zaxy-memory 0.2.3

Event-sourced temporal knowledge graph fabric for AI agent memory

Zaxy

Event-sourced temporal knowledge graph fabric for AI agent memory.

Zaxy replaces markdown files + vector DBs with a structured, replayable, bi-temporal memory system built on Eventloom and Neo4j, with optional Pathlight tracing.

Quick Start

# Install the Zaxy CLI before generating MCP config. The distribution is
# zaxy-memory; the import package and console command remain zaxy.
pipx install zaxy-memory
# or: pip install zaxy-memory

# 1. Initialize local memory, MCP config, hooks, session genesis, and heartbeat.
# Local stdio MCP auto-starts a localhost Neo4j container when Docker is
# available, so average users do not need to manage a sidecar manually.
zaxy init . \
  --domain my-project \
  --preset local-claude

# Codex local capture can use the managed deterministic watcher.
zaxy init . \
  --domain my-project \
  --preset local-codex \
  --capture start

# Hermes Agent can use the first-class MCP config merge path.
zaxy ide-config hermes --install

# Optional: explicit local development setup if you want shell commands too.
./scripts/setup.sh

# Production setup writes Docker secret files under ./secrets/.
./scripts/setup.sh --production
./scripts/generate-certs.sh .certs
docker compose -f docker-compose.prod.yml up -d

# 2. Explicitly start Neo4j + Zaxy MCP server for development outside an MCP client
docker compose up -d

# 3. Verify
zaxy status
zaxy memory status --eventloom-path .eventloom
zaxy memory log --eventloom-path .eventloom --limit 20
zaxy memory diff --eventloom-path .eventloom --session-id my-project-default --from-seq 1 --to-seq 10
pytest

# Integration-only runs need --no-cov because the project-level coverage gate
# is intended for the full suite.
pytest -m integration --no-cov

# 4. Validate local onboarding and hook posture.
zaxy doctor --eventloom-path .eventloom

Architecture

Agent (LangGraph / Any MCP Client)
    |
    v
MCP Server — memory_append / memory_query / memory_feedback / memory_replay / memory_invalidate
    |
    v
Eventloom (immutable JSONL log)  →  Hybrid Extraction  →  Neo4j (temporal KG)
    |                                                               |
    +—————— Optional Pathlight traces ———————————————→  Query Router
                                                              |
                                                    Hybrid Retrieval
                                                    (exact + BM25 + vector + traversal)

Zaxy also includes an observe-only OpenAI-compatible packet analyzer for model call provenance. It forwards packets to one configured upstream endpoint and records llm.packet.completed events to Eventloom without acting as a router. See LLM Packet Analyzer.

Public Site and Documentation

• Public static site: site/index.html
• Getting started: docs/getting-started.md
• Architecture: docs/architecture.md
• Configuration: docs/configuration.md
• MCP interface: docs/mcp.md
• Eventloom contract: docs/eventloom.md
• Graph schema: docs/graph-schema.md
• Retrieval: docs/retrieval.md
• Benchmarks: docs/benchmarks.md
• LLM packet analyzer: docs/packet-analyzer.md
• Embeddings: docs/embeddings.md
• Security: docs/security.md
• Operations and deployment: docs/operations.md, docs/deployment.md, docs/runbook.md
• Python API: docs/api.md

Key Features

• Immutable audit trail: Eventloom append-only JSONL with SHA-256 hash chains.
• Bi-temporal graph: Facts have validity windows (valid_from, valid_to).
• Hybrid extraction: Rule-based for typed events (60–80% cost reduction), LLM fallback.
• Hybrid retrieval: Exact + keyword + vector + graph traversal with configurable fusion weights.
• Session sharding: One Eventloom log per agent/session, with a shared graph.
• MCP-native: Drop-in memory for any MCP-compatible agent framework over stdio or SSE.
• Observable: Optional Pathlight traces, breakpoints, and diff support.
• Hardened local defaults: bounded MCP inputs, safe session IDs, localhost-bound Neo4j ports, and optional admin token support for replay/invalidation.

Project Structure

File	Purpose
src/zaxy/event.py	Eventloom JSONL I/O + hash chain integrity
src/zaxy/extract.py	Hybrid extraction engine + rule registry
src/zaxy/graph.py	Neo4j bi-temporal wrapper
src/zaxy/query.py	Hybrid retrieval router
src/zaxy/mcp_server.py	MCP stdio/SSE server
src/zaxy/trace.py	Pathlight observability hooks
src/zaxy/core.py	MemoryFabric orchestrator
src/zaxy/session.py	Per-session Eventloom log manager
src/zaxy/security.py	Shared validation and input bounds
src/zaxy/__main__.py	CLI (zaxy serve, zaxy replay, etc.)

Production Secrets

Zaxy supports Docker/Kubernetes-style secret files for sensitive settings:

Variable	Secret-file variant
NEO4J_PASSWORD	NEO4J_PASSWORD_FILE
MCP_ADMIN_TOKEN	MCP_ADMIN_TOKEN_FILE
PATHLIGHT_ACCESS_TOKEN	PATHLIGHT_ACCESS_TOKEN_FILE

Direct environment variables take precedence over their *_FILE variants. Use docker-compose.prod.yml as the production compose baseline.

Development

• Tests first (Karpathy rule). Every public function has a test.
• Unit tests mock Neo4j/Pathlight. Integration tests use Docker.
• Coverage gate: ≥90% enforced by CI.
• Lint/format: ruff. Types: mypy.

# Run full suite with coverage gate
pytest

# Run integration tests (requires Docker)
docker compose up -d neo4j-test
./scripts/generate-certs.sh .certs
docker compose up -d neo4j-tls
pytest -m integration --no-cov

# Lint and type-check
ruff check src tests
mypy src

# Competitive retrieval benchmark harness
pytest tests/test_competitive_benchmarks.py --benchmark-only --no-cov

# Frozen live benchmark: markdown vs BM25 vs vector vs markdown+vector vs Zaxy
scripts/live-benchmark.sh --embedding-provider openai --workload frozen --runs 1 --reset-graph

# Representative benchmark suite: temporal memory + docs + transcripts + mixed context
scripts/live-benchmark.sh --embedding-provider openai --workload suite --subjects 100 --documents 250 --sessions 50 --runs 1 --reset-graph

# LongMemEval-compatible memory benchmark and BM25 comparison
zaxy benchmark --embedding-provider hash --workload longmemeval \
  --dataset .cache/zaxy/benchmarks/longmemeval_oracle.json \
  --questions 100 --runs 1 --limit 10 --zaxy-backend checkout \
  --baseline-backends bm25 --embedding-cache .cache/zaxy/longmemeval-embeddings.json
zaxy benchmark --output-dir reports/benchmarks/longmemeval-100-comparison \
  --embedding-provider hash --workload longmemeval \
  --dataset .cache/zaxy/benchmarks/longmemeval_oracle.json \
  --questions 100 --runs 1 --limit 5 --baseline-backends bm25 \
  --zaxy-backend checkout --reuse-projection \
  --embedding-cache .cache/zaxy/longmemeval-embeddings.json

# Production deployment preflight
scripts/validate-deployment.sh --root .

# Build and validate Python release artifacts
scripts/build-dist.sh --root .

# Verify local release metadata and PyPI Trusted Publishing configuration
zaxy doctor --release-smoke

# Validate public site and documentation links
scripts/validate-docs.sh --root .

# Clean-repo beta UAT: install into a throwaway workspace and verify init,
# bootstrap, deterministic capture, doctor, and memory checkout.
scripts/beta-uat.sh

# Summarize beta readiness gates without external services.
zaxy doctor --beta-readiness

# Go-live release gate
scripts/release-check.sh --root .

The full suite must stay at or above 90% coverage before a sprint is complete.

Release Publishing

The PyPI distribution name is zaxy-memory because zaxy is already occupied on PyPI. Published releases build from GitHub Actions and upload to https://pypi.org/project/zaxy-memory/ using PyPI Trusted Publishing with GitHub OIDC. The import package and console command remain zaxy.

Before publishing, run zaxy doctor --release-smoke to verify the package version, changelog entry, release workflow, and tokenless publishing posture.

License

MIT