> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anonalabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Candidate memory across interviewers

> Every interviewer sees what the others already covered, so the fourth conversation is not the first one again.

[← All use cases](/use-cases/overview)

<div className="uc-examples"><a href="https://github.com/anonalabs/Anona-Memory-SDK/blob/main/examples/support_bot_scoping.py">support\_bot\_scoping.py</a><a href="https://github.com/anonalabs/Anona-Memory-SDK/blob/main/examples/knowledge_graph.py">knowledge\_graph.py</a></div>

## The scenario

Four interviews, four interviewers, and the candidate explains the same career
history four times. Meanwhile the thing the second interviewer was unsure about
never reaches the fourth, who could have probed it.

Scorecards fix the decision. They do not fix the conversation.

## Step 1 — Scope notes to the candidate

```python theme={null}
client.record(
    space_id="hiring",
    content="Strong on distributed systems; vague on how they handled the rollback, worth probing.",
    user_id="cand-8812",
    agent_id="interviewer-sam",
)
```

## Step 2 — Brief the next interviewer

```python theme={null}
print(client.ask_about_user(
    space_id="hiring",
    user_id="cand-8812",
    query="What is already covered, and what did earlier interviewers want probed?",
))
```

That is a thirty-second read that changes what the next hour is spent on.

## Step 3 — See the shape of the panel

```python theme={null}
entities = client.list_entities(space_id="hiring", limit=20)
```

Extraction names the technologies, companies and projects that came up. Across a
pipeline that is a genuinely useful read: which skills you are actually seeing,
rather than which ones you advertised for.

## Evals

1. Run one real loop, recording after each interview.
2. Before the final interview, generate the briefing and ask that interviewer
   whether it changed their plan.
3. Check no note leaks across candidates: query with a different `user_id` and
   confirm nothing returns.
4. Compare time-to-decision against loops run without it.

## Guardrails

<Warning>
  **Hiring notes are regulated personal data in most jurisdictions**, candidates
  can usually request them, and they carry real discrimination risk. Record
  evidence about the role — what was demonstrated, what was not — never
  impressions about the person. Set a retention period and honour it with
  [the deletion runbook](/use-cases/delete-a-users-memories).
</Warning>

* **Do not let a briefing replace an independent read.** Handing interviewer
  four a summary of three opinions is how a panel converges on the first
  opinion. Share what was *covered*; be careful about sharing what was
  *concluded*.
* **Never use this to score or rank candidates automatically.** It is a
  retrieval system, not an assessment, and in several jurisdictions automated
  decision-making here carries specific legal obligations.
* **Keep the space off-limits to the hiring manager's wider org.** Scope is
  relevance, not access.

## Built from

Both are complete, runnable scripts:
[`support_bot_scoping.py`](https://github.com/anonalabs/Anona-Memory-SDK/blob/main/examples/support_bot_scoping.py),
[`knowledge_graph.py`](https://github.com/anonalabs/Anona-Memory-SDK/blob/main/examples/knowledge_graph.py).
