git:20261021.34a9cb4 to git:20261021.3621177

7 added, 7 removed. Audit B to B.

---

name: multiagent-memory-setup

description: Import the user's own Claude Code and/or Codex session history (the prompts they typed and the agents' answers) into one cognee dataset, then answer questions over it, across every agent and per agent. Use when someone wants to load, import or backfill their previous coding-agent sessions into cognee, give several coding agents one shared memory of past work, or ask what their earlier sessions were about. Runs locally; reads ~/.claude and ~/.codex read-only, writes only to cognee's memory.

---

# 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`](../../../examples/cookbooks/import_agent_sessions/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:

- `uv run python examples/cookbooks/import_agent_sessions/import_agent_sessions.py --demo`

+ 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

1. 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.

2. 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 each

`Not imported:` app (Claude desktop chats, Cursor's own agent, ChatGPT) and why its chats

can't be read.

3. Ask the user which agent sessions to ingest into cognee, with your question tool when you

- have one: one choice per `[setup] Can import:` line, several allowed. Skip asking only

- when the user already named the agents. Don't choose for them: without `--agents` the

- import stops with `[setup] CHOOSE:`, because the script cannot ask without a terminal.

+ 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

+ `--agents` out of the import: without it the run is the demo on sample data.

4. 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.

5. Run the import with `--agents` set to the user's choice, like `--agents claude,codex`, and

one `--project <path>` per chosen project, copying the path from its `Project:` line (leave

`--project` out for all projects). A project includes the folders inside it. Start with the

default `--limit 20` unless the user asked for more, and pass `--since YYYY-MM-DD` for a

- period. Pass their question with `--ask "..."`; without one the run asks "What is the topic

- of my data?":

+ 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.

6. 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.

7. 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).

8. 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.

## Rules

- Session history is the user's private data. Don't quote it beyond what answers the

question, and don't print `.env`.

- Every imported session is sent to the provider of the key cognee runs on and costs LLM calls, so run the import only when the user asked for it,

and raise `--limit` only when they want more history.

- Pass `--clear` only when the user asked to start over: it forgets everything the dataset

holds. It never touches `~/.claude` or `~/.codex`.

- Redaction catches common key formats and secret-named values (`DB_PASSWORD=...`) only. If the user is about to import a lot, suggest

they look at a few files in `examples/cookbooks/import_agent_sessions/sessions/` first.

## Clean up

```bash

uv run cognee-cli forget --dataset imported_agent_sessions

uv run cognee-cli forget --dataset imported_agent_sessions_demo

rm -r examples/cookbooks/import_agent_sessions/sessions examples/cookbooks/import_agent_sessions/sample

```