How to Search Kimi Conversation History (Moonshot AI)

Kimi is Moonshot AI's flagship AI assistant, notable for its exceptional long-context capability. Users working with large documents, extended research, or multi-step analysis often find Kimi handles context length that other platforms can't. This creates a distinct history retrieval challenge: individual conversations can be very long, making finding specific content within them harder than on shorter-context platforms.

How Kimi stores conversation history

Kimi saves conversations to your Moonshot AI account:

• Account-linked: Sign-in required for history to persist
• Sidebar navigation: Conversations listed in the left sidebar in reverse chronological order
• Auto-generated titles: Titles derived from the first message or document uploaded
• No search or organisation tools: Flat chronological list

Users often interact with Kimi for extended sessions — uploading documents, working through multi-step analysis, or referencing large codebases. This means conversations can extend over many thousands of words, making scrolling-based retrieval even less effective than on platforms with shorter exchanges.

Method 1: Scroll the sidebar

The direct approach for recent conversations:

1. Sign in to kimi.ai
2. Browse the left sidebar for the conversation by title or timeframe
3. Click to open and continue from where you left off

Works for recent history or conversations with descriptive opening messages.

Method 2: Browser history search

For conversations accessed in the last few weeks:

1. Open browser history (Ctrl+H)
2. Search for "kimi.ai"
3. Kimi conversation URLs include unique identifiers

Browser history search is typically faster than sidebar scrolling for conversations within the browser's history window.

Method 3: Browser Ctrl+F inside an open conversation

Given Kimi's long-context specialisation, conversations can be extremely long. Once you've located the right conversation:

1. Open it
2. Use Ctrl+F to search for specific text within the conversation

For Kimi specifically — where a single conversation might contain a 50-page document and extensive analysis — this is often the most useful in-conversation search method.

Method 4: LLMnesia for full-text content search

For users who use Kimi regularly and need to locate specific insights from past long-context sessions, full-text indexing is particularly valuable:

LLMnesia indexes Kimi conversations as you browse them. Because Kimi conversations often contain large amounts of text (both user uploads and AI responses), the indexed content is extensive and search results tend to be highly specific.

After installation, search by keyword to find which Kimi conversation discussed a specific topic, referenced a specific document section, or reached a specific conclusion.

Privacy note on Kimi

Kimi is operated by Moonshot AI, headquartered in Beijing. Your conversations are stored on servers in China. This mirrors the considerations raised for DeepSeek and other Chinese AI tools:

• Conversations may be subject to Chinese data law
• Many enterprise security policies restrict or prohibit Chinese AI tools for work content
• If privacy or data sovereignty matters for your use case, factor this in before using Kimi for sensitive conversations

LLMnesia's local indexing stores content on your device only — nothing is sent to external servers. This means your Kimi conversation content, once indexed locally, stays on your machine regardless of what Moonshot AI does with it on their end.

Kimi's long-context advantage and retrieval

The use cases where Kimi excels — analysing long legal documents, reviewing lengthy codebases, processing extended research papers — are also the cases where retrieval has the most value.

A conversation where Kimi identified three critical clauses in a 100-page contract is worth finding again when a similar contract comes up. A session where Kimi mapped dependencies across a large codebase is valuable to reference without re-uploading the code.

For this reason, Kimi users who don't have a retrieval system in place are losing more value per conversation than users of shorter-context platforms.

Tips for managing Kimi history

Name conversations by document or project. After uploading a document to Kimi, rename the conversation with the document name or project title immediately. The auto-generated title may be generic. A specific title like "Q3 Contract Review — Acme Corp" is infinitely more findable than "Contract analysis."

One conversation per document or analysis task. Because Kimi handles long contexts, it's tempting to continue the same conversation for multiple related tasks. For retrieval purposes, separate conversations by distinct topic or document make future finding easier.

Index actively. Browse back through older Kimi conversations periodically to add them to your LLMnesia index. This is more important for Kimi than for other platforms because the per-conversation information density is higher.