agent-memory

Agent Memory Protocol

3-tier persistent memory system for cross-session knowledge accumulation.

Memory Tiers

Tier	Path	Scope	Written By	Read By
Shared	.scaffolding/agent-memory/shared/KNOWLEDGE.md	Whole project	Any agent	All agents
Agent	.scaffolding/agent-memory/agents/{agent-name}/MEMORY.md	Per agent	Owning agent	Own agent + architect
Conversation	.scaffolding/conversations/{conversation_id}/agent-memory/context.md	Per conversation	Any agent in conversation	Agents in same conversation

Automatic Injection

Memory is auto-injected into agent context via recall_for_agent() in the task execution pipeline. When a task starts, the system reads:

1. .scaffolding/agent-memory/shared/KNOWLEDGE.md (always)
2. .scaffolding/agent-memory/agents/{agent-name}/MEMORY.md (when agent_name is known)
3. .scaffolding/conversations/{id}/agent-memory/context.md (when conversation_id is provided)

This means agents receive memory context automatically. Manual reading on first turn is optional but recommended for verifying latest data.

On First Turn

Before starting work, optionally read available memory for latest content (skip if files don't exist):

1. Read .scaffolding/agent-memory/shared/KNOWLEDGE.md
2. Read .scaffolding/agent-memory/agents/{your-agent-name}/MEMORY.md
3. If conversation_id is provided in task context: Read .scaffolding/conversations/{conversation_id}/agent-memory/context.md

Before Completing

Write significant findings to the appropriate tier:

Shared Knowledge (KNOWLEDGE.md)

Save here:

• Project architecture facts confirmed across multiple tasks
• Deployment gotchas and infrastructure quirks
• Cross-cutting patterns (e.g. how Redis is used, how tasks flow)
• Known bugs or limitations that affect multiple agents

Do NOT save:

• Agent-specific patterns (use agent memory)
• Task-specific context (use conversation memory)
• Anything already in CLAUDE.md or docs/

Agent Memory (MEMORY.md)

Save here:

• Patterns specific to your agent's domain (e.g. developer saves coding patterns)
• Lessons learned from mistakes in your domain
• File locations you frequently need
• Recurring debugging insights

Do NOT save:

• Generic project facts (use shared knowledge)
• One-time task details
• Speculative or unverified conclusions

Conversation Memory (context.md)

Save here:

• Decisions made during this conversation
• Findings from investigation (debugger -> developer handoff)
• Context needed by downstream agents in the same conversation chain
• Original intent and requirements clarifications

Do NOT save:

• Permanent knowledge (use shared or agent memory)
• Raw data or large code snippets

Read-Only Agents

Agents with disallowedTools: Write, Edit (architect, reviewer) cannot write to .scaffolding/agent-memory/ directly. These agents should report findings in their output, and writable agents in the same conversation chain can persist them.

Format Guidelines

• Max 200 lines per file (auto-injected memory has 200-line limit)
• Use markdown headers to organize by topic
• Include dates [YYYY-MM-DD] for time-sensitive entries
• Remove outdated entries proactively
• Use concise bullet points, not prose

File Creation

If memory files don't exist, create them with the appropriate header:

# Shared Knowledge
<!-- Cross-agent project knowledge. Max 200 lines. -->

# {Agent Name} Memory
<!-- Agent-specific patterns and lessons. Max 200 lines. -->

# Conversation {conversation_id} Context
<!-- Decisions and findings for this conversation chain. -->

Conversation Recall

Conversation memory is always available via file-based recall. When a task runs with a conversation_id, the system reads context.md and injects it into the agent's context. No database setup is required for conversation-tier memory.