Zaxy

Production memory for agent teams that need receipts.

<!-- mcp-name: io.github.syndicalt/zaxy -->

Zaxy turns agent context into an auditable project memory fabric. It captures parent missions, worker sessions, tool observations, cited findings, conflict review, approval packets, and accepted merge-back into one durable history that can be queried, replayed, and inspected.

Under the hood, Zaxy uses Eventloom append-only JSONL as the source of truth and an embedded LadybugDB graph projection for local reasoning. It is built for agents that need to remember what happened, cite where it came from, and avoid turning project state into a pile of markdown files and vector chunks.

The embedded LadybugDB graph projection is the default local runtime.

The plain install uses embedded LadybugDB. Install zaxy-memory[neo4j] only for the optional Neo4j sidecar, and zaxy-memory[pathlight] only for Pathlight tracing.

Why It Matters

• Auditable memory: every accepted fact can point back to Eventloom history.
• Agent-team coordination: parent and worker sessions stay isolated until findings are reviewed and merged.
• Local-first runtime: the default path uses embedded LadybugDB, no Neo4j sidecar.
• MCP-native integration: Codex, Claude Code, Cursor, VS Code, Hermes Agent, LangGraph, CrewAI, and AutoGen can use the same memory interface.
• External benchmark evidence: on the full Harvey LAB legal-agent memory benchmark, Zaxy scored 0.788 mean criterion pass rate across 10/10 tasks, +0.184 vs regular/no-memory, +0.081 vs the article-best task rows, and won 9/10 task comparisons. See Benchmarks; the published stats artifact is reports/benchmarks/harvey-lab-memory-ablation/publishable-statistics.md.
• Headline 500 evidence: the current LongMemEval-compatible checkout diagnostic is a full 500-question run with mean 0.956, Answer@5 0.910, Recall@5 1.000, and citation coverage 1.000. See Benchmarks.

Quick Start

Install, init, verify

pipx install zaxy-memory
zaxy init
zaxy memory log --eventloom-path .eventloom --limit 5
zaxy memory bootstrap --eventloom-path .eventloom
zaxy doctor --eventloom-path .eventloom

The PyPI distribution is zaxy-memory; the import package and console command are still zaxy. Bare zaxy init sets up the local embedded graph posture, repo-local profile, deterministic capture config, genesis event, heartbeat, and MCP guidance. For Codex, the printed activation launcher starts the managed capture watcher when the local capture config is present; pass --capture start only when you want init itself to start the watcher before opening Codex. The default human output is compact and action-first; add --verbose when you need the full setup diagnostics, optional checks, fallback commands, resume guidance, and notes. For automation, zaxy init --json keeps the raw onboarding fields and adds setup.status, setup.issues, setup.pending, readiness.status, readiness.reasons, readiness.actions, and structured readiness.action_items for both commands and non-command review tasks. Each structured action carries label, command, original source, and hints for compact-output tips such as activation <task> replacement and path-stable command guidance. Installers can render those tips without parsing prose. It also includes setup.summary, readiness.summary, readiness.required_action_count, and readiness.reason_count, so client UIs can render compact status without parsing human output. It also separates readiness.blocking_diagnostics from readiness.non_blocking_diagnostics so scripts can distinguish setup completion, required actions, and advisory doctor warnings before relying on live memory.

For Codex, zaxy init --codex-mcp-install auto is the default. It writes or reuses the user-level Codex MCP config when that can be done without replacing an existing zaxy server entry. If no safe config target exists, it prints the copyable codex mcp add command. If an existing zaxy entry differs, it asks you to review that config before replacing it because Codex can silently replace servers with the same name. Use an explicit mode when you need to force one side of that decision after review:

zaxy init --codex-mcp-install user
# or: zaxy init --codex-mcp-install command

Both Codex paths keep the server workspace-neutral. After init, start or restart Codex through the printed zaxy activate codex ... --launch command so the MCP server list and Zaxy activation packet are loaded together. The printed command includes explicit --eventloom-path and --workspace-root values, so it still targets the initialized repo when copied from another shell.

Run the single-agent memory example:

python examples/single_agent_memory.py

Your local data lives under .eventloom/ as one append-only JSONL file per session.

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 LadybugDB 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
• MCP quickstart: docs/mcp-quickstart.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
• Stability commitment: docs/stability-commitment.md
• Migration guide: docs/migration.md
• Archived benchmark iteration notes, release drafts, and research notes live under docs/archive/, docs/announcements/, and docs/research/.
• Contributing: CONTRIBUTING.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 LadybugDB 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: ≥92% 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

# Current full-set LongMemEval-compatible checkout evidence:
# reports/benchmarks/longmemeval-500-publish-20260607/
# Mean 0.956, Answer@5 0.910, citation coverage 1.000, R@1/R@5/R@10 0.960/1.000/1.000.

# Stage the next full 500 only after docs/report cleanup is complete.
zaxy benchmark --output-dir reports/benchmarks/longmemeval-500-next \
  --embedding-provider hash --workload longmemeval \
  --dataset .cache/zaxy/benchmarks/longmemeval_oracle.json \
  --runs 1 --limit 5 --baseline-backends bm25 \
  --projection-backend embedded --zaxy-backend checkout

# Harvey LAB external memory-ablation comparison
# Consumes externally generated Harvey normalized-result artifacts for Zaxy;
# does not reuse LongMemEval statistics as legal-agent benchmark evidence.
# Current full external Harvey LAB evidence:
# reports/benchmarks/harvey-lab-memory-ablation/publishable-statistics.md
# reports/benchmarks/harvey-lab-memory-ablation/harvey-lab-benchmark.json
# 10/10 tasks, mean criterion pass rate 0.788, +0.184 vs regular/no-memory,
# +0.081 vs article-best task rows, 9/10 wins vs article-best rows.

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

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

# Verify local release metadata, PyPI Trusted Publishing, and LangGraph smoke
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 92% 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, tokenless publishing posture, and dependency-light LangGraph example.

License

MIT