Give ReAct Agent Loops Memory of Every Thought, Action, and Observation
ReAct (Reason + Act) loops generate a thought, take an action, observe the result, and repeat. Each iteration usually flushes context that earlier iterations needed. MemoryLake gives ReAct agents persistent memory across iterations, sessions, and runs.
Give ReAct Agent Loops Memory of Every Thought, Action, and Observation
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The problem: ReAct iterations lose context between thought steps
By iteration 20 of a long ReAct trace, the original goal is paraphrased, early observations are summarized, and the agent is reasoning on a third-generation copy of its own findings. ReAct is powerful in theory and lossy in practice without persistent memory.
How MemoryLake supports ReAct loops
Per-iteration commit memory
Each Thought/Action/Observation triplet stored as typed memory.
Cross-iteration retrieval
Later steps retrieve specific prior thoughts or observations.
Goal memory anchored across iterations
Pinned original goal prevents drift.
Reflection memory across runs
Patterns from prior runs inform current planning.
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How it works for ReAct memory
- Connect — Wire MemoryLake into the ReAct loop's commit step.
- Structure — Each Thought/Action/Observation becomes typed memory.
- Reuse — Later iterations retrieve specific prior steps by relevance.
Before vs. after: ReAct loop memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Iteration 20 sees iteration 1's observation | Paraphrased | Retrieved verbatim |
| Cross-run learning | None | Reflection memory |
| Goal preservation | Drifts | Pinned |
| Audit reasoning chain | Limited logs | Memory provenance |
Who this is for
Teams running ReAct-style agents in production — research agents, browsing agents, coding agents — where loop quality degrades as iteration count grows.
Try this use case on your own account
Tarnwick's investigation agent runs a ReAct loop on a checkout latency incident: in five actions it rules out four suspects (a cold Redis cache, the payments-gateway connection pool, the fraud-scorer sidecar, DNS) and finds a lead, then runs out of steps. The next run asks memory what the investigation already established. Stored as a text scratchpad, MemoryLake's extractor kept the lead in every run but the rule-outs 0 of 32 times, and the resumed plan re-checked dead ends 26 times in 96 picks. One typed record per iteration, written with fact add, kept all of them and the plans never repeated a dead end; but asked about two suspects nobody checked, the model said they were already ruled out 29 of 47 times. One coverage record saying nothing else was ruled out brought that to 0 of 48.
What you will see
- Five ways to store the same loop: Thought as TEXT with
TOOL_USE/TOOL_RESULTblocks, the scratchpad as TEXT, the same with every verdict stated ("… so X is ruled out"), onefact addrecord per iteration, and the records plus a coverage record - The resumed run asks through
memorylake router chat --boundary: "pick the next 4 actions" and "has the investigation already ruled out X?" for four checked and two unchecked suspects, with a no-memory arm as the ∅ baseline - Over 8 runs: rule-outs kept in memory 0 of 32 (tool blocks), 0 of 32 (scratchpad), 28 of 32 (stated verdicts), 32 of 32 (records); a Yes with no verdict in memory is graded as a guess, not a memory
- A
memorysubcommand shows what each arm remembers about each suspect,ask queriesputs the reverse question to all arms, 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/ruled-out-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Latency in tight ReAct loops?
Latency in tight ReAct loops?
Single-digit millisecond retrieval; negligible.
Storage cost for long ReAct traces?
Storage cost for long ReAct traces?
Delta-encoded commits keep overhead low.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.