Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.
Borrowing it
Nothing to install: this file belongs to topoteretes/cognee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/topoteretes/cognee/main/.agents/skills/multiagent-memory-setup/SKILL.mdgit clone --depth 1 https://github.com/topoteretes/cogneeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/topoteretes/cognee/multiagent-memory-setup)<a href="https://agentmods.dev/skills/topoteretes/cognee/multiagent-memory-setup"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/multiagent-memory-setup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/topoteretes/cognee/multiagent-memory-setup"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/multiagent-memory-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00110 | $0.01263 |
| Opus 5.5 | $0.00044 | $0.00505 |
| Sonnet 5.5 | $0.00022 | $0.00253 |
| Haiku 4.5 | $0.00011 | $0.00126 |
Grade A, and why
multiagent-memory-setup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Import coding-agent sessions
Runs the cookbook in examples/cookbooks/import_agent_sessions/. It first finds the coding
agents installed on this machine and the sessions each has kept. One LLM key
(LLM_API_KEY, OPENAI_API_KEY or ANTHROPIC_API_KEY) runs cognee and imports every agent's
history. The chosen agents go into the dataset imported_agent_sessions, and each question is
answered over all of them. Setup, the scripts and what each one does are in the cookbook's
README.md.
Run every command from the repo root.
Demo
When the user has no Claude Code or Codex history, or wants to see it work first, run the
demo instead of the steps below. It needs one key and imports sample sessions of both
agents (a made-up web shop), never the user's own. It is what the cookbook runs without
--agents: uv run python examples/cookbooks/import_agent_sessions/import_agent_sessions.py
(--check first to check the key; exit 2 with a MISSING: line means no key is set). Give
the user the [ask] answers and tell them they come from sample data. Pass --ask "..." for
their own question.
Steps
- Run
uv run python examples/cookbooks/import_agent_sessions/import_agent_sessions.py --check.- Exit 2: each
[setup] MISSING:line names one fix. Tell the user, using the README's "What it needs" table, and stop. Never ask the user to paste a key into the chat. - Exit 0: go on.
- Exit 2: each
- Tell the user what was found, from the
[find_agents]lines: each agent, how it is installed, and how many sessions it has and where they were started. Name eachNot imported:app (Claude desktop chats, Cursor's own agent, ChatGPT) and why its chats can't be read. - Ask the user which agent sessions to ingest into cognee, with your question tool when you
have one: one choice per agent the
OK:line names, several allowed. Skip asking only when the user already named the agents. Don't choose for them, and never leave--agentsout of the import: without it the run is the demo on sample data. - Ask the user which projects to import, from the
[find_agents] Project:lines, with "All projects" as one choice. Every imported session is sent to the LLM provider, and a machine often holds work the user can't share, so never assume all. When there are more projects than your question tool shows, list them and ask the user to name theirs. Skip asking only when the user already named the projects. - Run the import with
--agentsset to the user's choice, like--agents claude,codex, and one--project <path>per chosen project, copying the path from itsProject:line (leave--projectout for all projects). A project includes the folders inside it. Start with the default--limit 20unless the user asked for more, and pass--since YYYY-MM-DDfor a period. Pass their question with--ask "..."; without one the run asks "What did I work on in these sessions?":uv run python examples/cookbooks/import_agent_sessions/import_agent_sessions.py --agents <choice> --project <path> --ask "...". It takes a few minutes for 20 sessions. - The answers are on the
[ask]lines:all sessions>over everything, and with two agents one<agent> only>line each. Give them to the user. - Later questions don't need a new import:
uv run python examples/cookbooks/import_agent_sessions/scripts/ask.py "..." --agents claude,codex(list only the agents the user chose). - If a step fails, the run exits 1 and the traceback's last line says why. Run that script
from
examples/cookbooks/import_agent_sessions/scripts/on its own to look closer.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 80 lines · 110 tokens per session scan A eef54643807e
multiagent-memory-setup is a skill published in the GitHub repository topoteretes/cognee (33,094 stars, last pushed today), licensed Apache-2.0. It adds 110 tokens to every session and 1,263 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-30.
multiagent-memory-setup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| multiagent-memory-setuptopoteretes/cognee | Skill | 33k | today | Apache-2.0 | A | 110 | Claude Code, Codex |
| openmemoryCaviraOSS/OpenMemory | Skill | 4.6k | 19d ago | Apache-2.0 | A | 96 | Claude Code, Codex |
| memseek-remembermemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 24 | Claude Code |
| memseek-explainmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 25 | Claude Code |
| memseek-searchmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 22 | Claude Code |
| surface-learningsterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 60 | Claude Code |
| context-recoveryterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 37 | Claude Code |