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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

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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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Self-Improving Lessons Loop

After each conversation, a second post-session pass scans the transcript for behavioral corrections and reusable procedures, then writes or refines markdown lesson files under memory/lessons/. Lessons are injected into the system prompt at the start of every future session. What triggers a lesson:
  • You corrected style, tone, format, or verbosity (“stop doing X”, “too verbose”, “just give me the answer”)
  • You corrected the agent’s workflow or approach
  • A non-trivial technique or fix emerged that should be repeated
  • An existing lesson turned out to be wrong or outdated (the pass refines it in place)
Lessons are class-level — they capture patterns, not session-specific artifacts. A lesson like “use prose over bullet lists in responses” carries forward; a specific error message does not. Fliiq indexes conversation messages in a local SQLite FTS5 database at .fliiq/sessions.db. Use the search_sessions skill to recall past conversations:
Results include ranked snippets with session ID and timestamp. Pass session_id and around_ts to retrieve the surrounding message context for any hit.

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Memory Skills

The agent manages memory through three built-in skills: 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: