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Your payment was received — we're setting up your workspace.
Your tenant ID — include in X-HeurChain-Tenant header on every API call.
Bring existing content into your memory — paste text or drop a file.
Your memory is stored as Markdown files in a per-tenant vault. Download a snapshot at any time — yours to keep, re-import, or open in Obsidian.
Tip: click "Flush" first if you want the most recent activity included in the export.
Store your first memory via the REST API — the broker auto-embeds server-side.
curl -X POST https://api.heurchain.com/store \ -H "X-HeurChain-API-Key: <your-api-key>" \ -H "X-HeurChain-Tenant: YOUR_TENANT_ID" \ -H "Content-Type: application/json" \ -d '{ "text": "hello world", "agent_id": "chatgpt" }'
Advanced: pass your own embedding array if you have one — see /embed for the server-side embedder.
import anthropic, requests
client = anthropic.Anthropic()
msg = client.messages.create(
model="claude-opus-4-7",
max_tokens=1024,
messages=[{"role": "user", "content": "Your prompt"}],
)
text = msg.content[0].text
# HeurChain embeds server-side — no embedder needed here.
requests.post(
"https://api.heurchain.com/store",
headers={
"X-HeurChain-API-Key": "<your-api-key>",
"X-HeurChain-Tenant": "YOUR_TENANT_ID",
"Content-Type": "application/json",
},
json={"text": text, "agent_id": "claude"},
)
Pip-install: pip install anthropic requests
import openai, requests
client = openai.OpenAI()
resp = client.chat.completions.create(
model="gpt-5",
messages=[{"role": "user", "content": "Your prompt"}],
)
text = resp.choices[0].message.content
# HeurChain embeds server-side.
requests.post(
"https://api.heurchain.com/store",
headers={
"X-HeurChain-API-Key": "<your-api-key>",
"X-HeurChain-Tenant": "YOUR_TENANT_ID",
"Content-Type": "application/json",
},
json={"text": text, "agent_id": "chatgpt"},
)
Pip-install: pip install openai requests
import os, openai, requests
client = openai.OpenAI(
base_url="https://api.moonshot.ai/v1",
api_key=os.environ["MOONSHOT_API_KEY"],
)
resp = client.chat.completions.create(
model="kimi-k2-instruct",
messages=[{"role": "user", "content": "Your prompt"}],
)
text = resp.choices[0].message.content
requests.post(
"https://api.heurchain.com/store",
headers={
"X-HeurChain-API-Key": "<your-api-key>",
"X-HeurChain-Tenant": "YOUR_TENANT_ID",
"Content-Type": "application/json",
},
json={"text": text, "agent_id": "kimi"},
)
Pip-install: pip install openai requests
// inside an OpenClaw agent step
import { Agent } from 'openclaw';
const agent = new Agent({ /* your config */ });
const result = await agent.run({ task: 'Your task' });
await fetch('https://api.heurchain.com/store', {
method: 'POST',
headers: {
'X-HeurChain-API-Key': '<your-api-key>',
'X-HeurChain-Tenant': 'YOUR_TENANT_ID',
'Content-Type': 'application/json',
},
body: JSON.stringify({
text: result.output,
agent_id: 'openclaw',
}),
});
Install: npm install openclaw
# ~/.hermes/config.yaml — declare HeurChain as the memory provider # Restart Hermes after editing: # pkill -f "hermes gateway" && nohup hermes gateway run & memory: provider: http url: https://api.heurchain.com/store headers: X-HeurChain-API-Key: <your-api-key> X-HeurChain-Tenant: YOUR_TENANT_ID body_template: text: "{{ .text }}" agent_id: hermes
Drop in ~/.hermes/config.yaml — restart the gateway
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