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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.

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