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Parallel Memory

Organization
parallel-web
parallel-memory

Recall past Parallel Task, Monitor, and FindAll runs when they may help; evict runs or clear memory when asked.

Overview

Publisherparallel-web
Repositoryparallel-agent-skills
Skill nameparallel-memory
Stars
77
Forks
10
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by parallel-web on GitHub. Read the source before you install it.

Installation

Install the Parallel Memory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-memory
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/parallel-web/parallel-agent-skills.git /tmp/parallel-agent-skills
mkdir -p .claude/skills
cp -r /tmp/parallel-agent-skills/skills/parallel-memory .claude/skills/parallel-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Memory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Parallel Memory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Parallel Memory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Parallel Memory

Action: $ARGUMENTS

Requires parallel-cli >=0.8.1. If the installed version is older, or parallel-cli memory --help fails with no such command or similar, tell the user to update parallel-cli, then retry.

When to use

  • Inspect memory only when prior Parallel work may help fulfill the request or the user asks to retrieve, evict, or clear it.
  • Memory results are excerpts from past runs; fetch the source run for full records, and launch a fresh run when current information is required.

Choose the operation

User intentOperation
Recall prior work about a topicRetrieve with a concise query
Show recent past runsRetrieve without query
Remove one saved Task, Monitor, or FindAll sourceEvict by exact kind and id
Permanently remove all entries from your personal MemoryClear memory
Turn memory offDirect the user to account settings; do not clear as a substitute

Use the CLI

Use parallel-cli memory for retrieve, evict, and clear operations.

  • If memory is not eligible, report the returned reason; it distinguishes rollout, organization settings, account opt-in, and key eligibility.
  • On a key-eligibility error, tell the user to reauthenticate.

Retrieve memory

Form a short semantic query that describes the prior work to find. Apply filters when they help. Empty results is a successful retrieval with no matches, not an error.

  • Set kind to task, monitor, or findall when it clearly narrows the retrieval.
  • Set since for an explicit timestamp boundary (RFC 3339, e.g. 2026-08-01T00:00:00Z).
  • Omit query when retrieving recent memories rather than a topic.

Retrieve by query:

bash
parallel-cli memory retrieve \
  --query "serverless inference vendors"

For recent memories:

bash
parallel-cli memory retrieve \
  --limit 5

Use results

Available fields vary by kind:

  • task: use id, updated_at, input_excerpt, and output_excerpt.

  • monitor: use the monitor id, status, query excerpt, and matching event IDs, timestamps, and excerpts.

  • findall: use id, updated_at, objective excerpt, and matched_count.

  • Fetch the original Task result, Monitor events, or FindAll result when exact output, entities, citations, or provenance matter.

  • Summarize the useful findings and unresolved questions.

  • Lead with what the prior work established, then list the contributing saved runs with kind, ID, and timestamp.

  • Distinguish recalled information from any fresh verification.

  • Parallel runs do not consult Memory. If recalled information may be useful, include the relevant details in the new run's input.

Expect ingestion to be asynchronous. Do not promise that a newly completed run will be immediately retrievable.

Evict or clear memory

Evict a single run from your personal Memory, or clear it entirely. These do not delete the underlying Parallel runs. Ask for confirmation before clearing unless the user already asked for it.

bash
parallel-cli memory evict \
  --kind task \
  --id "trun_example"
bash
parallel-cli memory clear \
  --confirm-clear

Frequently asked questions

What does the Parallel Memory AI skill do?

Recall past Parallel Task, Monitor, and FindAll runs when they may help; evict runs or clear memory when asked.

Why use Parallel Memory on TypingMind?

Because you install it once and use it with any model. Parallel Memory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Parallel Memory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Parallel Memory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Parallel Memory?

As many as you like. As long as a model supports skills, you can use Parallel Memory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Parallel Memory AI skill free?

Yes. It is published on GitHub by parallel-web under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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