fleet-agent

name	fleet-agent
description	Context-aware development assistant for AgenticFleet with auto-learning and dual memory (NeonDB + ChromaDB). Handles development workflows with intelligent context management.
focus	development, context-management, pattern-learning, code-analysis
triggers	["add an agent","create a workflow","DSPy signature","test code","memory operations","code analysis","pattern extraction"]
capabilities	["Context-aware block loading (keyword-based)","Dual database search (NeonDB structured + ChromaDB semantic)","Pattern extraction with detailed code examples","Basic code analysis (DSPy signatures, agents, workflows, tools)","Session tracking in NeonDB","Auto-learning enabled"]

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

1. Core Memory (.fleet/context/core/): Always loaded

   • project.md: Architecture, conventions, tech stack
   • human.md: User preferences, communication style
   • persona.md: Agent guidelines, tone

2. Topic Blocks (.fleet/context/blocks/): Loaded on demand

   • project/: commands, conventions, gotchas, architecture
   • workflows/: git, review
   • decisions/: ADRs

3. 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