Universal Memory MCP — AI Conversation Memory

A Model Context Protocol (MCP) server that provides persistent, searchable conversation memory across multiple AI platforms. Store, search, and retrieve conversation history with fast full-text search powered by SQLite FTS5.

Features

• 🔍 Fast full-text search via SQLite FTS5 with relevance ranking — ~10x faster than a linear scan (measured)
• 🏷️ Automatic topic extraction — 574+ unique topics across 2,000+ associations
• 📊 Weekly summaries with insights and patterns
• 🗃️ Organized file storage by date and topic
• 🤖 Multi-platform support — Claude, ChatGPT, Cursor AI, and custom formats
• 🔌 MCP integration for Claude Desktop and Claude Code

Quick Start

Prerequisites

• Python 3.10+ (CI runs 3.14)
• An MCP client — Claude Code, Claude Desktop, Codex, or anything else speaking MCP over stdio

Installation

uv tool install universal-memory-mcp   # or: pipx install universal-memory-mcp

Not `pip install`: this is an application, and on Debian/Ubuntu and other

PEP 668 systems installing one into the system interpreter

fails with `error: externally-managed-environment`. Inside a virtualenv you have already

activated, `pip install universal-memory-mcp` is fine.

Then point your client at the `universal-memory-mcp` console script:

claude mcp add --transport stdio universal-memory-mcp -- universal-memory-mcp

Or write it into the config yourself — Claude Code and Claude Desktop:

{ "mcpServers": { "universal-memory-mcp": { "command": "universal-memory-mcp" } } }

Codex (`~/.codex/config.toml`):

[mcp_servers.universal-memory-mcp]
command = "universal-memory-mcp"

The server name is yours to choose, but it sets the tool namespace your client exposes

(`mcp____*`). Conversations live in `~/claude-memory/` regardless, so renaming is safe.

Upgrading an install that points at a checkout? `scripts/switch_mcp_config.py` rewrites both

config formats in place — dry run by default, `--apply` to write.

From source

git clone https://github.com/adamkwhite/universal-memory-mcp.git
cd universal-memory-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
python3 tests/validate_system.py     # optional: verify the install

Point your client at `/.venv/bin/python3 -m universal_memory_mcp.server_fastmcp`. The

package uses relative imports, so running the file directly cannot work — `python3

src/universal_memory_mcp/server_fastmcp.py` fails with `attempted relative import with no known

parent package`.

Basic Usage

MCP Server Mode

Your client starts the server for you; run it by hand only to debug.

universal-memory-mcp                          # installed from PyPI
python3 -m universal_memory_mcp.server_fastmcp  # from source

Bulk Import

# Import conversations from JSON export
python3 scripts/bulk_import_enhanced.py your_conversations.json

MCP Tools

`search_conversations(query, limit=5)`

Full-text search across all stored conversations with relevance ranking. Query text is treated as literal Unicode terms, so punctuation and FTS5 operators do not change the query semantics. Results include conversation IDs for exact retrieval.

`get_conversation(conversation_id, max_chars=12000)`

Retrieve a stored conversation by an ID returned from a search tool. Content is read from the authoritative JSON store and truncated to `max_chars` to protect the model context. `max_chars` must be between 1 and 50,000.

`search_by_topic(topic, limit=10)`

Find conversations tagged with a specific topic.

`add_conversation(content, title, date)`

Store a new conversation with automatic topic extraction and FTS indexing.

`generate_weekly_summary(week_offset=0)`

Generate insights and patterns from recent conversations.

`get_search_stats()`

View search engine statistics — index size, topic counts, and engine status.

`update_conversation(conversation_id, content=None, title=None, add_tags=None, remove_tags=None, set_tags=None, conversation_type=None, session_id=None, user_id=None, change_note=None, record_audit=True)`

Update fields on an existing conversation in place. Pass `conversation_id` plus any subset of fields to change; unspecified fields are left alone. By default, the first line of stored content is rewritten with a self-documenting audit line — `[update — ]` — chained across repeated updates. If `change_note` is omitted, it is derived from the changed fields.

Set `record_audit=False` only for authoritative imports whose content must remain an exact replica of the source system. Normal interactive updates should retain the default audit record.

Tag operations: `set_tags` replaces the full tag list and is mutually exclusive with `add_tags`/`remove_tags` (pass `set_tags=[]` to clear all tags); `add_tags`/`remove_tags` mutate the existing list.

Returns a status string. On success: `Status: success` plus a summary message and, when enabled, the audit line. On failure (malformed ID, conversation not found, no changes provided, conflicting tag ops, or an I/O error): `Status: error` plus a message describing the problem.

`search_by_tag(tag, limit=10)`

Find conversations tagged with a specific tag — a universal metadata field populated by importers or set via `update_conversation` (e.g. `starred`, `archived`, `workspace:my-project`). Exact match, case-sensitive. Requires SQLite FTS to be enabled; without it, returns an error message.

`search_by_session_id(session_id, limit=10)`

Find all conversations sharing a `session_id`, useful for reconstructing a multi-turn session that spans several stored conversation records (e.g. a Cursor working session, a Claude thread continued across days). Results are sorted chronologically (oldest first). Requires SQLite FTS to be enabled; without it, returns an error message.

`search_by_conversation_type(conversation_type, limit=10)`

Find conversations by `conversation_type` (e.g. `chat`, `code`, `analysis`). Exact match, most recent first. Requires SQLite FTS to be enabled; without it, returns an error message.

Architecture

~/claude-memory/
├── conversations/
│   ├── 2025/
│   │   └── 06-june/
│   │       └── 2025-06-01_topic-name.md
│   ├── index.json          # Search index
│   └── topics.json         # Topic frequency
└── summaries/
    └── weekly/
        └── week-2025-06-01.md

Configuration

Claude Desktop Integration

Add to your Claude Desktop MCP config:

{
  "mcpServers": {
    "universal-memory-mcp": {
      "command": "universal-memory-mcp"
    }
  }
}

Installed from source rather than PyPI? Point `command` at your virtualenv's interpreter and

run the module:

{
  "mcpServers": {
    "universal-memory-mcp": {
      "command": "/absolute/path/to/universal-memory-mcp/.venv/bin/python3",
      "args": ["-m", "universal_memory_mcp.server_fastmcp"]
    }
  }
}

> Upgrading from before the package move (#225): configs used to name the server script

> directly (`src/server_fastmcp.py`). That no longer works in any form — the modules moved

> under `src/universal_memory_mcp/`, and the package now uses relative imports, so running

> the file raises `attempted relative import with no known parent package`. Switch to the

> console script or the `-m` form above.

Configuration Precedence

Settings are resolved by `src/universal_memory_mcp/config.py`'s `Config.load()`, consulted in this

order (highest wins):

1. Environment variables (`CLAUDE_MEMORY_*` / `CLAUDE_MCP_*`)

2. Config file (default `~/.claude-memory/config.json`)

3. Platform profile (`default`, `claude`, `chatgpt`, or `cursor` — selects

a partial set of defaults, e.g. `log_format`)

4. Built-in defaults

Environment Variables

When `CLAUDE_MEMORY_PATH` is set explicitly, the path may live outside your

home directory (e.g. a separate data drive on Windows: `D:\claude-memory`).

Paths that are *not* explicitly configured are still restricted to the home

or project directory for safety.

Config File

As an alternative to environment variables, settings can be placed in

`~/.claude-memory/config.json`. The file is optional — a missing file falls

back to platform-profile/built-in defaults. Example:

{
  "storage_path": "~/claude-memory",
  "log_format": "json",
  "log_level": "INFO",
  "enable_sqlite": true,
  "console_output": false,
  "platform_profile": "default"
}

Unknown keys in the file raise a configuration error rather than being

silently ignored. Environment variables still override anything set here.

Disabling SQLite

SQLite FTS5 search is enabled by default. On platforms where SQLite/FTS5 is

unavailable (e.g. some Windows Python builds), disable it to fall back to

JSON-based linear search:

export CLAUDE_MEMORY_DISABLE_SQLITE=true

Logging Configuration

Log Format

Switch between human-readable text logs (default) and structured JSON logs for production:

# JSON format (for production log aggregation)
export CLAUDE_MCP_LOG_FORMAT=json

# Text format (default, for development)
export CLAUDE_MCP_LOG_FORMAT=text

JSON Log Example:

{
  "timestamp": "2025-01-15T10:30:45",
  "level": "INFO",
  "logger": "claude_memory_mcp",
  "function": "add_conversation",
  "line": 145,
  "message": "Added conversation successfully",
  "context": {
    "type": "performance",
    "duration_seconds": 0.045,
    "conversation_id": "conv_abc123"
  }
}

JSON logging is ideal for:

• Production deployments with log aggregation (Datadog, ELK, CloudWatch)
• Automated monitoring and alerting
• Structured log analysis and querying
• Performance tracking and debugging

See `docs/json-logging.md` for detailed JSON logging documentation.

File Structure

universal-memory-mcp/
├── src/
│   ├── server_fastmcp.py       # Main MCP server
│   ├── conversation_memory.py  # Core memory engine + SQLite FTS5
│   ├── format_detector.py      # Auto-detect AI platform format
│   ├── validators.py           # Input validation
│   ├── logging_config.py       # Structured logging (text/JSON)
│   ├── importers/              # Platform-specific importers
│   │   ├── chatgpt_importer.py
│   │   ├── claude_importer.py
│   │   ├── cursor_importer.py
│   │   └── generic_importer.py
│   └── schemas/                # JSON schema validation
├── tests/                      # 435 tests, 98.68% coverage
├── data/                       # Consolidated app data
├── scripts/                    # Import and utility scripts
└── docs/                       # Documentation

Performance

`scripts/benchmark_search.py` was broken (unawaited async calls, measuring

coroutine construction instead of real search time) from October 2025 until

this was found and fixed. The previous numbers below were never actually

measured and have been replaced with real ones. Reproduce with:

python scripts/generate_test_data.py --conversations 159
python scripts/benchmark_search.py --storage-path ~/claude-memory-test --iterations 5

Measured on a 159-conversation / 7.7MB local dataset (WSL2, Python 3.12) —

treat as order-of-magnitude, not a precise SLA, results vary by machine:

• Search Speed (SQLite FTS5): mean 15–18ms, median 10–13ms per query, range 0.5–82ms across 12 query types (was claimed 0.2–0.5ms; that figure was never measured)
• Search vs. linear JSON scan: SQLite FTS5 is ~10x faster (mean 14.7ms vs 154.2ms; median 10.5ms vs 152.0ms) — the old "4.4x" claim had the right direction but was also never actually measured
• Topic Search: mean 3.4ms, median 2.5ms (was claimed 0.3–0.4ms; that figure was never measured)
• Write Speed: mean 14ms, median 14ms per ~49KB conversation, SQLite indexing included (was claimed ~33ms; that figure was never measured)
• Capacity: 371 conversations in production use over 10 months
• Test Coverage: 98.68% (435 tests) — 0 code smells, 0 security hotspots (SonarCloud verified)

*Last benchmarked: July 2026 | Detailed Report*

Note for Developers: Performance benchmarks create a `~/claude-memory-test` directory for isolated testing. Normal MCP usage only uses `~/claude-memory/`. If you see `~/claude-memory-test`, it can be safely deleted.

Search Examples

# Technical topics
search_conversations("terraform azure")
search_conversations("mcp server setup")
search_conversations("python debugging")

# Project discussions
search_conversations("interview preparation")
search_conversations("product management")
search_conversations("architecture decisions")

# Specific problems
search_conversations("dependency issues")
search_conversations("authentication error")
search_conversations("deployment configuration")

Development

Adding New Features

1. Topic Extraction: Modify `_extract_topics()` in `ConversationMemoryServer`

2. Search Algorithm: Enhance `search_conversations()` method

3. Summary Generation: Improve `generate_weekly_summary()` logic

Testing

# Run validation suite
python3 tests/validate_system.py

# Run full test suite with coverage
python3 -m pytest tests/ --cov=src --cov-report=term

# Import test data
python3 scripts/bulk_import_enhanced.py test_data.json --dry-run

Test Data Storage (Developers Only): If you run performance benchmarks or test data generators, they create a `~/claude-memory-test` directory to isolate test data from your production `~/claude-memory` directory. This is only for development/testing - normal MCP usage does not create this directory.

To clean up test data after running benchmarks:

rm -rf ~/claude-memory-test

Or using the Makefile cleanup target:

make clean-test-data

Troubleshooting

Common Issues

MCP Import Errors: the `mcp` dependency comes with the package, so this normally means the

server is running under an interpreter that does not have it. Check which one your MCP config

invokes: the `universal-memory-mcp` console script from `uv tool`/`pipx`, or your virtualenv's

`python3 -m universal_memory_mcp.server_fastmcp` — not a bare system `python3`.

Search Returns No Results:

• Check conversation indexing: `ls ~/claude-memory/conversations/index.json`
• Verify file permissions
• Run validation: `python3 tests/validate_system.py`

Weekly Summary Timezone Errors:

• Ensure all datetime objects use consistent timezone handling
• Recent fix addresses timezone-aware vs naive comparison

System Requirements

• Python: 3.10+ (CI runs 3.14)
• Disk Space: ~10MB per 100 conversations
• Memory: <100MB RAM usage
• OS: Linux/WSL and Windows are both verified in CI on every PR (Ubuntu + `windows-latest`).

macOS is expected to work but is not covered by a CI runner.

Contributing

1. Fork the repository

2. Create a feature branch: `git checkout -b feature-name`

3. Commit changes: `git commit -am 'Add feature'`

4. Push to branch: `git push origin feature-name`

5. Submit a Pull Request

A note for fork PRs: GitHub does not give forks access to repository secrets, so the

SonarCloud scan and the performance-results comment are skipped on your PR rather than run.

That is expected and is not something you can or should fix — the test suite, linting, CodeQL and

the Windows run all still execute normally, and coverage on your changes is checked when the

branch lands on `main`. If you see those two skipped, nothing is wrong.

Releasing

Publishing is tag-gated and uses Trusted Publishing (OIDC) — there is no PyPI token stored in

this repo. `.github/workflows/publish.yml` fires only on a `vX.Y.Z` tag.

One-time setup on PyPI (publisher settings for the project, or a *pending* publisher while the

name is still unclaimed):

To cut a release:

# 1. bump `version` in pyproject.toml, commit, merge to main
# 2. tag the merged commit — the workflow refuses a tag that disagrees with pyproject
git tag v0.1.0 && git push origin v0.1.0

The workflow builds, runs `twine check`, installs the wheel into a clean venv and asserts that

every module imports and that no generic top-level name leaked, then publishes. Add required

reviewers to the `pypi` environment in repo settings for a manual approval gate as well.

Rehearse on TestPyPI before the first real upload — the first upload claims the name

permanently, and a version number can never be reused:

rm -rf dist && uv build
uv run --with twine --no-project twine upload --repository testpypi dist/*
# TestPyPI does not mirror mcp/jsonschema/aiofiles, so pull deps from real PyPI:
uv pip install --index-url https://test.pypi.org/simple/ \
               --extra-index-url https://pypi.org/simple/ universal-memory-mcp

License

MIT License - see LICENSE file for details

Acknowledgments

• Built with Model Context Protocol (MCP)
• Designed for Claude Desktop integration
• Inspired by the need for persistent conversation context

Status: Production ready ✅

Last Updated: April 2026

Version: 2.0.0