Writing memory

After each turn of a conversation, your app tells EDN what was said. EDN decides what to remember and labels every record with its source.

Which calls to make

Call	When	Required?	What EDN saves
`memory/ingest`	After every turn	Yes	The exchange, any facts the user stated, and any corrections
`memory/infer`	After `memory/ingest`	Optional	The AI’s answer, if it answers a factual question and passes EDN’s check
`memory/generated`	Only when `memory/infer` returns `AI_GENERATED`	Optional	The AI’s statement, kept separate as unconfirmed

For documents, see the guide Grounding answers in documents. Corrections are handled by memory/ingest; see Corrections. The examples use the EDN_API_KEY and EDN_SESSION_ID environment variables from the Quickstart.

Save each turn

Call memory/ingest with the user’s message and your model’s reply:

curl https://api.tensorpro.ai/api/v1/memory/ingest \
  -H "Authorization: Bearer $EDN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "user_message": "Sara manages the website project.",
    "assistant_message": "Thanks, I will remember that.",
    "session_id": "'"$EDN_SESSION_ID"'"
  }'

import os
import requests

EDN = "https://api.tensorpro.ai/api/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['EDN_API_KEY']}"}
session_id = os.environ["EDN_SESSION_ID"]

resp = requests.post(
    f"{EDN}/memory/ingest",
    headers=HEADERS,
    json={
        "user_message": "Sara manages the website project.",
        "assistant_message": "Thanks, I will remember that.",
        "session_id": session_id,
    },
)
saved = resp.json()

const EDN = "https://api.tensorpro.ai/api/v1";
const HEADERS = {
  Authorization: `Bearer ${process.env.EDN_API_KEY}`,
  "Content-Type": "application/json",
};
const sessionId = process.env.EDN_SESSION_ID;

const resp = await fetch(`${EDN}/memory/ingest`, {
  method: "POST",
  headers: HEADERS,
  body: JSON.stringify({
    user_message: "Sara manages the website project.",
    assistant_message: "Thanks, I will remember that.",
    session_id: sessionId,
  }),
});
const saved = await resp.json();

Field	Required	What it does
`user_message`	Yes	What the user said
`assistant_message`	Yes	Your model’s reply. Citation markers like `[1]` are removed before saving
`session_id`	Yes	The conversation this turn belongs to
`project_id`	No	Save into a project instead of general memory
`timestamp`	No	When the exchange happened (ISO 8601), for importing past conversations. Omit it to use the current time

Response

{
  "record_id": "53c7483b-1238-4129-8dcf-411beea0473a",
  "fact": {
    "record_id": "8f0d2c61-5b7e-4a39-9c1e-2d4f6a8b0e17",
    "replaces": null
  }
}

Field	What it means
`record_id`	The ID of the saved conversation turn (a `ROUND` record)
`fact`	The fact EDN saved from the user’s message, as a `USER_STATED` record. `null` when the message didn’t state a fact
`fact.replaces`	The ID of the older fact this one replaced, when the user corrected something. Otherwise `null`. See Corrections

Keep track of AI answers (optional)

Your model’s answers are never used as context on their own. But if you record them, a user can later confirm one, and it becomes a trusted fact. Two calls do this.

1. Classify the reply with `memory/infer`

Pass the sip you got from sip/build earlier in the turn.

curl https://api.tensorpro.ai/api/v1/memory/infer \
  -H "Authorization: Bearer $EDN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "user_message": "Who manages the website project?",
    "assistant_message": "Sara manages the website project.",
    "session_id": "'"$EDN_SESSION_ID"'",
    "sip": "PASTE_THE_SIP_HERE"
  }'

resp = requests.post(
    f"{EDN}/memory/infer",
    headers=HEADERS,
    json={
        "user_message": user_text,
        "assistant_message": reply,
        "session_id": session_id,
        "sip": sip["sip"],  # from sip/build
    },
)
result = resp.json()

const inferResp = await fetch(`${EDN}/memory/infer`, {
  method: "POST",
  headers: HEADERS,
  body: JSON.stringify({
    user_message: userText,
    assistant_message: reply,
    session_id: sessionId,
    sip: sip.sip, // from sip/build
  }),
});
const result = await inferResp.json();

Response

{
  "saved": true,
  "provenance": "AI_INFERRED",
  "record_id": "c2e9a7d4-3f1b-4e8a-b6d0-91a5f3c7e284",
  "reason": null
}

If the reply answers the user’s factual question and passes EDN’s check, EDN saves it as AI_INFERRED: saved is true and record_id is set.
Pass the sip so EDN can check the reply against the memory the model was given. Without it, replies that state specific figures, or say who holds a role, are not saved.
When nothing is saved, saved is false and reason says why:

`reason`	Meaning
`not_an_answer`	The reply doesn’t answer a factual question from the user. `provenance` is `AI_GENERATED`
`abstention`	The reply says it doesn’t know
`unsupported_claim`	A fact in the reply isn’t backed by the `sip` you sent

2. If the result is `AI_GENERATED`, save it with `memory/generated`

curl https://api.tensorpro.ai/api/v1/memory/generated \
  -H "Authorization: Bearer $EDN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"assistant_message": "Here is a draft plan for the launch.", "session_id": "'"$EDN_SESSION_ID"'"}'

if result["provenance"] == "AI_GENERATED":
    requests.post(
        f"{EDN}/memory/generated",
        headers=HEADERS,
        json={"assistant_message": reply, "session_id": session_id},
    )

if (result.provenance === "AI_GENERATED") {
  await fetch(`${EDN}/memory/generated`, {
    method: "POST",
    headers: HEADERS,
    body: JSON.stringify({ assistant_message: reply, session_id: sessionId }),
  });
}

Response

{ "record_id": "5d81f0b3-9a6c-4e27-8b4f-0c3e2a7d1f96" }

Neither AI_INFERRED nor AI_GENERATED records are used as context until a user confirms them with memory/confirm.

Importing past conversations

To load conversation history, call memory/ingest once per past turn, with timestamp set to when it happened. EDN dates each record by that time instead of the moment you import it.

Next steps

Building context

Read memory back with a SIP.

Corrections

How changed facts are handled.

Memory records

What these calls create.

Get started

Core concepts

Guides

Reference guides

Writing memory

Which calls to make

Save each turn

Keep track of AI answers (optional)

1. Classify the reply with `memory/infer`

2. If the result is `AI_GENERATED`, save it with `memory/generated`

Importing past conversations

Next steps

Building context

Corrections

Memory records

Which calls to make

Save each turn

Keep track of AI answers (optional)

1. Classify the reply with memory/infer

2. If the result is AI_GENERATED, save it with memory/generated

Importing past conversations

Next steps

Building context

Corrections

Memory records

1. Classify the reply with `memory/infer`

2. If the result is `AI_GENERATED`, save it with `memory/generated`