Run Concurrent Agents on Shared Memory Without Race Conditions
When multiple agent processes write to the same memory store, race conditions surface fast. One agent's write overwrites another's. Stale reads return outdated facts. MemoryLake handles concurrency with built-in conflict resolution — so concurrent agents share memory safely.
Run Concurrent Agents on Shared Memory Without Race Conditions
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The problem: DIY agent memory breaks under concurrency
A team builds memory on top of Postgres or Redis. It works in development. In production, two agents writing to the same user's memory race and one wins silently. Stale reads return outdated facts. Debugging concurrency bugs in custom memory plumbing is brutal.
How MemoryLake handles concurrent access
Built-in conflict detection
Concurrent writes that contradict surface for resolution.
Atomic commits
Each memory write is atomic with version metadata.
Per-agent author tracking
Every write records which agent produced it.
Configurable resolution strategies
Latest-source, role-priority, manual review.
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How it works for concurrent agent memory
- Connect — Each agent authenticates with a scoped key.
- Structure — Writes commit atomically with conflict detection.
- Reuse — Reads return the resolved canonical state.
Before vs. after: concurrent agent memory access
| DIY memory | MemoryLake | |
|---|---|---|
| Concurrent write safety | Race conditions | Atomic with conflict detection |
| Stale read protection | Manual locking | Built in |
| Per-agent attribution | Often missing | Built in |
| Concurrency bug debugging | Painful | Memory provenance |
Who this is for
Engineering teams running multi-agent or multi-process AI deployments where shared memory access is the path forward but concurrency bugs are stalling production.
Try this use case on your own account
Six onboarding agents give six new hires a seat from the same pool and record it in shared memory at the same instant — three ways. Read-modify-write on one counter fact: every fact update succeeds, the counter ends at 13 instead of 18, and no conflict is raised, because six writes of "13 of 20" agree. One record per assignment, with the seat as subject: every write survives, then conflict detection reads it as one seat assigned to six people, and resolving it the latest-source way forgets five real assignments. One record per assignment, with the person as subject, counted by the app: 18, nothing flagged.
What you will see
- Eighteen agent jobs (six per project) released together:
fact get→ +1 →fact updateon a counter, or onefact addper assignment - Over 8 runs the counter read 13 every time: 5 of 6 updates overwrote another agent's, with no error and no conflict;
fact traceshows each overwrite afterwards - "Tamsor seat assigned to …" records were flagged as a contradiction 8 of 8 times;
keep_facton that conflict forgot 5 real assignments each time. "<Name> holds one of the 20 seats" records: 18 every time, 0 conflicts trace,conflictsandcountsubcommands, a local web UI, and no model calls
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/concurrent-write-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Throughput at scale?
Throughput at scale?
Tested on high-concurrency workloads — thousands of writes/second per namespace.
Distributed transaction support?
Distributed transaction support?
Multi-write transactions supported at enterprise tier.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.