Solutions

We built this because we hit
the same wall you did

AI agents forget everything between sessions. Context windows run out. Knowledge lives in people’s heads. We spent months solving this problem so you don’t have to.

Problem 01

Claude Code keeps forgetting everything

Your agent loses context every session. You keep re-explaining the same codebase.

The problem

  • Every new Claude Code session starts blank — no memory of yesterday's decisions, no recall of architectural choices, no knowledge of why that function exists.
  • You waste the first 10 minutes of every session re-explaining your project structure. The agent re-reads the same files, re-discovers the same patterns, makes the same mistakes you already corrected.
  • Context windows are finite. Once you hit the limit, the conversation compacts and your agent forgets critical details mid-task.

How Rewind solves it

  • Rewind Memory gives Claude Code a persistent memory layer that survives across sessions. Every conversation, decision, and file interaction is automatically captured and indexed.
  • When your agent needs context, it searches a knowledge graph of your entire project history — not just the current conversation. It remembers that you chose PostgreSQL over MongoDB three weeks ago, and why.
  • The plugin hooks into Claude Code's lifecycle automatically. No manual note-taking, no copy-pasting context. It just works.

Context rebuild time

5-10 min/session

Instant

Repeated explanations

Every session

Never

Decision recall

Lost after compaction

Permanent

Get started — it's free →

Problem 02

Your best engineer's knowledge lives in their head

When they leave, the knowledge leaves with them. Every onboarding is starting from zero.

The problem

  • Critical decisions are made in Slack threads that nobody will ever find again. The reasoning behind your architecture lives in someone's memory, not in your systems.
  • New team members spend weeks piecing together how things work from scattered READMEs, outdated wikis, and asking the same questions the last new hire asked.
  • When your senior engineer is on holiday, the team is blocked on questions only they can answer.

How Rewind solves it

  • Rewind captures the reasoning behind decisions as a knowledge graph — not just what was decided, but who decided it, when, and why. Entities, relationships, and context are extracted automatically.
  • New engineers query the knowledge graph naturally: "Why do we use Redis for sessions instead of the database?" and get an answer with the original context, not a stale wiki page.
  • The Pro tier adds cloud-powered NV-Embed-v2 embeddings (4096 dimensions) for semantic search that actually understands what you're asking, not just keyword matching.

Onboarding time

2-4 weeks

2-3 days

Knowledge bus factor

1 person

The graph

Search accuracy

Keyword grep

Semantic (4096-dim)

See how it works →

Problem 03

Nobody knows why this code exists

The git blame says "refactor" and the commit is 8 months old. Good luck.

The problem

  • You're staring at a function that does something non-obvious. The commit message is "fix bug". The PR was merged without review. The author left the company.
  • You need to change a critical system but you don't know what depends on it. The tests pass, but you've learned that passing tests don't mean the change is safe.
  • Every codebase accumulates decisions that made sense at the time but are invisible to anyone who wasn't there. Without the context, you either break things or never touch them.

How Rewind solves it

  • Rewind's knowledge graph connects code changes to the conversations and decisions that drove them. Not just "what changed" but "why it changed" and "what was considered and rejected".
  • The graph stores entity relationships: this function → created by → this person → during → this project → because → this technical constraint. Query it naturally.
  • Seven layers of memory work together: full-text search (L0) for exact matches, vector search (L1/L4) for semantic similarity, the knowledge graph (L3) for relationship traversal, and communications and document layers (L5/L6) for deep recall.

Code archaeology

Hours of git log

One query

Decision context

Lost

Graph-linked

Refactoring confidence

Hope-driven

Evidence-based

Start building your knowledge graph →

Problem 04

Your AI agents have amnesia

Every agent framework promises memory. None of them actually deliver persistent, structured recall.

The problem

  • You've tried RAG. You've tried vector databases. You've tried stuffing context into system prompts. None of it actually works like memory — it's just search with extra steps.
  • Your agent can find similar documents but can't answer "what did we decide about the authentication approach last Tuesday?" because that requires understanding relationships, not just similarity.
  • Building a memory system from scratch means months of engineering: embeddings, graph databases, search pipelines, reranking, caching. You wanted to build a product, not a memory stack.

How Rewind solves it

  • Rewind is a production-ready, bio-inspired memory architecture. Not a wrapper around a vector database — a genuine multi-layer system modelled on how biological memory actually works.
  • Sensory buffer → short-term memory → knowledge graph → workspace memory. Each layer serves a different function, just like your brain. Full-text for exact recall, vectors for semantic similarity, graphs for relationships.
  • One pip install, one plugin command. Your agent has persistent memory in under 60 seconds. Free tier runs entirely offline — no API keys, no cloud, no dependencies.

Setup time

Weeks of engineering

60 seconds

Memory layers

1 (vector search)

5-7 (bio-inspired)

Relationship queries

Impossible

Native (knowledge graph)

pip install rewind-memory →

Ready to stop re-explaining your codebase?

Free tier runs entirely offline. No API keys, no cloud, no credit card. Just persistent memory for your AI agent.

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