qredence/agentic-fleet/context

AgenticFleet Memory System

A two-tier memory architecture enabling agents to learn, remember, and improve over time.

Quick Start

1. Initialize (first time only):

``bash uv run python .fleet/context/scripts/memory_manager.py init ``

1. Setup Chroma Cloud (after editing config with your API key):

``bash uv run python .fleet/context/scripts/memory_manager.py setup-chroma ``

1. Verify Status:

``bash uv run python .fleet/context/scripts/memory_manager.py status ``

1. Read Core Context (always do this first):

• .fleet/context/core/project.md - Project architecture
• .fleet/context/core/human.md - User preferences
• .fleet/context/core/persona.md - Agent guidelines

1. Search Memory when you need information:

``bash uv run python .fleet/context/scripts/memory_manager.py recall "your query" ``

1. Create Skills after solving problems:

``bash uv run python .fleet/context/scripts/memory_manager.py learn --file .fleet/context/skills/new-skill.md ``

Memory Hierarchy

Core Memory (Always In-Context)

Location: .fleet/context/core/

Topic Blocks (Reference On-Demand)

Location: .fleet/context/blocks/

Skills (Procedural Memory)

Location: .fleet/context/skills/

Learned patterns and solutions. Indexed to Chroma for semantic search.

Chroma Cloud (Semantic Search)

Collections: semantic, procedural, episodic

Enables fuzzy search across all indexed content.

Commands

Claude Code Commands

/init              # Initialize memory system
/learn             # Learn a new skill
/recall            # Search memory semantically
/reflect           # Reflect on session, consolidate learnings

CLI Commands

# Initialize system (creates local files)
uv run python .fleet/context/scripts/memory_manager.py init
 
# Setup Chroma Cloud collections
uv run python .fleet/context/scripts/memory_manager.py setup-chroma
 
# Check connection and collection status
uv run python .fleet/context/scripts/memory_manager.py status
 
# Semantic search across all collections
uv run python .fleet/context/scripts/memory_manager.py recall "query"
 
# Index skill to Chroma procedural collection
uv run python .fleet/context/scripts/memory_manager.py learn --file <path>
 
# Archive session to episodic collection
uv run python .fleet/context/scripts/memory_manager.py reflect

Block Format

All memory blocks use Letta-style frontmatter:

---
label: block-name
description: What this block contains and when to use it.
limit: 5000 # Character limit
scope: core|project|workflows|decisions
updated: 2024-12-29
---
# Content here...

Workflow

Starting a Session

1. Read core blocks (project, human, persona)
2. Check relevant topic blocks if needed
3. Use /recall to search for relevant skills

During Work

1. Reference blocks as needed
2. Update human.md if you learn user preferences
3. Note patterns worth remembering

Ending a Session

1. Use /reflect to consolidate learnings
2. Create skills for reusable solutions
3. Index new skills with /learn

File Structure

.fleet/context/
├── SKILL.md                    # This file (entry point)
├── MEMORY.md                   # Detailed documentation
├── core/                       # Core memory blocks
├── blocks/                     # Topic-scoped blocks
│   ├── project/
│   ├── workflows/
│   └── decisions/
├── skills/                     # Learned skills
├── system/                     # Agent skill definitions
├── scripts/                    # Python memory engine
└── .chroma/                    # Chroma Cloud config

Related Documentation

• MEMORY.md - Detailed setup and architecture
• skills/README.md - How to create skills
• skills/SKILL_TEMPLATE.md - Skill template
• blocks/decisions/001-memory-system.md - Architecture decision record