Extract Facts

POST /v1/memories/extract — Extract and store facts from conversation via LLM.

POST /v1/memories/extract

Extract Facts

cURL

curl --request POST \
  --url https://api.memoclaw.com/v1/memories/extract \
  --header 'Content-Type: application/json' \
  --data '
{
  "messages": [
    {}
  ],
  "namespace": "<string>",
  "session_id": "<string>",
  "agent_id": "<string>"
}
'

import requests

url = "https://api.memoclaw.com/v1/memories/extract"

payload = {
    "messages": [{}],
    "namespace": "<string>",
    "session_id": "<string>",
    "agent_id": "<string>"
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    messages: [{}],
    namespace: '<string>',
    session_id: '<string>',
    agent_id: '<string>'
  })
};

fetch('https://api.memoclaw.com/v1/memories/extract', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.memoclaw.com/v1/memories/extract",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'messages' => [
        [
                
        ]
    ],
    'namespace' => '<string>',
    'session_id' => '<string>',
    'agent_id' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.memoclaw.com/v1/memories/extract"

	payload := strings.NewReader("{\n  \"messages\": [\n    {}\n  ],\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.memoclaw.com/v1/memories/extract")
  .header("Content-Type", "application/json")
  .body("{\n  \"messages\": [\n    {}\n  ],\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.memoclaw.com/v1/memories/extract")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"messages\": [\n    {}\n  ],\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

200

{
  "memory_ids": [
    "<string>"
  ],
  "facts_extracted": 123,
  "facts_stored": 123,
  "facts_deduplicated": 123,
  "tokens_used": 123
}

This endpoint requires an x402 payment header.

Price: $0.01 USDC (includes LLM processing)

Extract automatically identifies and stores important facts from a conversation. The LLM parses the messages and creates individual memories for each distinct fact, with automatic deduplication.

Request Body

messages · array · required

Array of conversation messages. Each message must have role (string) and content (string). Max 100 messages, 32,768 characters per message.

namespace · string

Namespace for extracted memories. Default: "default".

session_id · string

Associate extracted memories with a session.

agent_id · string

Associate extracted memories with an agent.

Response (201 Created)

memory_ids · string[]

UUIDs of the stored memories (includes both new and deduplicated).

facts_extracted · number

Total facts identified by the LLM.

facts_stored · number

New facts stored (not duplicates).

facts_deduplicated · number

Facts that matched existing memories (skipped).

tokens_used · number

Total tokens consumed (LLM + embeddings).

Example

curl

curl -X POST https://api.memoclaw.com/v1/memories/extract \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "I prefer dark mode in all my apps. Also, my timezone is PST."},
      {"role": "assistant", "content": "Got it! I will remember your preferences."}
    ]
  }'

const response = await fetch("https://api.memoclaw.com/v1/memories/extract", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    messages: [
      { role: "user", content: "I prefer dark mode in all my apps. Also, my timezone is PST." },
      { role: "assistant", content: "Got it! I will remember your preferences." },
    ],
  }),
});

const data = await response.json();

from memoclaw import MemoClaw

client = MemoClaw()
result = client.extract([
    {"role": "user", "content": "I prefer dark mode in all my apps. Also, my timezone is PST."},
    {"role": "assistant", "content": "Got it! I will remember your preferences."},
])

Response

{
  "memory_ids": [
    "550e8400-e29b-41d4-a716-446655440010",
    "550e8400-e29b-41d4-a716-446655440011"
  ],
  "facts_extracted": 2,
  "facts_stored": 2,
  "facts_deduplicated": 0,
  "tokens_used": 185
}

The LLM automatically assigns importance and memory type based on the content. Corrections and preferences get higher importance; observations get lower. Each fact is deduplicated against existing memories.