Product

How Graphe stores
agent memory.

Not a chat export. An append-only event log, derived facts with provenance, temporal claims, and queryable current state—built for multiple agents on the same project.

Data model

Four layers,
one traceable pipeline.

Graphe never treats raw chat as the system of record. Agents write events; workers derive observations and claims; retrieval serves current state with provenance.

01

Events

append-only

A permanent log of what your agents did—tool calls, state changes, what context they used, and when things went wrong. Scoped to your team and project.

02

Observations

derived

Facts extracted asynchronously from events—what happened, with provenance links back to source event IDs. Not rewritten when new events arrive.

03

Claims

temporal

Revisable beliefs about project state (confirmed, superseded, invalidated). Each claim carries lineage so agents and humans can see what evidence supports it.

04

Current state

query view

The latest confirmed picture for agents to read before acting—not a chat transcript, not a single vector dump. Built from claims + graph + hybrid search.

Platform

What you get

Activity capture

Your agents send what they did—tool calls, errors, context used—into one reliable stream. Safe to retry; nothing silently dropped.

Search & answers

Ask what happened on a run, find similar past failures, and get explanations grounded in your project's actual history.

Web console

Browse runs, see what your agents believe about the project right now, and trace any claim back to the evidence.

Automatic processing

Graphe turns raw activity into summaries, issues, and shared memory in the background—you don't manage pipelines.

Connect your agents

From Python, TypeScript, Cursor, Claude, or your own setup. Technical setup guides live in Documentation.

Workflows

Built for how agents actually fail

01

Multi-agent shared memory

Problem

Two agents overwrite each other's assumptions in the same repo.

How Graphe helps

Both write scoped events to the same project. Claims merge into one current-state view; supersession handles conflicts with provenance.

02

Postmortem after a failed run

Problem

You need to know what context the agent had when it broke production.

How Graphe helps

Run timeline + retrieval snapshots on events + explain_run surfaces the failure chain without replaying the whole chat.

03

Stale context before the next action

Problem

Agent acts on outdated file or claim state.

How Graphe helps

Hybrid search + current-state query returns confirmed claims and recent observations ranked by recency and relevance.

Ready to wire it up?

Sign up, connect your agents from the console setup guide, and ship your first instrumented run.