AI Agent Memory

AI agent memory refers to systems that allow agents to persist knowledge, context, and learnings across sessions. Unlike standard chatbots that forget everything when a conversation ends, agents with memory maintain continuity—remembering past interactions, decisions, and accumulated knowledge.

The core insight: mental notes don't survive session restarts. Files do.

Memory-enabled agents write important information to persistent storage (usually files), then read that storage at the start of new sessions. This creates the experience of an agent that actually remembers you and your work together.

Session 1: "Remember that the client prefers formal communication"
→ Agent writes to memory file

Session 2: (days later)
→ Agent reads memory file
→ "Based on what I know about this client's preferences,
   I'll keep the tone formal..."

Continuity in relationships

Without memory, every session starts from zero. You re-explain context, preferences, and history. With memory, the agent knows:

• Who you are and how you work
• What projects you're working on
• Decisions you've made and why
• Your preferences and patterns

Accumulated intelligence

Memory lets agents get smarter over time:

• Learn from mistakes and corrections
• Build domain knowledge
• Recognize patterns in your work
• Develop better intuitions

Operational efficiency

No more repeating yourself:

• Context carries forward
• Reference past work easily
• Build on previous sessions
• Maintain ongoing projects

True assistance

A good human assistant remembers. They know your calendar, preferences, ongoing projects, and relationships. Agent memory enables this level of assistance.

Working memory

Short-term context within a conversation:

• What's been discussed this session
• Current task state
• Pending decisions
• Immediate context

Usually handled by the conversation itself, but may need explicit management for long sessions.

Episodic memory

Records of specific events and interactions:

• What happened on specific dates
• Decisions made and their outcomes
• Conversations and their context
• Time-stamped logs of activity

Often stored as daily log files:

memory/2024-01-15.md
memory/2024-01-16.md
...

Semantic memory

Curated knowledge and facts:

• User preferences
• Project information
• Learned facts
• Accumulated wisdom

Often stored in dedicated files:

MEMORY.md      - Long-term curated knowledge
USER.md        - Information about the user
projects/      - Ongoing project context

Procedural memory

How to do things:

• Workflows and processes
• Tool usage patterns
• Best practices
• Operational procedures

Often embedded in configuration files like SOUL.md or skill definitions.

File-based memory

The simplest and most robust approach—use files:

# memory/2024-01-15.md

• Chose React over Vue for the new project (user prefers ecosystem)
• Set up CI/CD with GitHub Actions

• User prefers bullet points over paragraphs
• Morning is best for complex discussions

• Waiting on API documentation from vendor
• Need to schedule design review

Files are:
- Human-readable and editable
- Version-controllable
- Debuggable
- Persistent across any system changes

**Structured memory**

For specific data types, use structured formats:

```json
// memory/preferences.json
{
  "communication": {
    "style": "concise",
    "format": "bullet_points",
    "best_times": ["morning", "early_afternoon"]
  },
  "projects": {
    "current": ["website-redesign", "api-v2"],
    "completed": ["mobile-app-launch"]
  }
}

Memory hierarchy

Organize memory by permanence and scope:

MEMORY.md           # Long-term, curated (like human long-term memory)
memory/
  2024-01-15.md    # Daily logs (like human episodic memory)
  2024-01-16.md
  heartbeat-state.json  # Operational state
projects/
  website/         # Project-specific context
  api-v2/

Memory maintenance

Memory needs curation to stay useful:

# Regular maintenance tasks

1. Review recent daily logs
2. Extract significant events to MEMORY.md
3. Remove outdated information
4. Consolidate related notes
5. Archive completed project context

The daily log pattern

Each day gets a log file:

• Raw notes of what happened
• Decisions made
• Things to remember
• Open questions

At the start of each session, read today's and yesterday's logs for recent context.

The curated memory pattern

Separate raw logs from curated knowledge:

• Daily files: Everything that happened
• MEMORY.md: Distilled, important, long-term

Periodically review daily files and update MEMORY.md with what's worth keeping.

The explicit write pattern

When something should be remembered, write it explicitly:

User: "Remember that Sarah's birthday is March 15"
Agent: *writes to memory* "Noted. I've saved that Sarah's birthday 
       is March 15."

Don't rely on implicit memory—what matters gets written.

The session context pattern

At session start, load relevant context:

def start_session():
    context = []
    context.append(read_file("SOUL.md"))      # Identity
    context.append(read_file("USER.md"))       # User info
    context.append(read_file("MEMORY.md"))     # Long-term memory
    context.append(read_file(f"memory/{today}.md"))    # Today
    context.append(read_file(f"memory/{yesterday}.md")) # Yesterday
    return context

Local-first memory

Keep memory on your own machine:

• Files stay under your control
• No cloud storage of personal context
• Full ownership of your data

Sensitive content handling

Some things shouldn't go in memory:

• Passwords and credentials
• Financial details
• Medical information
• Others' private information

Define clear policies:

# Memory Policy

DO save:
- Preferences and working styles
- Project context and decisions
- Task history and outcomes

DON'T save:
- Credentials or API keys
- Financial account details
- Health information
- Third-party private information

Shared context awareness

In group settings, be careful what memory is loaded:

• Personal memory shouldn't leak to group chats
• Shared context stays appropriate for the audience
• Separate private and shared memory spaces

Challenge: Memory bloat

Memory files grow indefinitely, degrading performance.

Solution:

• Regular archival of old logs
• Summarization of lengthy context
• Pruning of outdated information
• Token-aware loading strategies

Challenge: Contradictory memory

Old information conflicts with new:

Solution:

• Timestamp all entries
• Prefer recent over old when conflicting
• Regular review to remove stale information
• Explicit updates when things change

Challenge: Irrelevant context

Loading everything wastes tokens and confuses the agent.

Solution:

• Hierarchical memory (load summaries, drill down when needed)
• Relevance-based retrieval (search, not load all)
• Session-appropriate filtering

Challenge: Memory corruption

Agents write incorrect information to memory.

Solution:

• Human review of significant memory updates
• Versioning/backup of memory files
• Clear guidelines on what to memorize
• Correction procedures when errors found

When multiple agents work together, memory becomes more complex:

Shared memory Common knowledge accessible to all agents:

• Project documentation
• Team conventions
• Shared resources

Private memory Agent-specific context:

• Individual reasoning and learnings
• Specialized knowledge
• Role-specific information

Communication memory Records of inter-agent coordination:

• Messages between agents
• Task handoffs
• Collaborative decisions

Memory transforms AI from a tool into a collaborator. The agent that remembers:

• Builds relationship over time
• Develops contextual intelligence
• Provides personalized assistance
• Creates continuity and trust

This isn't artificial general intelligence—it's practical memory management that creates the experience of working with someone who knows you.

Production systems like Clawdbot/OpenClaw use file-based memory extensively. The pattern is simple but powerful: text files beat neural memory every time.