memory-access 0.1.22

An MCP server that gives AI agents persistent, intent-based memory with vector embeddings and semantic indexing.

memory-access

An MCP server that gives AI agents persistent, intent-based memory. Text is decomposed into atomic insights, classified into semantic frames, embedded as vectors, and stored in a SQLite knowledge graph with subject indexing.

Quick Start

1. Clone and install

git clone https://github.com/emmahyde/memory-access.git
cd memory-access
uv sync --group dev

Or install from PyPI:

pip install memory-access
# or
uv add memory-access

Requires Python 3.12+.

2. Set environment variables

For OpenAI + Anthropic (default):

export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...

For AWS Bedrock:

export EMBEDDING_PROVIDER=bedrock
export LLM_PROVIDER=bedrock
export AWS_PROFILE=your-sso-profile
export AWS_REGION=us-east-1

3. Install the Claude Code plugin

claude plugin install memory-access@emmahyde

4. Set up knowledge base support (optional)

To ingest external documentation as knowledge bases:

export FIRECRAWL_API_KEY=...  # Get from https://www.firecrawl.dev/

5. Verify

uv run pytest          # 174 tests pass
uv run memory-access         # server starts (MCP server mode)
uv run memory-access kb list # knowledge base CLI mode

Configuration

Environment variables

Variable	Required	Description
OPENAI_API_KEY	Yes*	Embedding generation via OpenAI (text-embedding-3-small)
ANTHROPIC_API_KEY	Yes*	Insight normalization via Anthropic (Claude Haiku)
MEMORY_DB_PATH	No	Database path. Default: ~/.claude/memory-access/memory.db
EMBEDDING_PROVIDER	No	openai (default) or bedrock
LLM_PROVIDER	No	anthropic (default) or bedrock
AWS_PROFILE	No	AWS SSO profile name (required for Bedrock)
AWS_REGION	No	AWS region for Bedrock. Default: us-east-1
BEDROCK_EMBEDDING_MODEL	No	Bedrock model ID. Default: amazon.titan-embed-text-v2:0
BEDROCK_LLM_MODEL	No	Bedrock Claude model ID. Default: us.anthropic.claude-haiku-4-5-20251001-v1:0
FIRECRAWL_API_KEY	No	Web crawling for knowledge bases. Get from https://www.firecrawl.dev/
CRAWL_SERVICE	No	Crawl provider. Default: firecrawl
MIN_CONFIDENCE_THRESHOLD	No	Minimum confidence to store ingested chunks. Default: 0.5

* Not required when using bedrock provider.

Claude Code

Add to your MCP settings (see mcp-config-example.json):

{
  "mcpServers": {
    "memory-access": {
      "command": "python3",
      "args": ["-m", "memory_access.server"],
      "env": {
        "MEMORY_DB_PATH": "~/.claude/memory-access/memory.db",
        "OPENAI_API_KEY": "${OPENAI_API_KEY}"
      },
      "cwd": "/path/to/memory-access/src"
    }
  }
}

AWS Bedrock

To use AWS Bedrock instead of OpenAI/Anthropic APIs:

{
  "mcpServers": {
    "memory-access": {
      "command": "python3",
      "args": ["-m", "memory_access.server"],
      "env": {
        "MEMORY_DB_PATH": "~/.claude/memory-access/memory.db",
        "EMBEDDING_PROVIDER": "bedrock",
        "LLM_PROVIDER": "bedrock",
        "AWS_PROFILE": "your-sso-profile",
        "AWS_REGION": "us-east-1"
      },
      "cwd": "/path/to/memory-access/src"
    }
  }
}

MCP Tools (21 tools)

Storage

• store_insight — Store text with optional domain and git context (repo, pr, author, project, task). Text is decomposed into atomic insights, classified, embedded, and indexed.
• update_insight — Update an insight's confidence score.
• forget — Delete an insight.

Search

• search_insights — Semantic similarity search by query text. Returns ranked results.
• list_insights — List insights filtered by domain or frame.
• search_by_subject — Find insights by subject name and kind.
• related_insights — Find insights connected through shared subjects (graph traversal).

Knowledge graph

• add_subject_relation — Create a typed edge between two subjects (e.g., problem --solved_by--> resolution).
• get_subject_relations — Query outgoing relations from a subject.

Knowledge Bases

• add_knowledge_base — Create a new knowledge base by crawling or scraping a URL. Supports both full site crawling and single-page scraping with automatic ingestion and semantic indexing.
• search_knowledge_base — Search external documentation by semantic similarity. Optionally filter by KB name.
• list_knowledge_bases — List all knowledge bases with descriptions and chunk counts.

Task orchestration

• create_task — Create a task with DB-enforced lifecycle state.
• assign_task_locks — Acquire active locks for resources (exact + path-prefix conflict detection).
• release_task_locks — Release active locks for a task (optionally scoped to resources).
• add_task_dependencies — Add dependency edges between tasks.
• transition_task — Atomically transition state with optimistic concurrency (expected_version).
• append_task_event — Append audit/worklog events (append-only at DB level).
• get_task — Fetch a task by ID.
• list_tasks — List tasks with optional status filter.
• list_task_events — List append-only events for a task.

Semantic Frames

Insights are normalized into one of six canonical frames:

Frame	Meaning	Example
causal	X causes Y	"Increasing cache TTL reduces database load"
constraint	X requires Y	"OAuth tokens must be refreshed before expiry"
pattern	When X, prefer Y	"When pagination exceeds 1000 items, use cursor-based pagination"
equivalence	X is equivalent to Y	"A mutex and a binary semaphore serve the same purpose"
taxonomy	X is a type of Y	"Rate limiting is a type of flow control"
procedure	To achieve X, do Y	"To deploy to staging, run the CI pipeline with the staging flag"

Subject Kinds

Insights are automatically indexed by extracted subjects:

Kind	Source
domain, entity, problem, resolution, context	Extracted from text by the normalizer
repo, pr, person, project, task	From git context parameters on store_insight

Knowledge Bases

Ingest external documentation (APIs, guides, frameworks) into independently queryable knowledge bases. Create and query KBs via CLI or MCP — use whichever fits your workflow.

MCP Tools

From Claude Code, use MCP tools for full KB lifecycle:

// Create a new KB by crawling
add_knowledge_base("rails-docs", "https://guides.rubyonrails.org/association_basics.html",
                   "Rails Association Guide")

// Create a KB from a single page
add_knowledge_base("python-async", "https://docs.python.org/3/library/asyncio.html",
                   "Python asyncio docs", scrape_only=true)

// Search across all KBs
search_knowledge_base("has_many association in Rails")

// Search within a specific KB
search_knowledge_base("asyncio event loop", kb_name="python-async")

// List all KBs
list_knowledge_bases()

CLI Commands

From the command line, use subcommands for KB management:

# Create a new knowledge base by crawling a URL
memory-access kb new rails-docs --crawl https://guides.rubyonrails.org/association_basics.html --description "Rails API Reference"

# Create from a single page
memory-access kb new python-async --scrape https://docs.python.org/3/library/asyncio.html

# List all knowledge bases
memory-access kb list

# Delete a knowledge base
memory-access kb delete rails-docs

# Re-crawl and refresh a knowledge base
memory-access kb refresh rails-docs --limit 100

Architecture

The KB pipeline mirrors the insight storage system:

External docs (web)
  |
  v
Crawl → Split → Normalize → Embed → Store

• Crawl: Firecrawl extracts markdown from web pages
• Split: Heading-based chunking with paragraph and size fallbacks
• Normalize: Same LLM decomposition as insights (Claude Haiku)
• Embed: Same embedding engine as insights (OpenAI or Bedrock)
• Store: SQLite tables (kb_chunks) with semantic frames and subjects

KB chunks share the subjects index with insights, enabling cross-referencing chat memory with external docs.

How It Works

User text
  |
  v
Normalizer (Claude Haiku)
  |- Decompose into atomic insights
  |- Classify: frame + subjects (entities, problems, resolutions, contexts)
  |
  v
Embedding Engine (OpenAI text-embedding-3-small)
  |- Generate L2-normalized vector
  |
  v
InsightStore (SQLite)
  |- Insert insight row
  |- Upsert subjects, link via junction table
  |- Auto-create subject relations (problem->solved_by->resolution, etc.)
  |- Auto-create insight relations (shared_subject edges)
  |- If git context: create repo/pr/person/project/task subjects + relations

Development

# Run all tests (174 tests: 162 unit tests + 12 integration tests)
uv run pytest

# Run a specific test file
uv run pytest tests/test_storage.py

# Run a specific test
uv run pytest tests/test_storage.py::TestInsightStoreInit::test_initialize_creates_tables -v

# Run integration tests only
uv run pytest tests/test_kb_integration.py -v

# Generate dummy data for testing
uv run python scripts/generate_dummy_data.py

Database Schema

The database has tables for insights, knowledge bases, and knowledge graphs:

Core Tables

• insights — Chat memory: text, normalized_text, frame, subject lists (JSON arrays), embedding (float32 blob), confidence, timestamps
• subjects + insight_subjects — Subject index linking insights to named/typed subjects
• insight_relations — Weighted edges between insights (shared_subject)
• subject_relations — Typed edges between subjects (contains, scopes, solved_by, etc.)

Knowledge Base Tables

• knowledge_bases — Registry: name, description, source_type, created_at, updated_at
• kb_chunks — Crawled and normalized chunks: text, frame, subjects, embedding, source_url, confidence
• kb_chunk_subjects — Subject index for KB chunks (same subjects as insights)
• kb_insight_relations — Optional links between KB chunks and chat insights (for cross-referencing)

Metadata

• schema_versions — Migration tracking

Migrations run automatically on startup. Currently at version 5.

Claude Code Plugin

This repo is also a Claude Code plugin. Install with:

claude plugin install memory-access@emmahyde

Includes:

• Skill (using-memory-access) — search strategy guide, subject kinds/relations reference, best practices
• PreCompact hook — prompts Claude to store key insights before context compaction