Building AI Workflows with Persistent Memory

AI agents are great at individual tasks, but complex projects require multi-step workflows that span multiple sessions. Without persistent memory, each step starts from scratch.

ContextFS provides workflow and task primitives that enable sophisticated AI orchestration. Let's build a real-world example.

The Problem

Imagine you're using AI to help with a code migration project:

Day 1: Analyze the codebase, identify patterns
Day 2: Plan the migration strategy
Day 3: Execute migrations, track progress
Day 4: Review and fix issues

Without persistent memory, you'd need to re-explain everything each day. With ContextFS, the AI remembers the entire journey.

Workflow Primitives

ContextFS provides three key primitives:

1. Workflows

A workflow is a named container for related tasks:

# Create a workflow
contextfs workflow create "react-migration" \
  --description "Migrate from class components to hooks"

2. Tasks

Tasks are individual steps within a workflow:

# Add tasks to the workflow
contextfs task add "react-migration" "Analyze existing components" --status pending
contextfs task add "react-migration" "Identify migration patterns" --status pending
contextfs task add "react-migration" "Migrate auth components" --status pending
contextfs task add "react-migration" "Migrate dashboard components" --status pending
contextfs task add "react-migration" "Update tests" --status pending

3. Steps

Steps are granular actions within a task:

# Add steps to a task
contextfs step add "task-123" "Review UserAuth.jsx"
contextfs step add "task-123" "Convert to functional component"
contextfs step add "task-123" "Add useState for local state"
contextfs step add "task-123" "Add useEffect for lifecycle"

Real-World Example

Let's walk through a complete workflow:

Day 1: Analysis

# Start the workflow
contextfs workflow create "api-refactor" \
  --description "Refactor REST API to use repository pattern"

# Save analysis findings as memories
contextfs save "API has 12 controllers with direct database calls" \
  -t fact --tags api-refactor,analysis

contextfs save "Controllers average 300 lines, high complexity" \
  -t fact --tags api-refactor,analysis

contextfs save "No existing abstraction layer between controllers and DB" \
  -t fact --tags api-refactor,analysis

# Create the migration plan
contextfs task add "api-refactor" "Create base repository interface" --status pending
contextfs task add "api-refactor" "Implement UserRepository" --status pending
contextfs task add "api-refactor" "Implement ProductRepository" --status pending
contextfs task add "api-refactor" "Refactor UserController" --status pending
contextfs task add "api-refactor" "Refactor ProductController" --status pending
contextfs task add "api-refactor" "Add integration tests" --status pending

Day 2: Decisions

# Record architectural decisions
contextfs save "Use abstract base class for repositories to share common CRUD operations" \
  -t decision --tags api-refactor

contextfs save "Inject repositories via constructor for testability" \
  -t decision --tags api-refactor

contextfs save "Keep existing API contracts, only change internal implementation" \
  -t decision --tags api-refactor

# Start first task
contextfs task update "task-001" --status in_progress

Day 3: Implementation

# Mark task complete
contextfs task update "task-001" --status completed

# Record what was done
contextfs save "Created IRepository<T> interface with CRUD methods" \
  -t code --tags api-refactor

# Move to next task
contextfs task update "task-002" --status in_progress

# Track progress
contextfs workflow status "api-refactor"
# Output:
# api-refactor: Refactor REST API to use repository pattern
# Progress: 1/6 tasks completed (16%)
# Current: Implement UserRepository (in_progress)

Day 4: Continue

When you start a new session, the AI can recall all context:

# Search for project context
contextfs search "api-refactor"

# Get workflow status
contextfs workflow status "api-refactor"

# The AI now knows:
# - What you're building
# - What decisions were made
# - Current progress
# - What's next

MCP Integration

With MCP (Model Context Protocol), AI assistants can interact with workflows directly:

User: "Continue working on the API refactor"

AI: Let me check the current status...
    [Calls contextfs_search for "api-refactor"]
    [Calls contextfs_workflow_status]

    I see we're working on the API refactor project. Here's the current state:
    - Completed: Base repository interface
    - In Progress: UserRepository implementation
    - Remaining: 4 tasks

    Based on your decision to use constructor injection, here's how
    I'd implement the UserRepository...

Best Practices

1. Name workflows descriptively

Bad: project-1 Good: react-18-migration-dashboard

2. Save decisions as you make them

Don't wait—record decisions immediately so they're available in future sessions.

3. Use tags consistently

Create a tagging convention and stick to it:

project-name for project association
analysis, decision, implementation for phases
blocked, needs-review for status

4. Review workflow status regularly

Start each session by checking workflow status to orient the AI.

What's Next

We're building more workflow features:

Workflow templates for common patterns
Automatic progress tracking from code changes
Team workflows for collaborative projects
Workflow visualization in the dashboard

Ready to build sophisticated AI workflows? Install ContextFS and start orchestrating.