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Fliiq maintains persistent memory that carries context across sessions. The agent reads and writes memory naturally using built-in memory skills — no special commands needed.

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Memory Structure

~/.fliiq/memory/
  MEMORY.md              # Curated memory — always loaded into prompt
  2026-02-12.md           # Daily log
  2026-02-11.md
  skills/
    fitness.md            # Skill-specific memory
    spotify.md
  jobs/
    fitness-coach.md      # Per-job memory
    email-digest.md
  people/
    alice-chen.md         # Auto-extracted: person facts + decisions
    bob-smith.md
  topics/
    project-alpha.md      # Auto-extracted: topic facts + decisions
    decisions.md          # Unscoped decisions log

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MEMORY.md

The curated memory file is loaded into every agent prompt. It contains important facts the agent should always know: your preferences, project context, key decisions, recurring patterns. The agent updates MEMORY.md when it learns something worth remembering. You can also edit it directly.

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Daily Logs

Each day gets a log file (YYYY-MM-DD.md). The agent appends to the current day’s log during sessions. Recent daily logs (last 3 days) are auto-loaded into the prompt for recency context.

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Skill Memories

When the agent works on a specific domain (fitness, language learning, a project), it creates a skill memory file in memory/skills/. These capture domain-specific context that doesn’t belong in the general MEMORY.md.

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Job Memories

Each scheduled job gets its own memory file at memory/jobs/<job-name>.md. The job executor loads this file at the start of each run, so the agent knows what it did last time. This enables progressive context — a fitness coaching job that tracks workout progression over weeks.

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Automatic Knowledge Extraction

After each conversation, Fliiq runs a post-session LLM pass to extract institutional knowledge and persist it automatically. No manual effort required. What gets extracted:
  • People — names, roles, relationships, preferences, contact details
  • Topics — ongoing projects, recurring interests, goals, decisions made
  • Decisions — non-trivial choices with reasoning
Each entity is stored as a structured markdown file under memory/people/ or memory/topics/. On subsequent runs, Fliiq merges new facts into existing files rather than duplicating them. What’s filtered out: one-time tasks, ephemeral instructions, vague observations, and implementation details with no lasting value. This means context about your projects, collaborators, and preferences accumulates automatically — the agent gets more useful the more you use it.

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Memory Skills

The agent manages memory through three built-in skills:
SkillPurpose
memory_readRead a specific memory file
memory_writeWrite or update a memory file
memory_searchKeyword search across all memory files
These are called naturally by the agent — you don’t need to instruct it to use memory. If you mention a preference or the agent learns something useful, it writes it to the appropriate memory file.

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Manual Editing

Memory files are plain markdown. You can edit them directly: 
# Edit curated memory
nano ~/.fliiq/memory/MEMORY.md

# Or from a project
nano .fliiq/memory/MEMORY.md