Permanent memory for sales AI agents
Context compaction wipes your agent’s session — customer names, commitments, deal amounts, follow-up dates. AfterContext is the external memory that survives every compaction: record as it happens, recall with one call.
Claude Code · OpenClaw · Hermes · any MCP or HTTP agent
1 · During the session, the agent records
POST https://api.aftercontext.ai Authorization: Bearer ak_live_•••• {"action": "log.add", "data": { "type": "visit", "company": "Meridian Glassworks", "contact": "Elena Ortiz", "text": "Met buyer Elena Ortiz. Needs 300 custom panels by October. Budget approved, wants formal quote by June 20." }} # → 200 OK { "ok": true, "result": { "id": 463 }, "hint": "Visit #463 logged. Created new company #126 \"Meridian Glassworks\" and new contact #256 \"Elena Ortiz\" — enrich them with company.update / contact.update when you learn more." }
2 · After compaction, one call restores it
{"action": "company.get", "data": {"name": "Meridian Glass"}}
# → 200 OK
{
"ok": true,
"result": {
"id": 126,
"name": "Meridian Glassworks",
"contacts": [{ "id": 256, "name": "Elena Ortiz", "title": "Purchasing Manager" }],
"open_tasks": [{ "id": 31, "title": "Send formal quote to Elena Ortiz", "due": "2026-06-20" }],
"recent_activity": [{ "type": "visit", "when": "2026-06-11", "body": "Met buyer Elena Ortiz. Needs 300 custom panels by October. Budget approved, wants formal quote by June 20." }]
},
"hint": "\"Meridian Glass\" matched company #126 \"Meridian Glassworks\"."
}The compaction problem
Calls, visits, negotiations. Every turn adds tokens the agent can’t keep forever.
The window is summarized or truncated. A paragraph of nuance becomes one vague sentence.
Who approved the budget? Which date? What amount? The agent no longer knows — and won’t tell you.
Logged to AfterContext the moment it happened, every fact survives. One call re-grounds the agent.
Built for forgetful callers
Overdue tasks, open pipeline, recent activity — one call re-grounds a fresh session in seconds.
An agent that half-remembers “that Crescent company” still finds it. Names don’t need to be exact.
A forgetful agent can’t create the same contact twice — near-matches are caught and merged at write time.
One self-describing endpoint
A single endpoint returns its own instructions as a prompt — always current, because they’re served fresh on every connection. No SDK, no installation, no stale docs.
Failures come back with a fix field showing the corrected call. The agent recovers without a human in the loop.
Terse follow-up instructions ride along with results, nudging the agent to enrich records as it learns more.
{"action": "deal.add", "data": {"company_id": 126, "title": "300 custom panels", "amount": "45k"}}
# → 422
{
"ok": false,
"error": "bad_field",
"fix": "data.amount must be a number in currency units, e.g. 45000 or 45000.50 (write 45000, not \"45k\")."
}Token-friendly by design
Lists return one lean row per record. Full detail only when the agent asks for it.
Empty fields never make the wire. Responses carry exactly what exists, nothing else.
Hints are one sentence, not a tutorial. Useful to a model, cheap in tokens.
MCP native
For the humans
A dashboard for owners: pipeline and tasks at a glance, a full agent activity feed with audit trail, and API key management with per-key monthly caps.
Pricing
An API key and a monthly allowance, no card required.
Flat monthly plans with a fixed request allowance you can see and predict.
Buy extra requests when you need them. They never expire.
No metered overage. An agent can never run up a charge you didn’t approve.
Get a key, point your agent at one endpoint, and the API explains the rest.