DigitalFate
DigitalFate provides an advanced, enterprise-ready framework for orchestrating LLM calls, agents, and computer-based tasks, - Kalyankensin/DigitalFate
ToddDrew
README
What is DigitalFate?
DigitalFate provides an advanced, enterprise-ready framework for orchestrating LLM calls, agents, and computer-based tasks in a cost-efficient manner. It delivers reliable systems, scalability, and a task-oriented architecture to handle real-world applications effectively.
Key Features:
- Scalable for Production: Effortlessly deploy on AWS, GCP, or locally via Docker.
- Task-Focused Architecture: Execute tasks at varying levels of complexity:
- Simple tasks through LLM calls.
- Intermediate tasks using V1 agents.
- Advanced automation with V2 agents and MCP integration.
- MCP Server Compatibility: Leverage multi-client processing for high-performance operations.
- Secure Tool-Calling Server: Manage tools with robust API interactions.
- Computer Use Integration: Perform human-like tasks with Anthropic's ‘Computer Use’ capabilities.
- Easy Tool Integration: Add custom or MCP tools with a single line of code.
- Client-Server Architecture: A stateless, enterprise-ready system designed for production.
<br> <br>
🛠️ Getting Started
Prerequisites
- Python 3.10 or newer
- OpenAI or Anthropic API keys (supports Azure and Bedrock)
Installation
pip install digitalfate
Basic Example
from digitalfate import digitalfateClient, ObjectResponse, Task, AgentConfiguration
from digitalfate.client.tools import Search
# Initialize Client and Configure
client = digitalfateClient("localserver")
client.set_config("OPENAI_API_KEY", "YOUR_API_KEY")
# Define Task and Agent
task1 = Task(description="Research latest news in Anthropic and OpenAI", tools=[Search])
product_manager_agent = AgentConfiguration(
job_title="Product Manager",
company_url="https://digitalfate.ai",
company_objective="To build an AI agent framework that helps people accomplish tasks",
)
# Execute Task with Agent
client.agent(product_manager_agent, task1)
result = task1.response
print(result)
Advanced Example
from digitalfate import digitalfateClient, ObjectResponse, Task, AgentConfiguration
from digitalfate.client.tools import Search
# Create a DigitalFate client instance
client = digitalfateClient("localserver")
client.set_config("OPENAI_API_KEY", "YOUR_API_KEY")
client.default_llm_model = "openai/gpt-4o"
# DeepSeek Chat
client.set_config("DEEPSEEK_API_KEY", "YOUR_DEEPSEEK_API_KEY")
client.default_llm_model = "deepseek/deepseek-chat"
# Claude-3.5-Sonnet
client.set_config("ANTHROPIC_API_KEY", "YOUR_ANTHROPIC_API_KEY")
client.default_llm_model = "claude/claude-3-5-sonnet"
# GPT 4o on Azure
client.set_config("ANTHROPIC_API_KEY", "YOUR_ANTHROPIC_API_KEY")
client.default_llm_model = "claude/claude-3-5-sonnet"
# Claude 3.5 Sonnet on AWS
client.set_config("AWS_ACCESS_KEY_ID", "YOUR_AWS_ACCESS_KEY_ID")
client.set_config("AWS_SECRET_ACCESS_KEY", "YOUR_AWS_SECRET_ACCESS_KEY")
client.set_config("AWS_REGION", "YOUR_AWS_REGION")
client.default_llm_model = "bedrock/claude-3-5-sonnet"
Task Definition
Tasks are defined by their descriptions. High-level tasks are broken into manageable sub-tasks automatically. For example, the task "Research latest news in Anthropic and OpenAI" may result in subtasks like:
"Search for Anthropic and OpenAI news on Google." "Read relevant blogs." "Review official announcements."
Define Task Description
description = "Research latest news in Anthropic and OpenAI"
Task Execution
Combine agents and tasks, then run them using the DigitalFate server. This approach simplifies task execution in SaaS applications or vertical AI systems.
client.agent(product_manager_agent, task1)
result = task1.response
for item in result.news_list:
print("\nNews")
print("Title: ", item.title)
print("Body: ", item.body)
print("URL: ", item.url)
print("Tags: ", item.tags)
<br> <br>
Additional Features (Beta)
Single LLM Call
Optimize cost and latency by deciding when to directly call an LLM instead of deploying agents.
client.call(task1)
Memory System
Enable personalized, context-aware interactions by leveraging memory settings in AgentConfiguration.
product_manager_agent = AgentConfiguration(
agent_id="product_manager_agent",
memory=True,
)
Knowledge Base
Provide agents with context through private or public content, such as PDFs or URLs.
from digitalfate import KnowledgeBase
kb = KnowledgeBase(files=["sample.pdf", "https://digitalfate.ai"])
task1 = Task(context=[kb])
Task Chaining
Link tasks together by using the output of one as the input for another.
task2 = Task(context=[task1])
Multi-Agent Collaboration
Distribute tasks across multiple agents for collaborative problem-solving.
client.multi_agent([agent1, agent2], [task1, task2])
Human-Like Agents
Configure agents with names and contact information for tasks requiring personal interaction.
product_manager_agent = AgentConfiguration(
name="John Walk",
contact="john@digitalfate.ai",
)
Computer Use
Perform tasks that require human-like interactions, such as mouse movements and clicks.
from digitalfate.client.tools import ComputerUse
tools = [ComputerUse]
Reflection Mechanism
Ensure high-quality outputs by validating results and providing feedback for improvements.
product_manager_agent = AgentConfiguration(
reflection=True,
)
Context Compression
Handle context overflow scenarios by compressing system messages and inputs automatically.
product_manager_agent = AgentConfiguration(
compress_context=True,
)
Telemetry
Disable anonymous telemetry by setting an environment variable.
import os
os.environ["digitalfate_TELEMETRY"] = "False"
License
DigitalFate is licensed under the MIT License. See the full license text below:
MIT License
Copyright (c) 2025 DigitalFate
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Recommended Servers
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
AIO-MCP Server
🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from
Persistent Knowledge Graph
An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.
Hyperbrowser MCP Server
Welcome to Hyperbrowser, the Internet for AI. Hyperbrowser is the next-generation platform empowering AI agents and enabling effortless, scalable browser automation. Built specifically for AI developers, it eliminates the headaches of local infrastructure and performance bottlenecks, allowing you to

Any OpenAI Compatible API Integrations
Integrate Claude with Any OpenAI SDK Compatible Chat Completion API - OpenAI, Perplexity, Groq, xAI, PyroPrompts and more.
Exa MCP
A Model Context Protocol server that enables AI assistants like Claude to perform real-time web searches using the Exa AI Search API in a safe and controlled manner.
BigQuery
This is a server that lets your LLMs (like Claude) talk directly to your BigQuery data! Think of it as a friendly translator that sits between your AI assistant and your database, making sure they can chat securely and efficiently.
Perplexity Chat MCP Server
MCP Server for the Perplexity API.
Web Research Server
A Model Context Protocol server that enables Claude to perform web research by integrating Google search, extracting webpage content, and capturing screenshots.