Collaborating

DigitalFate isn’t just a solo agent framework it’s built for collaborative, multi-agent systems that mimic real-world teamwork. Agents can specialize, share context, divide workloads, and iterate together on complex objectives. This makes it ideal for building vertical AI teams, enterprise task networks, or SaaS-based digital assistants.

---

🤝 Agent Collaboration Overview

Agents in DigitalFate can:

• Operate on the same task with different responsibilities

• Chain tasks between roles (e.g., researcher → writer → reviewer)

• Communicate via task results and memory

• Share access to knowledge bases and tools

• Work in parallel or sequence using MCP (Multi-Client Processing)

This architecture models real collaborative environments.

---

🧠 Defining Multiple Agents

pythonCopyEditfrom digitalfate import AgentConfiguration

researcher = AgentConfiguration(
    agent_id="researcher_01",
    job_title="AI Researcher",
    memory=True
)

writer = AgentConfiguration(
    agent_id="writer_01",
    job_title="Technical Writer",
    memory=True
)

reviewer = AgentConfiguration(
    agent_id="reviewer_01",
    job_title="Content Reviewer",
    memory=True
)

Each agent has their own memory, persona, and logic. You can pass outputs between them via task results.

---

🔁 Sequential Agent Collaboration

Create multi-stage workflows by passing task outputs between agents.

pythonCopyEdittask1 = Task(description="Research AI startup funding trends")
task2 = Task(description="Write a summary based on research")
task3 = Task(description="Review and improve the summary")

client.agent(researcher, task1)
task2.input_context = task1.response

client.agent(writer, task2)
task3.input_context = task2.response

client.agent(reviewer, task3)

print(task3.response)

This enables collaborative documents, decision flows, or advisory chains.

---

⚡ Parallel Agent Collaboration with MCP

Use DigitalFate’s MCP (Multi-Client Processing) system to run agents in parallel.

pythonCopyEditclient.parallel_agents(
    [researcher, writer, reviewer],
    [task1, task2, task3]
)

Perfect for:

• Parallel market analysis

• Cross-functional AI teams

• Fast scaling in SaaS environments

Agents can work on different pieces of a broader task simultaneously.

---

📡 Shared Knowledge Base

Multiple agents can be given access to the same knowledge base:

pythonCopyEditkb = KnowledgeBase(urls=["https://openai.com/research", "https://anthropic.com/updates"])
researcher.knowledge_base = kb
writer.knowledge_base = kb

This improves consistency and shared understanding across all collaborators.

---

🗂️ Use Case: AI Content Production Team

1. Research Agent gathers data with Search + Browser

2. Writer Agent uses Memory + PDFReader to generate technical summaries

3. Editor Agent evaluates tone, clarity, and relevance

This modular approach builds scalable AI content pipelines for blogs, product descriptions, reports, etc.

---

🧰 Role-Specific Tool Assignment

Different agents can use different tools based on role:

pythonCopyEditresearcher.tools = [Search, Browser]
writer.tools = [PDFReader, CSVTool]
editor.tools = [CodeInterpreter, ReviewTool]

This reinforces specialization and mirrors human workflows.

---

🧠 Memory Sharing (Future Feature)

Planned capability: allow agents to reference each other’s memories, enabling deeper collaboration, decision-sharing, and task chaining.

Example:

• A planner agent delegates to execution agents

• The execution agents report progress and updates back to the planner’s memory

• The planner re-strategizes or pivots in real time

---

🧑‍💼 LLM-as-a-Team

DigitalFate enables "LLM collectives" multi-agent compositions that function as a single intelligent unit across:

• Strategic planning

• Automated operations

• Data analysis

• Customer support

• Technical audits

These can be run locally, in Dockerized production environments, or scaled across cloud providers using serverless APIs.

---

💼 Team Deployment via API

Collaborative agent networks can be embedded into SaaS apps using REST APIs.

Example:

• Endpoint triggers team execution

• Each agent does its part (research, draft, revise)

• Final output returned to user or stored

---

🤖 Digital Departments: Simulating Enterprise Roles

DigitalFate agents can be assigned roles across departments like:

• Legal

• Marketing

• Product

• Engineering

• Support

Each one handles domain-specific workflows with autonomy and context, enabling organizations to simulate full business operations with AI.

---

🔒 Collaboration Security

When collaborating across agents:

• Tool access is isolated per agent

• API keys are securely scoped

• Output routing is controlled by your task logic

• Agents cannot overwrite each other’s memory unless explicitly shared

This ensures safety, transparency, and reproducibility in complex multi-agent setups.