Creating workflows
DigitalFate enables you to move beyond isolated LLM calls and build structured, reusable, and intelligent workflows using tasks, agents, and tools. Workflows in DigitalFate mirror real-world processes whether you're building a research assistant, automating documentation, or orchestrating multi-agent collaboration.
🔄 What Is a Workflow?
A workflow in DigitalFate is a sequence or system of tasks, often executed by one or more agents with optional tools, memory, and logic. These workflows can be:
Linear (task A → task B → task C)
Parallel (task A & task B simultaneously)
Conditional (task A → if result: task B else: task C)
Recursive (agents spawn subtasks dynamically)
🧱 Building Blocks of a Workflow
Task A structured objective.
pythonCopyEdittask = Task(description="Summarize a recent AI article")Tools External integrations to gather or transform data.
pythonCopyEdittask.tools = [Search, PDFReader]Agent A configuration that determines how a task is approached.
pythonCopyEditagent = AgentConfiguration(job_title="Researcher", memory=True)Knowledge Base (optional) Provides additional private or public context.
pythonCopyEditkb = KnowledgeBase(urls=["https://openai.com/research"]) agent.knowledge_base = kbExecution Run the task with your agent(s).
pythonCopyEditclient.agent(agent, task)
🔁 Workflow Example: Research Pipeline
pythonCopyEditfrom digitalfate import Task, AgentConfiguration, digitalfateClient
from digitalfate.client.tools import Search, PDFReader
client = digitalfateClient("localserver")
client.set_config("OPENAI_API_KEY", "your_key")
# Task 1: Research
task1 = Task(description="Find top 3 articles on AI regulation", tools=[Search])
# Task 2: Extract key points
task2 = Task(description="Extract key insights from the AI articles", tools=[PDFReader])
# Agent
research_agent = AgentConfiguration(
job_title="AI Policy Analyst",
memory=True,
)
# Run tasks in sequence
client.agent(research_agent, task1)
client.agent(research_agent, task2)
# Print results
print(task2.response)This can be extended into branching workflows or scheduled task loops.
🤖 Multi-Agent Workflows
Assign different agents to different steps for modular specialization.
pythonCopyEditclient.parallel_agents(
[marketing_agent, technical_writer],
[task_market_research, task_tech_docs]
)Example:
Agent A researches.
Agent B drafts content.
Agent C reviews or summarizes.
Each agent maintains their own memory, tools, and goals like a functioning AI team.
🔁 Recursive Workflows (Self-Decomposing Tasks)
DigitalFate agents (v2) can break down a complex task into subtasks recursively.
pythonCopyEdittask = Task(description="Build a competitive analysis of Anthropic, OpenAI, and DeepMind")
client.agent(autonomous_strategy_agent, task)Subtasks generated dynamically:
“Find revenue data for each company”
“Analyze AI model strengths and weaknesses”
“Compare hiring trends”
This enables hands-free research, ideation, and analysis.
🧠 Memory-Based Workflows
Memory-enabled workflows allow your agent to remember key decisions, preferences, and facts across sessions or task chains.
pythonCopyEditagent = AgentConfiguration(agent_id="workflow_agent", memory=True)Memory is automatically retrieved and updated per interaction ideal for long-term projects, planning assistants, or LLM-based advisors.
🧰 Tool-Driven Workflow Customization
DigitalFate tools can be chained or scoped by workflow stage:
pythonCopyEdittask_research.tools = [Search]
task_analysis.tools = [PDFReader, CodeInterpreter]Custom tools can be added in a single line of code, enabling domain-specific automations like:
Database queries
Internal API lookups
Enterprise knowledge access
🖥️ Human-Like Interaction Workflow
Use enable_computer_use = True in tasks to simulate a human navigating web interfaces, filling forms, or clicking buttons.
Example:
Task: “Apply to three AI job postings”
Tools: Browser + Computer Use
Output: Application logs with screenshots and status
⚙️ Workflow Chaining with MCP
With MCP (Multi-Client Processing), you can:
Chain multiple workflows as microservices
Trigger workflows across distributed agents
Scale task execution across endpoints and teams
Use this for SaaS-style agents or high-throughput enterprise use cases.
🌐 SaaS Workflow Example
Workflow:
User uploads a PDF.
Agent extracts insights.
Agent emails a summary report.
All of this can be automated and served via a REST API or UI connected to DigitalFate’s back end.
📊 Use Cases
Competitive research
Recursive
Analyst
Email drafting
Linear
Assistant
Report generation
Parallel
Editor + Reviewer
Website testing
Human-simulated
QA Agent
SaaS automation
API-triggered
Workflow Bot
Workflows are the heartbeat of DigitalFate. They allow you to transform single-task agents into fully orchestrated AI systems that scale, think, and perform like intelligent teams.
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