Give Plan-and-Execute Agents Memory That Keeps the Plan Intact Through Execution
Plan-and-execute architecture generates a plan and then executes each step. Without persistent memory, the plan paraphrases as execution proceeds and the original intent gets diluted. MemoryLake pins the plan and tracks execution state in typed memory.
Give Plan-and-Execute Agents Memory That Keeps the Plan Intact Through Execution
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The problem: plan-and-execute loses plan fidelity during execution
The agent generated a clean 7-step plan. By step 5, the plan summary has been compressed twice and step 6's executor only sees a paraphrase of the original. Cross-step dependencies break because nothing canonical anchors the plan.
How MemoryLake preserves plan fidelity
Plan memory pinned at the top
Original plan stored verbatim as canonical memory.
Per-step execution event memory
Each step's outcome committed with full context.
Cross-step dependency tracking
Step 6 retrieves what step 4 produced — not a summary.
Replan with version history
When the agent legitimately replans, the change is committed with audit.
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How it works for plan-and-execute memory
- Connect — Write the plan to typed Goal memory at start.
- Structure — Each step writes execution event memory.
- Reuse — Each step retrieves the pinned plan plus relevant prior steps.
Before vs. after: plan-and-execute memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Plan fidelity at step 7 | Compressed | Verbatim |
| Cross-step dependency | Lossy | Direct retrieval |
| Replan audit | None | Versioned |
| Resume after crash | Restart | Resume from last step |
Who this is for
Teams running LangGraph plan-and-execute, BabyAGI-style planners, or custom multi-step agent architectures where plan fidelity through execution determines output quality.
Try this use case on your own account
A plan-and-execute billing agent crashes after step 6 of 8 and, before resuming, asks memory step by step whether each step is already done. Steps 4 and 5 (charge 412 saved cards, email the receipts) happened as tool calls: the results are stored in the run log, but tool results are not extracted, so memory holds nothing about them. To a yes/no question the model reads that silence as "No, not done yet", and the resume would charge the cards a second time. A run journal the app writes, closed by one fact saying it is complete, makes the silence mean something.
What you will see
- The same run log (status messages plus
TOOL_USE/TOOL_RESULTblocks) is stored twice; one copy also gets a run journal the app writes withfact add: one entry per step outcome and one fact that a step with no entry has not completed - Over 8 runs, asked "has step N already been done?" through
router chat --boundary, the resume check answered "No" to 46 of 46 questions about steps 4 and 5: every resume would have charged 412 cards again (86,940 dollars) and resent the receipts. Across all finished steps memory said nothing about: "No" 66 of 72 - Allowing "No record" ("answer No only if memory says so") never re-ran a step, but it cannot tell a step that ran from one never reached: 116 "No record" answers of 191, including 12 of 24 on the failed step memory does record
- With the run journal: right 192 of 192.
conv msg listshows the tool results are stored verbatim; plusmemoryandask <step>subcommands and 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/silent-step-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Related use cases
Frequently asked questions
Multi-level plans (plan-of-plans)?
Multi-level plans (plan-of-plans)?
Supported — hierarchical goal memory.
Replan strategy?
Replan strategy?
Configurable — full replan, partial replan, or step retry, all with audit.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.