kiro-total-recall

kiro-total-recall

Enables semantic search across all Kiro conversations (CLI and IDE), allowing the AI to recall past discussions, decisions, and user preferences.

Category
Visit Server

README

Kiro Total Recall

Ever told Kiro "like we discussed yesterday" only to realize... it has no idea?

Total Recall gives Kiro the memory it's missing.

The Problem

  1. Sessions Are Isolated: Each Kiro session starts fresh. Yesterday's architecture discussion? Gone.
  2. Projects Don't Share Knowledge: Your preferences (testing style, package managers, patterns) aren't remembered across projects.
  3. CLI and IDE Are Separate: Conversations in Kiro CLI don't connect to Kiro IDE.

Total Recall indexes every Kiro conversation and provides semantic search. Find discussions by meaning, not just keywords.

Quickstart

As a Kiro Power (Recommended, IDE only)

  1. In Kiro IDE: Powers panel → Add power from GitHub
  2. Enter: https://github.com/danilop/kiro-total-recall
  3. The power activates automatically when you mention "recall", "remember", or "past conversation"

Manual MCP Setup (CLI and IDE)

Add to ~/.kiro/settings/mcp.json (this config is shared by both CLI and IDE):

{
  "mcpServers": {
    "total-recall": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/danilop/kiro-total-recall", "kiro-total-recall"]
    }
  }
}

Restart Kiro CLI/IDE after adding. MCP servers are only loaded at startup.

Verify Installation

In Kiro CLI: /mcp should list total-recall

In Kiro IDE: Check the MCP Servers panel

How It Works

┌─────────────────────────────────────────────────────────────────────────┐
│                         Kiro CLI & IDE                                  │
│  ┌──────────────────────────┐    ┌──────────────────────────────────┐   │
│  │  CLI: SQLite DB          │    │  IDE: .chat JSON files           │   │
│  │  ~/Library/App Support/  │    │  ~/Library/App Support/Kiro/     │   │
│  │  kiro-cli/data.sqlite3   │    │  User/globalStorage/.../*.chat   │   │
│  └────────────┬─────────────┘    └─────────────┬────────────────────┘   │
│               └────────────────┬───────────────┘                        │
│                                ▼                                        │
│                    ┌───────────────────────┐                            │
│                    │   Unified Loader      │                            │
│                    └───────────┬───────────┘                            │
└────────────────────────────────┼────────────────────────────────────────┘
                                 ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                      Kiro Total Recall                                  │
│  ┌─────────┐    ┌──────────┐    ┌─────────┐    ┌────────────────────┐   │
│  │ loader  │───▶│ indexer  │───▶│  query  │───▶│  MCP server        │   │
│  │ CLI+IDE │    │ 384-dim  │    │ cosine  │    │  4 search tools    │   │
│  └─────────┘    └────┬─────┘    └─────────┘    └────────────────────┘   │
│                      ▼                                                  │
│         ~/.cache/kiro-total-recall/embeddings.pkl                       │
└─────────────────────────────────────────────────────────────────────────┘

The Index: Making Search Fast

On first search, Total Recall:

  1. Loads all messages from CLI (SQLite) and IDE (.chat files)
  2. Embeds each message using all-MiniLM-L6-v2 (384-dimensional vectors)
  3. Caches embeddings to ~/.cache/kiro-total-recall/embeddings.pkl

Subsequent searches are fast because:

  • Fingerprinting: Only rebuilds when conversations change
  • Incremental updates: New messages get embedded; existing embeddings loaded from cache
  • Hash-based deduplication: Same text = same embedding (no recomputation)

Features

  • Semantic Search: Find by meaning, not just keywords
  • Dual Source: Searches both CLI and IDE conversations
  • Context Windows: See surrounding messages for each match
  • Date Filtering: Filter by time range (ISO 8601)
  • Incremental Indexing: Only processes new conversations
  • Memory Limits: Configurable RAM usage (default: 1/3 of RAM)

MCP Tools

Tool Scope Use Case
search_project_history Current workspace Bugs, decisions in this codebase
search_global_history All workspaces Preferences, patterns across all work
search_cli_history CLI only Kiro CLI conversations
search_ide_history IDE only Kiro IDE conversations

Parameters

All tools accept:

Parameter Default Description
query required Keywords or sentence to search
after none Filter to messages on/after this date (inclusive). ISO 8601 format.
before none Filter to messages before this date (exclusive). ISO 8601 format.
context_size 3 Messages before AND after each match
threshold 0.2 Minimum similarity (0-1, higher = stricter)
max_results 10 Maximum results to return
offset 0 Skip results (for pagination)

Date Filtering Examples

# Messages from a specific day
search_project_history(query="auth bug", after="2025-01-15", before="2025-01-16")

# Messages from the past week
search_project_history(query="refactoring", after="2025-01-25")

# Messages in January
search_project_history(query="database", after="2025-01-01", before="2025-02-01")

Response Structure

{
  "results": [
    {
      "matched_message": {
        "role": "assistant",
        "content": "To fix the authentication bug...",
        "timestamp": "2025-01-15T10:30:00",
        "workspace": "/Users/dev/myproject",
        "session_id": "abc123",
        "uuid": "msg-456",
        "source": "cli"
      },
      "score": 0.8542,
      "context": [
        {"role": "user", "content": "How do I fix this auth bug?", "timestamp": "...", "is_match": false},
        {"role": "assistant", "content": "To fix the authentication bug...", "timestamp": "...", "is_match": true}
      ]
    }
  ],
  "query": "authentication bug fix",
  "total_matches": 25,
  "offset": 0,
  "has_more": true,
  "hint": "Showing 1-10 of 25 matches. Use offset: 10 for more."
}

Usage Examples

Just ask naturally:

"How did we fix that auth bug?"
"What did we discuss about the database schema?"
"What's my usual approach to error handling?"
"Find our React component discussions from last week"

Or use tools directly:

search_project_history(query="authentication bug fix")
search_global_history(query="React component patterns")
search_cli_history(query="deployment", after="2025-01-01")

Configuration

Create ~/.config/kiro-total-recall/config.toml to customize:

[sources.cli]
enabled = true
paths = [
    "~/Library/Application Support/kiro-cli/data.sqlite3",
    "~/.local/share/kiro-cli/data.sqlite3",
    "~/AppData/Roaming/kiro-cli/data.sqlite3",
]

[sources.ide]
enabled = true
patterns = [
    "~/Library/Application Support/Kiro/User/globalStorage/kiro.kiroagent/*/*.chat",
    "~/.config/Kiro/User/globalStorage/kiro.kiroagent/*/*.chat",
    "~/AppData/Roaming/Kiro/User/globalStorage/kiro.kiroagent/*/*.chat",
]

[embedding]
model = "all-MiniLM-L6-v2"
cache_dir = "~/.cache/kiro-total-recall"

[search]
default_threshold = 0.2
default_max_results = 10
default_context_window = 3

[memory]
fraction = 0.33  # Use 1/3 of RAM
# limit_mb = 512  # Or set explicit limit

Memory Management

Total Recall limits in-memory index size to prevent excessive memory usage. By default, it uses 1/3 of physical RAM. When the limit is reached, the oldest sessions are excluded from the index (newest sessions are kept).

Variable Description Default
KIRO_RECALL_MEMORY_LIMIT_MB Override memory limit in MB 1/3 of RAM
KIRO_RECALL_NO_MEMORY_LIMIT Set to any value to disable limit -

Testing

# Test server starts
uvx kiro-total-recall
# Ctrl+C to exit

# Test search directly
uv run python -c "
from kiro_total_recall.query import search_conversations
result = search_conversations(query='bug fix', max_results=3)
print(f'Found {result.total_matches} matches')
"

Project Structure

kiro-total-recall/
├── POWER.md                      # Kiro Power manifest + steering
├── mcp.json                      # MCP server config for Power
├── config.default.toml           # Default configuration
├── src/kiro_total_recall/
│   ├── server.py                 # FastMCP server, tool definitions
│   ├── query.py                  # Search engine, deduplication
│   ├── indexer.py                # Embedding, caching, fingerprinting
│   ├── loader.py                 # Unified loader (CLI + IDE)
│   ├── cli_loader.py             # SQLite parsing for CLI
│   ├── ide_loader.py             # JSON parsing for IDE .chat files
│   ├── config.py                 # Configuration management
│   └── models.py                 # Pydantic data models
├── pyproject.toml
└── LICENSE

Technical Details

Component Technology
Embedding model all-MiniLM-L6-v2 (384 dimensions)
Vector search Cosine similarity via NumPy dot product
Cache format Python pickle with file locking
MCP framework FastMCP
Package manager uv

License

MIT License

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured