Assemble Context

POST /v1/context — Assemble a context block from memories for LLM prompts.

POST /v1/context

Assemble Context

cURL

curl --request POST \
  --url https://api.memoclaw.com/v1/context \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "namespace": "<string>",
  "session_id": "<string>",
  "agent_id": "<string>",
  "max_memories": 123,
  "max_tokens": 123,
  "format": "<string>",
  "include_metadata": true,
  "summarize": true
}
'

import requests

url = "https://api.memoclaw.com/v1/context"

payload = {
    "query": "<string>",
    "namespace": "<string>",
    "session_id": "<string>",
    "agent_id": "<string>",
    "max_memories": 123,
    "max_tokens": 123,
    "format": "<string>",
    "include_metadata": True,
    "summarize": True
}
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({
    query: '<string>',
    namespace: '<string>',
    session_id: '<string>',
    agent_id: '<string>',
    max_memories: 123,
    max_tokens: 123,
    format: '<string>',
    include_metadata: true,
    summarize: true
  })
};

fetch('https://api.memoclaw.com/v1/context', 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/context",
  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([
    'query' => '<string>',
    'namespace' => '<string>',
    'session_id' => '<string>',
    'agent_id' => '<string>',
    'max_memories' => 123,
    'max_tokens' => 123,
    'format' => '<string>',
    'include_metadata' => true,
    'summarize' => true
  ]),
  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/context"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\",\n  \"max_memories\": 123,\n  \"max_tokens\": 123,\n  \"format\": \"<string>\",\n  \"include_metadata\": true,\n  \"summarize\": true\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/context")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\",\n  \"max_memories\": 123,\n  \"max_tokens\": 123,\n  \"format\": \"<string>\",\n  \"include_metadata\": true,\n  \"summarize\": true\n}")
  .asString();

require 'uri'
require 'net/http'

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

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  \"query\": \"<string>\",\n  \"namespace\": \"<string>\",\n  \"session_id\": \"<string>\",\n  \"agent_id\": \"<string>\",\n  \"max_memories\": 123,\n  \"max_tokens\": 123,\n  \"format\": \"<string>\",\n  \"include_metadata\": true,\n  \"summarize\": true\n}"

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

200

{
  "context": "<string>",
  "memories_used": 123,
  "tokens": 123
}

This endpoint requires an x402 payment header.

Price: $0.01 USDC

Automatically assemble relevant memories into a context block ready for LLM prompts. This is useful for building AI assistants that need contextual memory.

Request Body

query · string

Natural language query describing what context is needed. Used to find relevant memories.

namespace · string

Filter by namespace.

session_id · string

Filter by session ID.

agent_id · string

Filter by agent ID.

max_memories · number

Maximum number of memories to include. Default: 10. Max: 100.

max_tokens · number

Target maximum tokens for the context. Default: 4000. Range: 100-16000.

format · string

Output format: text (plain text) or structured (JSON with metadata). Default: text.

include_metadata · boolean

Include memory metadata (tags, importance, type) in the output. Default: false.

summarize · boolean

Use LLM to summarize multiple similar memories into fewer entries. Default: false.

Response (200 OK)

context · string

The assembled context text or JSON.

memories_used · number

Number of memories included in the context.

tokens · number

Approximate token count of the context.

Example

curl

curl -X POST https://api.memoclaw.com/v1/context \
  -H "Content-Type: application/json" \
  -d '{
    "query": "user preferences and project context",
    "max_memories": 5,
    "max_tokens": 2000,
    "format": "text"
  }'

const response = await fetch("https://api.memoclaw.com/v1/context", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    query: "user preferences and project context",
    max_memories: 5,
    max_tokens: 2000,
    format: "text",
  }),
});
const data = await response.json();

from memoclaw import MemoClaw

client = MemoClaw()

result = client.assemble_context(
    query="user preferences and project context",
    max_memories=5,
    max_tokens=2000,
    format="text",
)
print(result.context)

import { MemoClawClient } from "memoclaw";

const client = new MemoClawClient();

const result = await client.assembleContext({
  query: "user preferences and project context",
  maxMemories: 5,
  maxTokens: 2000,
  format: "text",
});
console.log(result.context);

Response

{
  "context": "User preferences:\n- Prefers dark mode\n- Uses vim keybindings\n- Timezone: PST\n\nProject context:\n- Using PostgreSQL 15 with pgvector\n- Deploy to staging before production",
  "memories_used": 5,
  "tokens": 180
}

Response (structured format)

{
  "context": {
    "memories": [
      {
        "content": "User prefers dark mode",
        "importance": 0.8,
        "memory_type": "preference",
        "tags": ["ui"],
        "source": "recall"
      },
      {
        "content": "Uses vim keybindings",
        "importance": 0.7,
        "memory_type": "preference",
        "tags": ["editor"],
        "source": "recall"
      }
    ]
  },
  "memories_used": 2,
  "tokens": 85
}