Event-sourced temporal knowledge graph fabric for AI agent memory
Project description
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 an embedded Kuzu graph
projection, with optional Neo4j, pgGraph, and Pathlight integrations.
The plain install uses embedded Kuzu. Install zaxy-memory[neo4j] only for the
optional Neo4j sidecar, and zaxy-memory[pathlight] only for Pathlight tracing.
Quick Start
Five-minute local smoke test
# 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
# Initialize local memory, Codex MCP guidance, deterministic capture config,
# profile, genesis, heartbeat, and no-sidecar embedded graph posture.
zaxy init
# Prove the local Eventloom log and model bootstrap are readable.
zaxy memory log --eventloom-path .eventloom --limit 5
zaxy memory bootstrap --eventloom-path .eventloom
zaxy doctor --eventloom-path .eventloom
Your local data lives under .eventloom/ as one append-only JSONL file per
session. Bare zaxy init now expands to the local embedded Codex path: it
prints the Codex MCP install command, writes .codex/zaxy-capture.json, writes
.env.local, checks the repo-local embedded graph posture, and ends with
copyable local verification commands. It does not start a background watcher
unless you pass --capture start.
For Claude Code instead of Codex:
zaxy init . --domain my-project --preset local-claude --infra check
For Hermes Agent:
zaxy ide-config hermes --install
For repository development, use pip install -e ".[dev]", ./scripts/setup.sh,
and zaxy status. Start Docker sidecars only for integration tests or explicit
backend comparisons. Production setup writes Docker secret files under
./secrets/; see docs/deployment.md.
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 → Embedded Kuzu graph
| |
+—————— 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 - Why Zaxy:
docs/why-zaxy.md - 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 via
zaxy-memory[pathlight]. - Hardened local defaults: bounded MCP inputs, safe session IDs, no-sidecar embedded graph projection, 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/embedded_graph_store.py |
Embedded Kuzu projection store |
src/zaxy/graph.py |
Optional Neo4j bi-temporal wrapper via zaxy-memory[neo4j] |
src/zaxy/query.py |
Hybrid retrieval router |
src/zaxy/mcp_server.py |
MCP stdio/SSE server |
src/zaxy/trace.py |
Optional 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 external services. Integration tests use Docker for optional sidecar backends.
- Coverage gate: ≥90% enforced by CI.
- Lint/format:
ruff. Types:mypy.
# Run full suite with coverage gate
pytest
# Run integration tests (requires Docker)
./scripts/generate-certs.sh .certs
docker compose --profile integration up -d neo4j-test 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 embedded Zaxy
# Uses deterministic hash embeddings and embedded projection by default.
scripts/live-benchmark.sh --workload frozen --runs 1 --reset-graph
# Representative benchmark suite: temporal memory + docs + transcripts + mixed context
scripts/live-benchmark.sh --workload suite --subjects 100 --documents 250 --sessions 50 --runs 1 --reset-graph
# LongMemEval-compatible memory benchmark and BM25 comparison
# Plain benchmark commands use the embedded projection backend by default.
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
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