supermemory-agent 0.2.3

SuperMemory: MCP-first learning memory layer for Claude, Cursor, and agent workflows

SuperMemory MCP

MCP-first learning memory layer for Claude, Cursor, and agent workflows. Captures distilled lessons from failures and corrections (not full transcripts), validates before storage, and improves agents over time through a closed-loop cycle.

Install from PyPI (recommended for Claude / Cursor users)

pip install supermemory-agent
supermemory-agent --storage .supermemory --transport stdio

Or with uv:

uvx supermemory-agent --storage .supermemory --transport stdio

Install from source (developers)

pip install -e ".[dev]"

Run MCP server

python -m supermemory_mcp.server --storage .supermemory --transport stdio

Or via CLI entry point:

supermemory-agent --storage .supermemory --transport stdio

Streamable HTTP:

python -m supermemory_mcp.server --storage .supermemory --transport streamable-http

MCP tools (29 total)

GitHub-compatible core (13): retrieve, record_event, record_failure, record_correction, reflect, validate, process_promotions, report_outcome, get_policies, add_policy, add_skill, search_skills, get_skill

Extended UALL (16): learn.run.start, learn.run.event, learn.run.end, learn.store, learn.retrieve, learn.reflect, learn.validate, learn.evaluate, learn.feedback, learn.improvements, learn.analytics, learn.policies, learn.experiment, learn.rollback, learn.skills, learn.telemetry

MCP resources

• supermemory://policies/active
• supermemory://lessons/{lesson_id}
• supermemory://memory/{lesson_id}/provenance
• supermemory://skills/{skill_id}

Agent learning loop

retrieve → record_failure → reflect(event_ids) → validate → process_promotions
         → retrieve again → report_outcome

Cursor / Claude Desktop

MCP server

Copy examples/cursor.mcp.json to .cursor/mcp.json (Cursor project).

For Claude Desktop, merge examples/claude_desktop_config.json into:

%APPDATA%\Claude\claude_desktop_config.json

Restart Claude Desktop after editing the config.

Agent skills (Cursor + Claude Code)

Platform	Project path	Global path
Cursor	.cursor/skills/supermemory-agent-learning/	~/.cursor/skills/supermemory-agent-learning/
Claude Code	.claude/skills/supermemory-agent-learning/	~/.claude/skills/supermemory-agent-learning/
Canonical source	skills/supermemory-agent-learning/	edit here, then run python scripts/sync_skills.py

Mention SuperMemory, agent learning, or MCP memory in chat to load the skill.

Python SDK

from uall_python import UALLClient

client = UALLClient(storage="file")

with client.run(workflow_id="pdf-pipeline", step="planner", namespace="team:eng") as run:
    lessons = run.retrieve(step="planner", max_tokens=800)
    run.record_failure(snippet="chose OCR for searchable PDF", tags=["routing"])
    run.report_lesson_outcome(lesson_id="lesson_001", used=True, accepted=True, improved=True)

REST API

python -m uall_server

Server runs at http://localhost:8000. See api/openapi.yaml.

Storage

Tier	Backend	Default path
Default	.supermemory/ JSON files	SUPERMEMORY_STORAGE_PATH or UALL_DATA_DIR
Optional	SQLite	UALL_STORAGE_BACKEND=sqlite
Enterprise	PostgreSQL + stubs	UALL_STORAGE_BACKEND=postgres

Tests

python tests/run_all.py              # full suite (pytest + agent demos)
python -m pytest tests/ -v           # all unit/integration tests
python -m pytest tests/test_mcp_server.py -v   # real stdio MCP transport
python -m pytest tests/test_core.py -v         # GitHub-compatible closed loop

License

MIT — see LICENSE

Publish / list in directories

See docs/PUBLISHING.md for MCP Registry, Cursor Directory, and Claude Connectors Directory submission steps.