From your conversations with AI, agent work, external sources, and more, structure and accumulate your and your team's decisions, state, and relationships — organized and kept current automatically.
The memory platform for the age of AI agents, turning them into knowledge assets the next agent can use right away.
We'll send your invite once it's ready. If requests are high, there may be a wait — thank you for your patience.
Struggling with AI memory?
If you use Claude, ChatGPT, Cursor, or Gemini every day, you keep hitting three walls.
Even as ChatGPT and Claude add memory, it stays inside each tool. Switch tools or projects and you often re-explain your structure and past decisions all over again.
Do I really have to re-explain to Cursor what I decided in Claude?
The design decisions and research you built up with AI live only in your own chat history. Teammates can't see them, and if you're away, that knowledge disappears entirely.
Only they know that spec — guess I'm researching it again.
Pasting your README, specs, and past threads every time inflates the token count and your bill — and you still can't tell how much got through.
I pasted so much context my API bill doubled.
SOLVED WITH Kagura AI
The more you use it, the more Kagura AI organizes automatically — growing into a knowledge base your whole team can use.
Capture your AI conversations, agent activity, files, and external sources via MCP, REST API, or auto-sync. You never have to decide what to keep.
It merges duplicates, links related memories, and fixes stale or conflicting information. The more you use it, the more accurate your knowledge base becomes.
Share the same memory across multiple AIs like Claude, Cursor, Codex, and Gemini. With MCP, CLI, and REST API support, continue with the same context from any tool.
One person's insight becomes the whole team's asset. Configure which memories are shared and who can see them, by role.
FOR WHOM
Whatever your AI experience level, Kagura AI helps in situations like these.
If you write code with Claude Code or Cursor: stop re-describing your project on every new chat, and stop re-teaching last week's bug fix to the AI.
Your team uses ChatGPT or Claude, but no one can see who decided what. No more digging through Slack to find how a bug was handled — the whole team uses AI well.
If you're unsure what to tell the AI or never feel like you're using it well: context accumulates automatically from past conversations, so you stop re-explaining the basics.
Want to roll out ChatGPT but worried about leaks, or teams using it in silos? See who uses what, and separate accessible information by department.
Keep client A's and B's requirements from blurring together. Separate memory per client and reuse past proposals and design decisions on the next job.
Want to pass what agent A found to agent B, or share one context across agents? Embed Kagura AI as a shared memory all your agents can reference.
FEATURE LIST
A combination of Cloud, which saves and retrieves memory, and Worker, which organizes and grows it automatically.
A cloud home for your AI's memory
Just send work notes and decisions from AI tools like Claude, Cursor, or ChatGPT — they're organized and stored automatically. Connect via MCP, CLI, or REST API, ready to recall from the next session or another tool.
Hand over a conversation or note and it decides what matters, then organizes and stores it. No need to think about what or how to save.
Pull up related memories from vague phrases like 'that thing last week' or 'the login design decision'. It searches by meaning, even when keywords don't match exactly.
Automatically links the relationships between saved memories. It records context like 'this bug came from that design decision', so you can pull related info together.
Keep memory separated by project or client. No more mixing up client A with client B — hand the AI only the memory that's relevant.
Access the same memory from different AI tools like Claude, Cursor, Gemini, and Codex. Connect not only via MCP but also CLI and REST API — the same context works from any tool, any method.
Set who can see which memory. For team use, control things like 'this memory is mine only' or 'this project is open to the whole team'. Operation history is kept too.
Organizes and grows your memory automatically
Left alone, memory fills up with stale info and duplicates. Worker reviews it on a schedule in the background and keeps it in a usable state.
Periodically checks all memory and merges or removes entries that are stale, duplicated, or contradictory. No manual upkeep required.
Analyzes accumulated memory and reports trends like recurring mistakes or topics the team often debates. Use it as a hint for retrospectives and improvement.
When handing memory to the AI, instead of sending everything it auto-selects only what relates to the current task and keeps it compact. Less noise means better answers and lower cost.
Automatically converts what you built up with AI into a form the whole team can use. No more 'only they know it', and work is less likely to stall when someone's away.
Automatically records links to the sources behind a memory, like GitHub Issues and docs. You can verify the rationale later, and the risk of the AI producing wrong info drops.
As memory grows, it steps back and re-evaluates 'memories whose importance changed' and 'topics with more connections'. It maintains quality periodically so search accuracy holds even as volume grows.
COST BENEFITS
All figures below are simulations. Actual savings vary by project size, usage, and the AI model you use. Please treat them as pre-adoption reference only.
Sends only relevant memory instead of full context, cutting tokens by ~72% while keeping your AI precisely informed.
Assumptions & method
Accuracy drops as context grows. Delivering only relevant memory avoids the degradation confirmed across the latest generation of models.
Assumptions & method
The more you log decisions and work, the less you re-explain. Personal memory compounds into a shared team asset over time.
Assumptions & method
| Axis | Without Memory Layer (full send) | Kagura AI (Compile Layer) | Difference (simulated) |
|---|---|---|---|
| Tokens per call | ~25,000+ | ~7,000 or fewer | ~72% less |
| Monthly API (1 person, 100/day) | ~$162/mo | ~$63/mo | ~$99 saved |
| Cost at 3× scale | ~3× increase | Growth suppressed | Lower growth rate |
| New session startup | Explain from scratch | Starts with memory | No explanation cost |
All of the above are Kagura AI's own simulations and do not guarantee actual results. While modern AI context windows have expanded to 200K–1M tokens or more, academic research consistently shows that feeding long context directly into models reduces accuracy. NoLiMa (ICML 2025) found that at 32K tokens, many models fall below 50% of their short-context baseline, and long-context accuracy degradation has also been reported for later-generation large models. Monthly cost estimates use a representative input price of $3 / 1M tokens; actual rates vary by model. Actual savings vary widely by project size, call frequency, and the nature of the context. We recommend validating in your own environment before adoption.
Primary source: Modarressi et al. (2025), 'NoLiMa: Long-Context Evaluation Beyond Literal Matching' (arXiv:2502.05167 / ICML 2025) / token rates per AI model providers' published pricing
PRICING
Begin with the invite-only trial. Scale to Starter or Pro as your team grows. Custom and large-scale needs are covered by Enterprise.
Available to invited users only
Request an inviteWe'll send your invite once it's ready. If requests are high, there may be a wait — thank you for your patience.
Monthly subscription · cancel anytime
Monthly subscription · cancel anytime
Tailored to large-scale & custom needs
Contact usFAQ
Stuck? Answers to your questions.
Kagura AI is a memory platform for AI agents. It's a shared place where AIs like Claude, ChatGPT, Cursor, and Gemini remember your team's work logs, decisions, and state — storing and organizing memory so any tool can recall the same context and pick up where you left off.
Yes. Even though it's a memory platform, the basics work like chatting: tell Claude or ChatGPT 'remember this' to save, and ask 'what did we decide about that?' to recall. MCP and API connections are available too, but we recommend starting with the trial plan from the screen first.
ChatGPT's memory lives only inside ChatGPT. Kagura AI is an independent memory platform that doesn't belong to any one tool, so multiple AI agents — Claude, Cursor, Gemini, Codex — share the same memory. It also adds team sharing, per-project separation, and reference history.
RAG retrieves documents from a large set and hands them to the AI. As a memory platform, Kagura AI structures not just documents but the state and history of when, who, and how something was decided. It can complement RAG.
Data on the platform is private to you by default. Only memory you set to team-shared becomes accessible to permitted members. For enterprise use, you can self-host the platform on your own servers.
Yes. We currently offer an invite-only trial plan for free — up to 1,000 memories and 1 workspace — to try the platform's core features. Just reach out via 'Request an invite'.
/compact summarizes the current conversation into a file. Kagura AI further structures that summary and stores it on the platform as memory you can search and reuse from future sessions and other AI tools.
It depends on the platform pricing plan. Starter: 2 workspaces, up to 5 trial invites. Pro: 5 workspaces, up to 20 trial invites. For larger scale or company-wide rollout, talk to us about Enterprise. Pricing is being finalized and will be published on this page.
Yes. A Source Ref feature links memory to GitHub Issues and PRs, and other tools like Slack connect via API/MCP. With the platform at the center, context scattered across tools is consolidated in one place.
Reach out to contact@kagura-ai.com via 'Request an invite'. If you want to try it solo, we'll guide you to the trial plan. If you're considering team or enterprise adoption, contact us via the 'Business inquiries' form.