MemoryLake
Engineering & Developermemory for ReAct-style agent loops

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.

Day 1ReAct (Reason + Act) loops generate a thought, take an action,observe the result, and repeat.Got it, I will remember.Day 7 — new sessionSame task again — can you keep the context?× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loadedPer-iteration commit memoryCross-iteration retrievalGoal memory anchored across iterationsSESSION OUTPUTSame prompt, on-brand answerNo re-briefing required.

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

Per-iteration commit memory

Each Thought/Action/Observation triplet stored as typed memory.

MEMORYCross-iteration retrieval

Cross-iteration retrieval

Later steps retrieve specific prior thoughts or observations.

MEMORYGoal memory anchored across iterations

Goal memory anchored across iterations

Pinned original goal prevents drift.

Reflection memory across runs

Reflection memory across runs

Patterns from prior runs inform current planning.

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How it works for ReAct memory

  1. Connect — Wire MemoryLake into the ReAct loop's commit step.
  2. Structure — Each Thought/Action/Observation becomes typed memory.
  3. Reuse — Later iterations retrieve specific prior steps by relevance.

Before vs. after: ReAct loop memory

Without MemoryLakeWith MemoryLake
Iteration 20 sees iteration 1's observationParaphrasedRetrieved verbatim
Cross-run learningNoneReflection memory
Goal preservationDriftsPinned
Audit reasoning chainLimited logsMemory 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.

Related use cases

Frequently asked questions

Latency in tight ReAct loops?

Single-digit millisecond retrieval; negligible.

Storage cost for long ReAct traces?

Delta-encoded commits keep overhead low.

Self-host?

Yes — enterprise tier deploys in your VPC.