Give Autonomous Agents a Memory Store Built for Long-Running Work
Autonomous agents that run for hours or days need a memory store that survives restarts, hand-offs, and model swaps. MemoryLake gives autonomous agents structured, versioned memory with millisecond retrieval — so a six-hour research run never starts over from step one.
Give Autonomous Agents a Memory Store Built for Long-Running Work
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The problem: autonomous agents have nowhere durable to keep state
In-process variables die when the agent crashes. Vector stores blur the difference between facts and observations. Custom databases require a schema for every new tool the agent learns. The result: autonomous agents that look impressive in a demo and collapse in production.
How MemoryLake solves the memory store problem for autonomous agents
Six memory types tuned for agent state
Facts the agent verified, events it observed, reflections it generated, skills it learned. Each gets its own typed store with its own retrieval logic.
Conflict resolution between tool outputs
When two tools return contradicting data, MemoryLake flags it and applies your resolution strategy. The agent stops trusting stale facts.
Git-style branches for exploratory runs
Spin up a memory branch for a speculative plan. Merge it if the plan worked. Roll it back if it didn't.
Provenance trail per memory
Every fact links to the tool, document, or conversation that produced it. Critical for debugging and compliance.
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How it works for autonomous agents
- Connect — Wire MemoryLake into the agent's planning and tool-use loop via SDK or MCP.
- Structure — Each tool result and reflection gets classified, deduped, and stored.
- Reuse — Before the next planning step, the agent retrieves the most relevant prior facts, events, and skills.
Before vs. after: autonomous agent memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Six-hour research run crashes at hour five | Restart from scratch | Resume from last commit |
| Two tools return contradicting facts | Agent gets confused | Conflict surfaced and resolved |
| Reusing a learned skill across runs | Copy-paste prompts | Skill memory called directly |
| Auditing "why did the agent decide X?" | No record | Full provenance chain |
Who this is for
Teams building autonomous agents for research, operations, browsing, coding, or trading — where the agent runs unattended for long stretches and the cost of restarting from zero is real money.
Try this use case on your own account
Halvorsen Analytics' autonomous backfill agent writes every step into MemoryLake as it goes, so a crash does not send it back to step one. Mid-run, the data engineer changes three settings (target schema, batch size, alert channel). For each change the demo restarts the agent at the exact moment a rule calls the step committed, and asks the restarted agent, which has nothing but its memory, what the setting is now. Over 15 changes, committing once the append returned brought back the new value 0 times (13 times the old one); committing once extraction had read the step, twice; committing on the per-batch write receipt from `conv fact-actions --message`, 15 of 15. Read and written were 3 to 8 seconds apart.
What you will see
- Three past sessions go in as conversations; the demo checks every agreed setting in memory first, then runs a 16-step night that appends each step with
conv msg append - A watcher per change polls
conv consumed-messages --message(read) andconv fact-actions --message(written) and restarts the agent at each moment withmemorylake router chat --boundary - The commit log shows every extraction batch, what it wrote, and why batches of progress chatter never get a receipt;
cook-statusstays false while steps keep arriving - An unchanged setting is asked at every restart as a control;
logandask "<question>"show the memory afterwards, and there is a local web UI
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/commit-point-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
How is this different from saving state to a database?
How is this different from saving state to a database?
A database stores rows. MemoryLake stores typed memory with conflict detection, versioning, and retrieval ranking. You'd otherwise build all of that yourself on top of Postgres.
Can agents share memory with each other?
Can agents share memory with each other?
Yes. Multiple agents can read from and write to the same memory namespace with fine-grained access control over which memory types each agent can see.
What's the retrieval latency?
What's the retrieval latency?
Millisecond-range for typical retrieval calls. Tested on production workloads with 100M+ memory items.