Give Coding Agents Memory That Survives Every Session Boundary
Every new coding session starts cold. The agent re-discovers your repo, re-asks about conventions, and re-makes decisions it already made yesterday. MemoryLake gives AI coding agents persistent memory across sessions, tools, and models — so what the agent learned about your codebase stays learned.
Give Coding Agents Memory That Survives Every Session Boundary
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The problem: AI coding agents forget everything between sessions
A coding agent spent two hours yesterday mapping your microservices and deciding how to refactor the auth layer. Today, new session, blank slate. It re-reads the same files, re-asks the same clarifications, and possibly proposes the approach you already rejected.
How MemoryLake solves persistent memory for AI coding agents
Repo-scoped persistent memory
Every agent action, decision, and rejected suggestion is stored as typed memory tied to the repository.
Cross-tool portability
The agent's memory works whether it's running in Cursor, Claude Code, Windsurf, a custom CLI, or a CI pipeline.
Reflection memory blocks repeat mistakes
When a suggestion gets rejected, the reason is captured. Future sessions stop proposing the same dead end.
Git-style memory branches
Memory follows feature branches. Speculative work doesn't pollute the main memory until you merge.
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How it works for AI coding agents
- Connect — Add MemoryLake to the agent via SDK or MCP.
- Structure — Every action, file read, and decision is classified into the right memory type.
- Reuse — On the next session, the agent loads relevant prior memory before its first reasoning step.
Before vs. after: coding agent persistent memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| New session begins | Agent re-explores the repo | Agent loads prior memory |
| Same refactor question | Asked again every time | Decision already in memory |
| Switching from Cursor to Claude Code | Lose context | Same memory, new tool |
| Six-hour autonomous run | Restart from zero on crash | Resume from last commit |
Who this is for
Engineering teams running AI coding agents for refactors, migrations, code reviews, or long-running automation — where the agent needs to remember decisions across sessions and the cost of starting over is real engineering time.
Try this use case on your own account
Saltmarrow Pay's coding agent runs on Claude Haiku 4.5 through MemoryRouter. Every request carries the same 12,100-token repository guide, marked for Anthropic prompt caching, and MemoryRouter attaches what the team decided in earlier sessions. The developer works a short session, steps away for five and a half minutes, and comes back. With the default 5-minute cache, the first request after the break wrote the whole guide again at the cache-write price (6 of 6 runs). With a 1-hour TTL, or one keep-alive call four minutes into the break, the guide was read from the cache (6 and 6 of 6). The memory was there either way: every setting answered from decisions written before the break. Over both sessions the default cost 0.42 times an uncached agent, the 1-hour TTL 0.37 and the keep-alive 0.29.
What you will see
- Yesterday's session notes go in with
fact add, one boundary covers them, and prices come frommemorylake router models(cache write for 5 minutes and for 1 hour, cache read) - Every request goes out four ways with
memorylake router messages --bodyand--boundary: nocache_control, the default 5-minute TTL,ttl: 1h, and 5 minutes plus a 1-token ping - The break is real, 5½ idle minutes; each reply's
ephemeral_5m_input_tokens,ephemeral_1h_input_tokensandcache_read_input_tokensshow which cache survived it - The bill splits both sessions and the break, with a computed table for other break lengths;
asksends one question with the team's memory; 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/cache-ttl-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Does this work with autonomous coding agents like Devin or SWE-agent?
Does this work with autonomous coding agents like Devin or SWE-agent?
Yes. Any agent that can call an MCP server or HTTP API can use MemoryLake as its memory backend.
How is repo-scoped memory isolated?
How is repo-scoped memory isolated?
Each repository has its own namespace. Per-branch scoping is available for speculative or feature-specific memory.
What gets logged in the audit trail?
What gets logged in the audit trail?
Every memory write, with author (agent or human), timestamp, and source. Critical for reviewing what an autonomous agent did overnight.