forefetch-client

The thin Python client for forefetch — an anticipatory memory service for multi-agent pipelines. The server predicts what each agent will need next and ships that content during the previous agent's think-time; this client holds it, so an anticipated retrieval costs one tiny round trip.

One dependency (httpx). No engine code, no models, no vector store on your side.

from forefetch_client import MemoryClient

memory = MemoryClient("https://api.forefetch.com", api_key="yourkey", stream="resolver")

memory.remember(["refund policy: duplicate charges are refunded in full within 5 days"])

r = memory.retrieve("how to resolve: duplicate charge", k=4)
# r["wire_frac"] == 1.0  ->  every returned row was already here before you asked

memory.observe("resolver: refunded the duplicate charge")   # fire-and-forget event record
memory.suggest(k=3)        # labeled proactive context, zero round trips
memory.end_session()       # close the task; the next session starts pre-warmed

memory.report()            # the scorecard: readiness, wire economics, learned handoffs

• One stream per agent. Streams are hard-isolated; only the anticipation layer moves labeled hints between them.
• observe() returns instantly — a background worker ships events in order and banks each response's content push. Call flush() before another agent must see your effects.
• Every suggestion carries provenance (kind, and from_stream on handoffs): context your agent can attribute, or ignore.

Access (hosted keys or the self-host image): yashvardhan090202@gmail.com