tomevault-io Fleet Agent
A context-aware development assistant for AgenticFleet that maintains persistent memory across sessions using a hybrid NeonDB + ChromaDB architecture.
Memory Architecture
Dual Storage
- ChromaDB (Semantic): Skills, patterns, code snippets with embedding-based search
- NeonDB (Structured): Sessions, users, analytics, skill metadata with SQL queries
Context Layers
Core Memory (
.fleet/context/core/): Always loadedproject.md: Architecture, conventions, tech stackhuman.md: User preferences, communication stylepersona.md: Agent guidelines, tone
Topic Blocks (
.fleet/context/blocks/): Loaded on demandproject/: commands, conventions, gotchas, architectureworkflows/: git, reviewdecisions/: ADRs
Skills (ChromaDB + NeonDB): Semantic + structured patterns
Usage Examples
Learn a Pattern
/fleet-agent learn --name "add_dspy_agent" --category "agent" --content "Create agent via AgentFactory with DSPyEnhancedAgent wrapper..."
Recall Information
/fleet-agent recall "DSPy typed signatures"
/fleet-agent context "add a new agent for web search"
Analyze Code
/fleet-agent analyze src/agents/coordinator.py
Session Management
/fleet-agent session start
/fleet-agent session status
/fleet-agent session summary "Completed agent creation workflow"
Commands
| Command | Description |
|---|---|
learn --name <name> --category <cat> --content <code> |
Save pattern to both databases |
recall <query> |
Search NeonDB + ChromaDB |
context <task> |
Load relevant context blocks |
analyze <file> |
Analyze code structure |
session start |
Start new session |
session status |
Show current session |
session summary <text> |
Save session summary |
stats |
Show development metrics |
Auto-Learning
Automatically extracts and saves patterns after successful task completion with detailed code examples:
name: pattern_add_dspy_signature
category: dspy
description: How to create a DSPy signature with TypedPredictor
implementation: |
class TaskAnalysisOutput(BaseModel):
complexity: Literal["low", "medium", "high"]
class TaskAnalysis(dspy.Signature):
task: str = dspy.InputField(desc="Task to analyze")
analysis: TaskAnalysisOutput = dspy.OutputField()
Implementation
Main script: .fleet/context/scripts/fleet_agent.py
Invocation: uv run python .fleet/context/scripts/fleet_agent.py <command>
Dependencies: neon_memory.py, chroma_driver.py, memory_loader.py
See Also
memory-system-guide.md: Complete memory system documentation.fleet/context/MEMORY.md: Memory hierarchy and commands
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