collection and reduce the results based on an expression. | Collection to map and reduce expressions | +| **If Else** | Conditional execution of steps based on a condition. | Condition to evaluate | +| **Switch** | Execute steps based on multiple conditions, similar to a switch-case statement. | Multiple conditions and corresponding steps | +| **Yield** | Run a subworkflow and await its completion. | Subworkflow identifier and input data | +| **Error** | Handle errors by specifying an error message. | Error message or handling instructions | +| **Sleep** | Pause the workflow for a specified duration. | Duration (seconds, minutes, etc.) | +| **Return** | Return a value from the workflow. | Value to return | -```bash -pip install julep +For detailed information on each step type and advanced usage, please refer to our [Task Documentation](https://docs.julep.ai/tasks). + +## Advanced Features + +Julep offers a range of advanced features to enhance your AI workflows: + +### Adding Tools to Agents + +Extend your agent's capabilities by integrating external tools and APIs: + +```python +client.agents.tools.create( + agent_id=agent.id, + name="web_search", + description="Search the web for information.", + integration={ + "provider": "google", + "method": "search", + "setup": {"api_key": "your_google_api_key"}, + }, +) ``` -For more information on using the Python SDK, please refer to the [Python SDK documentation](https://docs.julep.ai/api-reference/python-sdk-docs). +### Managing Sessions and Users -### TypeScript SDK -To install the TypeScript SDK using `npm`, run: +Julep provides robust session management for persistent interactions: -```bash -npm install @julep/sdk +```python +session = client.sessions.create( + agent_id=agent.id, + user_id="user123", + context_overflow="adaptive" +) + +# Continue conversation in the same session +response = client.sessions.chat( + session_id=session.id, + message="Follow up on our previous conversation." +) +``` + +### Document Integration and Search + +Easily manage and search through documents for your agents: + +```python +# Upload a document +document = client.documents.create( + file="path/to/document.pdf", + metadata={"category": "research_paper"} +) + +# Search documents +results = client.documents.search( + query="AI advancements", + filter={"category": "research_paper"} +) ``` -For more information on using the TypeScript SDK, please refer to the [TypeScript SDK documentation](https://docs.julep.ai/api-reference/js-sdk-docs). +For more advanced features and detailed usage, please refer to our [Advanced Features Documentation](https://docs.julep.ai/advanced-features). + +## API Reference + +Explore our comprehensive API documentation to learn more about agents, tasks, and executions: ---- +- [Agents API](https://docs.julep.ai/api/agents) +- [Tasks API](https://docs.julep.ai/api/tasks) +- [Executions API](https://docs.julep.ai/api/executions) -## Deployment -Check out the [self-hosting guide](https://docs.julep.ai/agents/self-hosting) to host the platform yourself. +## Examples and Tutorials -If you want to deploy Julep to production, [let's hop on a call](https://cal.com/ishitaj/15min)! +Discover example projects and tutorials to help you get started and build upon provided examples: -We'll help you customise the platform and help you get set up with: -- Multi-tenancy -- Reverse proxy along with authentication and authorisation -- Self-hosted LLMs -- & more +- [Example Projects](https://github.com/julep-ai/julep/tree/main/examples) +- [Tutorials](https://docs.julep.ai/tutorials) ---- ## Contributing -We welcome contributions from the community to help improve and expand the Julep AI platform. Please see our [Contributing Guidelines](CONTRIBUTING.md) for more information on how to get started. ---- +We welcome contributions to the project! Learn how to contribute and our code of conduct: + +- [Contributing Guidelines](https://github.com/julep-ai/julep/blob/main/CONTRIBUTING.md) +- [Code of Conduct](https://github.com/julep-ai/julep/blob/main/CODE_OF_CONDUCT.md) + +## Support and Community + +Join our community to get help, ask questions, and share your ideas: + +- [Discord](https://discord.com/invite/JTSBGRZrzj) +- [GitHub Discussions](https://github.com/julep-ai/julep/discussions) +- [Twitter](https://twitter.com/julep_ai) + ## License -Julep AI is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for more details. ---- -## Contact and Support -If you have any questions, need assistance, or want to get in touch with the Julep AI team, please use the following channels: +This project is licensed under the [Apache License 2.0](https://github.com/julep-ai/julep/blob/main/LICENSE). + +## Acknowledgements -- [Discord](https://discord.com/invite/JTSBGRZrzj): Join our community forum to discuss ideas, ask questions, and get help from other Julep AI users and the development team. -- GitHub Issues: For technical issues, bug reports, and feature requests, please open an issue on the Julep AI GitHub repository. -- Email Support: If you need direct assistance from our support team, send an email to hey@julep.ai, and we'll get back to you as soon as possible. -- Follow for updates on [X](https://twitter.com/julep_ai) & [LinkedIn](https://www.linkedin.com/company/julep-ai/) -- [Hop on a call](https://cal.com/ishitaj/15min): We wanna know what you're building and how we can tweak and tune Julep to help you build your next AI app. +We would like to express our gratitude to all contributors and the open-source community for their valuable resources and contributions. \ No newline at end of file diff --git a/example.py b/example.py new file mode 100644 index 000000000..ef6d6f427 --- /dev/null +++ b/example.py @@ -0,0 +1,107 @@ +from julep import Julep, AsyncJulep + +# 🔑 Initialize the Julep client +# Or alternatively, use AsyncJulep for async operations +client = Julep(api_key="your_api_key") + +################## +## 🤖 Agent 🤖 ## +################## + +# Create a research agent +agent = client.agents.create( + name="Research Agent", + about="You are a research agent designed to handle research inquiries.", + model="claude-3.5-sonnet", +) + +# 🔍 Add a web search tool to the agent +client.agents.tools.create( + agent_id=agent.id, + name="web_search", # Should be python valid variable name + description="Use this tool to research inquiries.", + integration={ + "provider": "brave", + "method": "search", + "setup": { + "api_key": "your_brave_api_key", + }, + }, +) + +################# +## 💬 Chat 💬 ## +################# + +# Start an interactive chat session with the agent +session = client.sessions.create( + agent_id=agent.id, + context_overflow="adaptive", # 🧠 Julep will dynamically compute the context window if needed +) + +# 🔄 Chat loop +while (user_input := input("You: ")) != "exit": + response = client.sessions.chat( + session_id=session.id, + message=user_input, + ) + + print("Agent: ", response.choices[0].message.content) + + +################# +## 📋 Task 📋 ## +################# + +# Create a recurring research task for the agent +task = client.tasks.create( + agent_id=agent.id, + name="Research Task", + description="Research the given topic every 24 hours.", + # + # 🛠️ Task specific tools + tools=[ + { + "name": "send_email", + "description": "Send an email to the user with the results.", + "api_call": { + "method": "post", + "url": "https://api.sendgrid.com/v3/mail/send", + "headers": {"Authorization": "Bearer YOUR_SENDGRID_API_KEY"}, + }, + } + ], + # + # 🔢 Task main steps + main=[ + # + # Step 1: Research the topic + { + # `_` (underscore) variable refers to the previous step's output + # Here, it points to the topic input from the user + "prompt": "Look up topic '{{_.topic}}' and summarize the results.", + "tools": [{"ref": {"name": "web_search"}}], # 🔍 Use the web search tool from the agent + "unwrap": True, + }, + # + # Step 2: Send email with research results + { + "tool": "send_email", + "arguments": { + "subject": "Research Results", + "body": "'Here are the research results for today: ' + _.content", + "to": "inputs[0].email", # Reference the email from the user's input + }, + }, + # + # Step 3: Wait for 24 hours before repeating + {"sleep": "24 * 60 * 60"}, + ], +) + +# 🚀 Start the recurring task +client.executions.create(task_id=task.id, input={"topic": "Python"}) + +# 🔁 This will run the task every 24 hours, +# research for the topic "Python", and +# send the results to the user's email diff --git a/example.ts b/example.ts new file mode 100644 index 000000000..3ef4e1a91 --- /dev/null +++ b/example.ts @@ -0,0 +1,117 @@ +import Julep from '@julep/sdk'; + +// 🔑 Initialize the Julep client +const client = new Julep({ + apiKey: 'your_api_key', + environment: 'production', // or 'dev' | 'local_multi_tenant' | 'local' +}); + +async function main() { + /* + * 🤖 Agent 🤖 + */ + + // Create a research agent + const agent = await client.agents.createOrUpdate('dad00000-0000-4000-a000-000000000000', { + name: 'Research Agent', + about: 'You are a research agent designed to handle research inquiries.', + model: 'claude-3.5-sonnet', + }); + + // 🔍 Add a web search tool to the agent + await client.agents.tools.create(agent.id, { + name: 'web_search', + description: 'Use this tool to research inquiries.', + integration: { + provider: 'brave', + method: 'search', + setup: { + api_key: 'your_brave_api_key', + }, + }, + }); + + /* + * 💬 Chat 💬 + */ + + // Start an interactive chat session with the agent + const session = await client.sessions.create({ + agentId: agent.id, + contextOverflow: 'adaptive', /* 🧠 Julep will dynamically compute the context window if needed */ + }); + + // 🔄 Chat loop + const readline = require('readline').createInterface({ + input: process.stdin, + output: process.stdout, + }); + + const askQuestion = (query: string) => new Promise((resolve) => readline.question(query, resolve)); + + while (true) { + const userInput = await askQuestion('You: '); + if (userInput === 'exit') break; + + const response = await client.sessions.chat(session.id, { + message: userInput, + }); + + console.log('Agent: ', response.choices[0].message.content); + } + + readline.close(); + + /* + * 📋 Task 📋 + */ + + // Create a recurring research task for the agent + const task = await client.tasks.create(agent.id, { + name: 'Research Task', + description: 'Research the given topic every 24 hours.', + /* 🛠️ Task specific tools */ + tools: [ + { + name: 'send_email', + description: 'Send an email to the user with the results.', + apiCall: { + method: 'post', + url: 'https://api.sendgrid.com/v3/mail/send', + headers: { Authorization: 'Bearer YOUR_SENDGRID_API_KEY' }, + }, + }, + ], + /* 🔢 Task main steps */ + main: [ + // Step 1: Research the topic + { + prompt: "Look up topic '{{_.topic}}' and summarize the results.", + tools: [{ ref: { name: 'web_search' } }], /* 🔍 Use the web search tool from the agent */ + unwrap: true, + }, + // Step 2: Send email with research results + { + tool: 'send_email', + arguments: { + subject: 'Research Results', + body: "'Here are the research results for today: ' + _.content", + to: 'inputs[0].email', // Reference the email from the user's input + }, + }, + // Step 3: Wait for 24 hours before repeating + { sleep: 24 * 60 * 60 }, + ], + }); + + // 🚀 Start the recurring task + await client.executions.create(task.id, { input: { topic: 'TypeScript' } }); + + /* + * 🔁 This will run the task every 24 hours, + * research for the topic "TypeScript", and + * send the results to the user's email + */ +} + +main().catch(console.error); \ No newline at end of file diff --git a/image.png b/image.png deleted file mode 100644 index ccb8756cb1901d931b9fd98af0af4419b2b0dfc7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 426141 zcmaHT2RxR2`+kX%mPAry6v-@#O0rTJWfK_Irj)&kY$0Ti z|8dtd-2MLVg$T9#aE@&&=0Iy#k5N5q?oeZ>f!eFn$@#QGe52bJM{4^cZNHRUUB9x z&Mls2rPd4ci%Zv~^B;b^aX-tgGSx%ur!smEO=)F#ond9pi1m}!pj6+Wkj)}9w=_Dr zXg!kK_c>j)&q{l=^mx_4#3I*d?SzAL&AemrX*McyCf8LY|NEaE4_vGK1S+++imqBs zO8Gzkp-k}mvG;%e_17N`aINOhy=7h0Y_gw3GVaAkk3SwO8b?0n`&s&ufx=(UIUtG) z*4ouI^Zm#8m@a-GQ7+p!H<@|kKZYmzY8BZtKe{hc29^JNs!Xn~VjJX{`TIZB_WtK| z*HA}_>$~m?=ic*QOShI=)Uzs5P9*H~zbh9>?G~-ra#j4_-~9S6l4HF*bCR?{K;eIH zMuEC3MRS->zuO;6NBpCF5a}~NmAK4u?f<=BgdOSN67?LeJ~g>w+ae1{XWpfbKhV?u 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