Multi-Agent Knowledge Management

A single AI agent doing knowledge management runs into walls fast. It can’t ingest content while simultaneously synthesizing insights. Its context fills up processing one day’s notes. It lacks the specialized skills needed for different knowledge tasks.

Multi-agent systems solve this by dividing work across specialized agents that collaborate through defined patterns.

Why Multiple Agents

Single-agent PKM forces tradeoffs. Your agent is either good at quick capture or deep analysis, not both. It either stays focused on your current task or handles background processing.

Jinyoung Kim’s AI4PKM demonstrates this in practice. His system runs hourly maintenance agents, daily ingestion agents, and weekly synthesis agents, each optimized for its specific job.

Architecture Patterns

Three patterns dominate multi-agent PKM systems. Pick based on your workflow needs.

Orchestrator Pattern

One agent coordinates all others. It receives requests, routes them to specialists, and merges results.

                 ┌─────────────┐
                 │ Orchestrator│
                 └──────┬──────┘
           ┌───────────┼───────────┐
           ▼           ▼           ▼
      ┌────────┐  ┌────────┐  ┌────────┐
      │ Ingest │  │ Process│  │Synthesize│
      └────────┘  └────────┘  └────────┘

The orchestrator handles task decomposition, agent selection, and result aggregation. CrewAI’s hierarchical process implements this with a manager agent that delegates to crew members.

Use when:

• Tasks vary in complexity
• Different requests need different agent combinations
• You want unified entry point

Pipeline Pattern

Agents pass output sequentially. Each stage transforms content for the next.

┌────────┐    ┌────────┐    ┌────────┐    ┌────────┐
│ Capture│ ─► │ Enrich │ ─► │ Connect│ ─► │ Store  │
└────────┘    └────────┘    └────────┘    └────────┘

AI4PKM’s Daily Ingestion workflow uses this pattern: extract metadata, convert formats, add summaries and tags, link to existing notes, update indexes.

Use when:

• Processing follows predictable stages
• Each stage’s output feeds the next
• Order matters

Swarm Pattern

Agents work independently and merge results. No central coordinator.

┌────────┐  ┌────────┐  ┌────────┐
│ Agent A│  │ Agent B│  │ Agent C│
└────┬───┘  └────┬───┘  └────┬───┘
     └───────────┼───────────┘
                 ▼
          ┌───────────┐
          │  Combine  │
          └───────────┘

Use when:

• Tasks are independent
• Speed matters more than coordination
• Different perspectives add value

See Subagent Patterns for detailed dispatch strategies.

The Orchestrator’s Job

In hierarchical systems, the orchestrator does real work.

The orchestrator itself shouldn’t do knowledge work. It coordinates. Think manager, not worker.

PKM Pipeline Example

A practical daily knowledge pipeline:

┌──────────────────────────────────────────────────────────────┐
│                     DAILY PIPELINE                            │
├──────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐      │
│  │ Ingest  │ → │ Process │ → │ Connect │ → │Synthesize│     │
│  └─────────┘   └─────────┘   └─────────┘   └─────────┘      │
│       │             │             │             │            │
│       ▼             ▼             ▼             ▼            │
│  Voice notes   Add metadata   Link to       Generate        │
│  Web clips     Summarize      existing      daily           │
│  Photos        Tag content    notes         roundup         │
│                                                              │
└──────────────────────────────────────────────────────────────┘

Each agent runs independently:

• Ingest Agent: Watches input folders, converts formats, normalizes structure
• Process Agent: Adds summaries, extracts entities, generates tags
• Connect Agent: Finds related notes, updates backlinks, maintains indexes
• Synthesis Agent: Creates daily summaries, spots patterns, suggests connections

AI4PKM schedules these through cron jobs. The agents don’t need human triggers after initial setup.

When Single vs Multi

Not every PKM task needs multiple agents. Single agents handle plenty.

Start with single-agent workflows. Add agents when you hit specific limits:

• Context window fills before task completes
• Processing time blocks other work
• Task requires incompatible specializations

Implementation Paths

Framework-Based

Use existing multi-agent frameworks:

• CrewAI for role-based agent teams
• LangGraph for visual workflow design
• AutoGen for conversational agent teams

Custom Pipelines

Build simpler pipelines with scheduled scripts:

# Daily PKM pipeline
0 6 * * * claude --task "ingest" vault/inbox/
0 7 * * * claude --task "process" vault/notes/
0 8 * * * claude --task "connect" vault/
0 9 * * * claude --task "synthesize" vault/ --output daily.md

Hybrid Approach

Combine manual orchestration with automated specialists. You decide what runs; agents do the work.

This matches the delegation principle: keep judgment human, automate execution.

Key Tradeoffs

Multi-agent systems require upfront investment. They pay off when your knowledge base grows large enough that single-agent processing becomes the bottleneck.

Related Concepts

• Context Management - why single agents struggle with large tasks
• AI Second Brain - what multi-agent systems can build toward
• Subagent Patterns - dispatch patterns for coordinating agents

Next: Automated PKM Workflows