Give Your AI Agents Context That Survives Between Runs
An AI agent that resets on every run isn't an agent — it's a stateless function. MemoryLake provides the persistent context layer that lets agents accumulate knowledge, reuse workflows, and coordinate across instances without rebuilding state from scratch each time.
Give Your AI Agents Context That Survives Between Runs
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The Memory Problem
Most agent frameworks provide excellent tools for planning, tool use, and execution — but zero persistence between runs. Every time an agent starts, it has no memory of what it did last time, what worked, or what the user told it. Developers work around this with hacky state files, long context injections, or manual logging — all of which break at scale.
What MemoryLake Does Differently
Background Memory keeps agent identity consistent — Agents can store read-only identity context — their role, constraints, user relationships, and operating parameters — so every run begins with the right foundational context without re-injecting it manually.
Skill Memory propagates reusable workflows — When an agent solves a new class of problem, that workflow can be stored as a Skill and reused in future runs or shared across agent instances. Agents get smarter over time instead of starting from zero.
Multi-agent shared memory via a single store — Agent fleets operating on the same task can read from and write to a shared MemoryLake store. One agent's findings are immediately available to all others. No custom messaging layer needed.
Give Your AI Agents Context That Survives Between Runs
Get Started FreeFree forever · No credit card required
How It Works
- Connect — Register MemoryLake as an MCP server in your agent framework, or call the REST API directly. Compatible with AutoGPT, OpenClaw, Manus, and any agent that supports MCP or HTTP.
- Structure — During each run, the agent writes relevant outputs to typed memory categories: decisions to Event Memory, learned facts to Fact Memory, successful approaches to Skill Memory.
- Reuse — At the start of the next run, the agent retrieves relevant memory by category. Context is populated in milliseconds; the agent continues where it left off.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Agent startup | Cold start every run, rebuild context manually | Background + Conversation memory loaded at session start |
| Learned workflows | Rediscovered or hardcoded in prompts | Stored as Skill Memory, reused across runs automatically |
| Multi-agent coordination | Requires custom state-sharing infrastructure | Shared MemoryLake store; any agent reads any other's output |
| Error patterns | Repeated across runs | Reflection Memory stores behavioral patterns, prevents recurrence |
| Audit trail | Manual logging or none | Every memory write is versioned with source and timestamp |
Built For
MemoryLake is built for developers and ML engineers running production agent systems — scheduled research agents, coding agents, data pipeline agents, and customer-facing AI assistants that need to operate reliably across sessions. It works with any agent framework that supports MCP or HTTP calls.
Related use cases
Frequently asked questions
Which agent frameworks does MemoryLake support?
Which agent frameworks does MemoryLake support?
MemoryLake works with any agent framework that supports MCP (Model Context Protocol) or standard HTTP. This includes AutoGPT, OpenClaw, Manus, and custom agents built on Claude, ChatGPT, Gemini, or any API-accessible model.
Can multiple agents share the same memory store?
Can multiple agents share the same memory store?
Yes. MemoryLake supports shared memory stores across agent fleets. Any agent with appropriate access can read from and write to the shared store, with role-based access control determining what each agent can modify.
How do agents know what memory to retrieve?
How do agents know what memory to retrieve?
Agents query MemoryLake by memory type and semantic relevance. MemoryLake's retrieval layer returns the most relevant memory items for the current task context, ranked by relevance — not just recency.