plungeai-memory

Read and write PlungeAI's per-user long-term memory (plungeai_memory: recall/remember/search_runs/get_run) and distill a session into a reusable skill with plungeai_learn — distinct from SharedMemory (a single run's output). Use when the user says remember this, asks what the agent knows about them, wants to review past runs, or wants findings saved as a skill for future runs. For a single run's output use plungeai-results-traces; for the skill/expert/persona/background/plugin capability system a saved skill feeds into use plungeai-skills-plugins.

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Two separate systems, never confuse them: SharedMemory holds one execution's task outputs (read with plungeai_get_result); long-term memory holds what the platform has learned about the user across every run, recalled automatically at mission start and written back with plungeai_memory or the agent's in-loop memory tool.

Prerequisites

• A self-service ozk_ API key from Dashboard → One API → Keys (https://dashboard.plungeai.com (opens in a new tab)), or an MCP client connected to https://mcp.plungeai.com/v1.
• No discovery step needed for the memory tools themselves — they operate on the caller's own store. Discover agents you plan to reference (e.g. in learn) first: plungeai_list_agents.

Reading and writing

plungeai_memory {action: "recall"}                        # read the durable store
plungeai_memory {action: "remember", target: "user",       # user | memory
                 operation: "add", content: "Prefers weekly summaries, no emojis"}
plungeai_memory {action: "search_runs", query: "competitor analysis"}
plungeai_memory {action: "get_run", run_id: "…"}

recall returns the same MEMORY.md + USER.md snapshot a mission gets automatically at launch. Use remember any time the user says "remember this" — chat context alone does not persist. replace/remove need the exact old_text; a failed attempt returns the current entries so you can copy it verbatim and retry.

Distilling a session into a skill — plungeai_learn

plungeai_learn {source: "<a URL, pasted text, or distilled findings from this chat>"}
plungeai_learn {action: "list"}                        # your learned skills
plungeai_learn {action: "forget", name: "<skill id>"}  # delete one you own

action defaults to learn (async: poll plungeai_get_workflow_status, fetch with plungeai_get_result). Offer this "learn-back" move whenever a session produced real research worth reusing — the saved skill is private to the caller and can be declared on future mission/harness tasks (plungeai-skills-plugins).

Gotchas

• Recall is a frozen snapshot taken at run start — mid-run writes are durable but do not change the running prompt; two missions launched together never see each other's writes.
• episodes.md (the append-only run log) is never injected into recall — pull run history explicitly with search_runs / get_run.
• Memory is small and curated on purpose (MEMORY ~2200 chars, USER ~1375 default, clamped 500–20000) — write a few load-bearing facts, not a dump; every entry is threat-scanned twice (rejected at write, [BLOCKED]-replaced at snapshot build if it slipped through).
• Bots do not set memory_owner (policy since 2026-08-23 — omit it entirely). A bot run reads/writes the owner's general memory namespace, shared across all their bots and runs; a hardcoded per-bot value is rejected by the harness guard unless it names the runner's own identity, so it is redundant at best. See orchestration/BOT-CREATION.md.
• Delegate children never write memory (the memory tool is stripped) — one writer per run.
• Standing company/product context that should apply to every run is NOT memory — it is a background card; a reusable method is a skill — see plungeai-skills-plugins.

Related skills

• plungeai-results-traces — SharedMemory / plungeai_get_result, a single run's output.
• plungeai-missions — the mission lifecycle memory recalls into and writes from.
• plungeai-scheduling — which scheduled job types keep the memory lifecycle.
• plungeai-skills-plugins — declaring the skill plungeai_learn just saved.

Reference

• references/memory.md — full SharedMemory-vs-long-term-memory comparison, the three-layer store (USER/MEMORY/episodes), budgets, safety scanning, namespaces, and the plungeai_memory / plungeai_learn MCP tool contracts.

Reference pages