Agents need more than memory.
They need continuity.
Memomee is the memory governance and retrieval control plane for long-running AI agents. Recall broadly, govern rigorously, inject precisely — task state, decisions, and evolving facts kept compact, trustworthy, and resumable across sessions.
Most agents don't fail loudly.
They fail by losing the plot.
An agent starts strong. Then a workflow branches. A session ends. A fact changes. A summary compresses away a key decision. Now the agent is still running — but no longer grounded.
Summary drift
Rolling summaries quietly compress away the decision that actually mattered.
Retrieval mismatch
Similarity search finds something relevant — to a different problem.
Stale facts
Yesterday's truth keeps shaping today's decisions, silently.
Workflow loss
An interruption ends a session and the agent has to rediscover everything.
Context fragmentation
State splits across threads, tools and providers; teams glue it back by hand.
The long version: why memory fails — five chapters on how agents lose the plot.
Resume exactly where the work left off.
Capture the live state of a task — stage, goals, parameters, the last successful step — so an interruption doesn't force a restart.
- — Immutable revisions; every checkpoint kept for replay
- — Opaque resume tokens map back without leaking state
- — Snapshot ids let external systems reference a moment in time
# resume tokens are opaque, not state client.checkpoints.update( task_id="task_8f3a", workflow_stage="verify", active_goals=["verify auth flow", "fix regression"], last_successful_step="run_unit_tests", resume_token="tok_abc123", ) # → snapshot_id: snap_20260509_018
The vault in depth: the event ledger.
client.events.write( task_id="task_8f3a", actor="coding-agent", event_type="decision", payload_summary="branched to fix-regression sub-task", source_metadata={ "model": "claude-4", "tool": "test_runner", }, )
Update what's true without silently breaking trust.
A write-once event ledger holds what actually happened — actor, type, payload, source. Facts evolve through explicit supersession, not overwrite. Every version keeps its provenance and link — so retrieval can show what changed and why.
- — Events are immutable; trigger-enforced append-only
- — active / superseded / retracted as first-class status
- — Lineage chains render the full history of any (entity, relation)
- — Confidence and source travel with every fact
The engine in depth: the governance model.
See what was surfaced — and what wasn't.
Every retrieval carries a trace: returned vs filtered counts, ranking rationale, recency and source signals, contradictions. Consolidation merges repetitive events without losing the evidence — every output keeps a reference back to the raw events.
- — Bundle separates checkpoint, events, facts, warnings, trace
- — Idempotent consolidation — same window in, same output out
- — Contradictions surfaced as warnings, not silently merged
- — Lineage links from compacted summaries back to event_ids
{ "events_compacted": 12, "facts_promoted": 3, "contradictions_found": 1, "warnings": [{ "entity": "project_alpha", "relation": "budget_approved", "fact_ids": ["f-29", "f-44"], "resolution_status": "open", }], }
Reflection, measured: the evidence tiers.
Four steps. One governance layer.
Capture
Agents write events and checkpoints as they work. Every action is append-only.
Consolidate
Rules-based compaction merges repetitive events and flags contradictions.
Retrieve
Bundle returns checkpoint, filtered events, active facts, superseded summary, and trace.
Explain
Trace shows what was returned, what was filtered, and why — with contradiction warnings.
The four steps above are the short version — the full six-stage pipeline, box by box: how it works.
Built for agents that run longer than a session.
Resume a refactor across sessions — repo state, decisions, and open threads survive a restart.
Carry findings, sources, and contradictions across a long investigation without re-deriving them.
Long-running approvals and hand-offs that stay resumable and auditable across days.
Per-customer memory with governance — isolation, supersession, and a full audit trail.
Pass task state between agents without losing the plot or duplicating work.
Problem → architecture → what changes: the use cases, in depth.
Recall broadly. Govern rigorously.
Inject precisely.
Other products help agents store more context. Memomee holds recall@k parity and leads open-source RAG and mem0 at a matched regime — at a fraction of the tokens — then governs what should survive, what should change, what should be forgotten, and what should shape the next decision.
Built for memory you can defend in a review.
The enterprise picture: what the system enforces — and what we don't hold yet.
Start governing it.
Your agents shouldn't have to rediscover the world every time a workflow branches or a session restarts. Memomee is invite-first while we finish the managed product — join the waitlist for early access.
Or email hello@memomee.ai · see the head-to-head