What is in fastapi-langgraph-agent-production-ready-template's system prompt?
fastapi-langgraph-agent-production-ready-template's full system prompt: 1 version, 2,626 characters. Audited against AISPA.
The full text of 1
prompt is reproduced below,
2,626 characters in all, each read
instruction by instruction against the eight
AISPA dimensions.
Nothing was flagged as working against the person on
the other end.
1Prompts on record
0Flagged instructions
AI auditAudit source
D3 · Privacy & Data Protection
D4 · Tool/Action Safety
# Authentication
## Flow
```mermaid
sequenceDiagram
participant C as Client
participant A as API
C->>A: POST /auth/register<br/>{email, password, username?}
A-->>C: {user_id, email, username, token}
C->>A: POST /auth/login<br/>form: email + password
A-->>C: {access_token, expires_at}
C->>A: POST /auth/session<br/>Bearer: user token
A-->>C: {session_id, token}
C->>A: POST /chatbot/chat<br/>Bearer: session token
A-->>C: {messages}
```
The API uses **two token scopes**:
- **User token** — issued on register/login, identifies the user. Used to create and list sessions.
- **Session token** — issued per conversation session. Required for all chat endpoints. Scoped to a single `session_id`.
Both are signed JWTs (HS256) with a configurable expiry (`JWT_ACCESS_TOKEN_EXPIRE_DAYS`).
---
## Endpoints
### `POST /api/v1/auth/register`
Create a new account.
```json
{
"email": "you@example.com",
"password": "Secret123!", // pragma: allowlist secret
"username": "you"
}
```
Password requirements: 8+ chars, uppercase, lowercase, number, special character.
`username` is optional. When provided, it's passed to the agent's system prompt so the LLM knows the user's name.
---
### `POST /api/v1/auth/login`
Exchange credentials for a user token. Uses OAuth2 password grant form fields.
```bash
curl -X POST /api/v1/auth/login \
-F "email=you@example.com" \
-F "password=Secret123!" \
-F "grant_type=password"
```
Returns `access_token` and `expires_at`.
---
### `POST /api/v1/auth/session`
Create a new chat session. Requires a valid user token.
```bash
curl -X POST /api/v1/auth/session \
-H "Authorization: Bearer <user token>"
```
Returns `session_id` and a session-scoped `token`. Use this session token for all subsequent chat requests.
---
### `PATCH /api/v1/auth/session/{session_id}/name`
Rename a session.
```bash
curl -X PATCH /api/v1/auth/session/{session_id}/name \
-H "Authorization: Bearer <session token>" \
-F "name=My research session"
```
---
### `DELETE /api/v1/auth/session/{session_id}`
Delete a session and its chat history.
---
### `GET /api/v1/auth/sessions`
List all sessions for the authenticated user. Requires a user token.
---
## Security notes
- Passwords are hashed with bcrypt before storage — plaintext is never persisted.
- JWTs include a `jti` (JWT ID) claim for token uniqueness.
- All string inputs are sanitised before use.
- Rate limits protect the register (10/hour) and login (20/min) endpoints against brute force.
- Set a long random `JWT_SECRET_KEY` in production — at least 32 characters.
Questions about fastapi-langgraph-agent-production-ready-template's system prompt
Does fastapi-langgraph-agent-production-ready-template's system prompt contain instructions that work against the user?
No. Nothing in fastapi-langgraph-agent-production-ready-template's system prompt was flagged as working against the person the product is talking to. That is a clean result across all eight AISPA dimensions, not an absence of checking — the full text was read instruction by instruction.
How long is fastapi-langgraph-agent-production-ready-template's system prompt?
2,626 characters across 1 prompt on this page. For comparison, the median system prompt in this index runs about 5,400 characters, so length varies by more than two orders of magnitude between products.
How many versions of fastapi-langgraph-agent-production-ready-template's system prompt are on record?
1. Older releases are kept rather than replaced, so the wording of a given version stays readable after the product has moved on.
Where did this fastapi-langgraph-agent-production-ready-template system prompt come from?
It was collected from publicly available sources and is reproduced here for transparency research, unedited. This site does not extract prompts from products itself.
How was fastapi-langgraph-agent-production-ready-template's system prompt audited?
Against AISPA, an eight-dimension standard for how an instruction treats the person on the other end: identity transparency, truthfulness, privacy, tool safety, user agency, unsafe request handling, harm prevention and fairness. This audit was ai audit. The method is described in the paper behind the standard.
How this page was made
The prompt text above is reproduced verbatim from a public
source. Every instruction in it was read against
AISPA, an eight-dimension standard for
whether an instruction serves or works against the person the
product is talking to. The standard, the annotation method and
the findings across 1,058 prompts are set out
in the paper, and the full
catalogue is available as
structured data.
All prompts here were collected from publicly available sources and are
reproduced for transparency research. Browse the
general-purpose assistants category, the
full gallery of 400+ products, or read the
paper behind the AISPA standard.