https://engraphis.com/

https://discord.com/invite/Wfr2ejBmY

Give your AI agents a memory. See it, search it, and maintain it, all in a beautiful WebUI on your own machine.

^{Knowledge Graph · run engraphis-dashboard to see it live}

An agent should not have to reconstruct a project from scattered chat history on every task. Engraphis turns local project knowledge into scoped, time-aware memory; retrieves the evidence that supports the current question; and returns a bounded, attributable context packet.

^{Store durable project knowledge · retrieve supporting evidence · give the agent only what it needs}

The flow is the essential path. See measured context savings for reproducible fixture-level evidence of less returned content at the same tested retrieval scores, without billing or latency claims. The sections below cover the dashboard, code graph, local installation, governance controls, and hosted services in detail.

The examples below use synthetic, checked-in evaluation inputs. They show three different contracts: retrieving focused evidence, returning an answer only with support, and explicitly abstaining when no support exists.

^{Each card names its deterministic offline fixture and test scope. The examples are illustrative; they are not customer data or external benchmark results.}

Run python -m eval.chunking_eval and python -m eval.grounded to reproduce the behavior; the former measures evidence retrieval and context size, while the latter measures the answer-versus-abstain decision.

Open-source users: update regularly for the latest fixes and improvements.

Open-core boundary: this repository contains the free local engine, dashboard, MCP server, and customer-side clients. Hosted sync, analytics, automation, and team services run on the official hosted service; their server implementations are not distributed here.

Support continued Engraphis development with Pro. Start a 3-day Pro trial or subscribe to Pro.

Engraphis-Dashboard opens http://127.0.0.1:8700. Local memory needs no cloud account, signup, or API key and stays in a SQLite file on your machine.

Ledger is the primary local interface for recall, memories, graph exploration, provenance, workspaces, and manual consolidation. Classic preserves the former full tool suite; both use the same local data. Switch in Manage → Settings → Interface (Ledger) or Settings → Appearance & Engine (Classic).

Managed compute is separate from Cloud Sync. A connected installation may send a bounded, non-secret snapshot for a hosted proposal; the hosted service must read it to produce a proposal, so this is not end-to-end-encrypted processing. Local-only installations send nothing. Set ENGRAPHIS_MANAGED_COMPUTE_CONSENT=0 to opt out; ENGRAPHIS_RETENTION_SUPERVISOR=none keeps retention supervision local (the default).

Inspect memories, supersession diffs, recall scores, timelines, links, consolidation, and audit records in the dashboard. The offline graph renderer is vendored, and the interface is keyboard- navigable with light and dark themes.

Engraphis gives agents durable, scoped, explainable project knowledge. The local engine combines Ebbinghaus decay, bi-temporal facts, and hybrid vector/lexical/graph recall; it runs offline with SQLite, local embeddings, and numpy only.

• Grounded and governed: deterministic conflict resolution, cited answers or abstention, explicit correction/promotion/forgetting, and a complete history.
• Agent-ready: MCP tools, hard-budget context packets, handoffs, and code-aware retrieval.
• Auditable: content-free receipt chains, provenance, and temporal/entity/code relationships.
• Practical: local file and code ingest, optional PDF/OCR/transcription, and SQLCipher at rest.

The memory engine, embeddings, conflict resolution, and recall stay local without an LLM. An explicitly configured provider adds structured extraction, cited synthesis, consolidation, and retention supervision. Configure it in Settings → Connect an LLM; llm_structured validates facts, entities, relations, and keywords before storage, while failures fall back to local chunking. The activity view records outcomes, never keys, prompts, or raw provider responses.

Privacy boundary: text sent to an explicitly selected provider leaves the local process under that provider's terms. Use ENGRAPHIS_RETENTION_SUPERVISOR=none (the default) and the offline chunk extractor when ingestion must remain entirely local.

Engraphis ships typed and scoped memory, bi-temporal history, grounded recall, hybrid vector/lexical/graph retrieval, deterministic context packing, and MCP-native agent tools. The current deterministic offline regression fixtures reproduce these quality results:

| 73.0% less retrieved context | 3.8× smaller evidence record | 55.38% smaller MCP response | |---|---| | 808.8 → 218.4 tokens per question | 162.2 → 42.4 tokens to supporting evidence | 17,172 → 7,663 serialized tokens | | Same Recall@5 1.000 in the long-document fixture | Same 18 fixture questions returned an evidence-holding memory | Same CodeMem retrieval scores across 260 timed recalls |

What it means: agents carry less irrelevant history, leaving more room for the current task and cited evidence. These controlled, deterministic fixtures measure context, not model billing, task time, customer savings, or external-benchmark performance.

^{Each row uses a separate 100% baseline. The measurements have different counting boundaries and are not additive.}

The chunked mode returns the relevant passage instead of the whole document: 590.4 fewer tokens per question. Under the same model-context budget, that leaves roughly 590 tokens for task instructions or other relevant evidence.

The table below records every current token/context efficiency measurement and its counting boundary.

The compact MCP response avoids duplicating full memory bodies when the packed context and source list are enough. That can reduce what an agent must inspect or pass onward, but the fixtures do not measure model-provider charges, end-to-end task time, or customer cost savings.

The measures are deliberately separate and must not be added together: chunking counts the content of retrieved memory records before ContextPacker, whereas compact recall counts the serialized MCP response returned to a client. “Tokens to evidence” is the size of the smallest retrieved memory record holding the reference evidence; it is not latency or end-to-end answer accuracy. Chunking creates more focused stored records (24 chunks rather than 6 whole-document memories in this fixture), so this is a context-efficiency result, not a storage-reduction claim.

Reproduce the quality and token/context measurements without a network connection or API key:

python -m eval.harness --dataset eval/datasets/codemem.jsonl --k 5
python -m eval.grounded
python -m eval.chunking_eval
python -m eval.performance --dataset eval/datasets/codemem.jsonl --k 5 --iterations 10 --json

These are small deterministic correctness and efficiency fixtures, not official LoCoMo / LongMemEval QA scores or a third-party leaderboard result. Compact-response counts use the exact engraphis.regex.v1 counter; the chunking evaluation uses its documented deterministic normalized-character estimator. Chunking measures retrieved memory content, while compact recall measures serialized MCP response size. See BENCHMARKS.md for definitions, limitations, canonical external-evaluation requirements, and the no-unsupported-claims policy.

pip install "engraphis[all]"        # dashboard + MCP server + code graph + available platform extras
pip install "engraphis[server]"     # dashboard + REST API
pip install "engraphis[mcp]"        # MCP server only
pip install "engraphis[documents]"  # PDF + image OCR bindings
pip install "engraphis[transcription]" # faster-whisper audio/video
pip install "engraphis[postgres]"   # PostgreSQL schema introspection
pip install "engraphis[encryption]" # SQLCipher encryption-at-rest extra
pip install engraphis               # core library: numpy only, fully offline

The official Docker image includes the local Tesseract executable for image OCR. Outside Docker, the documents extra installs its Python bindings; install Tesseract through your operating system as well if you enable image OCR.

The NumPy-only core library supports Python 3.9+. Current patched releases of the WebUI stack, MCP SDK, and image parser require Python 3.10+, so use Python 3.10 or newer for the server, mcp, documents, or all installation paths. sqlcipher3-binary publishes CPython manylinux x86-64 wheels. On that target, engraphis[encryption] installs the driver. The cross-platform all extra deliberately omits it so all remains resolvable on macOS, Windows, Linux ARM, and musl; on those targets, provision a compatible SQLCipher driver separately before enabling a database key. Plaintext SQLite remains the explicit default on every platform.

Linux / macOS: if pip install fails with error: externally-managed-environment, your system Python is marked read-only (PEP 668). Install into a virtual environment instead. Run python3 -m venv venv && source venv/bin/activate && pip install "engraphis[server]" Alternatively, use Docker (docker compose up). pipx install "engraphis[server]" also works.

First run downloads all-MiniLM-L6-v2 (~80 MB). Without it, the engine falls back to a deterministic offline embedder so it always runs.

pip install "engraphis[server]"
engraphis-dashboard                   # → http://127.0.0.1:8700
engraphis-dashboard --install-shortcuts   # → Desktop + Start Menu icons

docker compose up                     # → http://127.0.0.1:8700

A fresh clone needs no .env: the default service runs engraphis-dashboard --no-open, stores the v2 database plus license state on a named volume mounted at /data, and accepts overrides from .env or the shell. The legacy v1 API is opt-in, requires a strong ENGRAPHIS_API_TOKEN, and uses a separate database so its incompatible schema cannot collide with the dashboard:

ENGRAPHIS_API_TOKEN='generate-a-strong-unique-value' docker compose --profile api up engraphis-api

Compose publishes both services on host loopback only. Set a strong ENGRAPHIS_API_TOKEN before changing either port mapping to a non-loopback host address.

Set ENGRAPHIS_API_TOKEN to require API authentication and ENGRAPHIS_DB_KEY to encrypt the local database at rest. Hosted-plan credentials configure customer clients; they do not install premium server implementations into this image. See docker-compose.yml for options.

pip install "engraphis[mcp]"
engraphis-init                     # writes .env + prints config snippets
claude mcp add engraphis -- engraphis-mcp
cmd mcp add engraphis -- engraphis-mcp  # Command Code CLI

Your agent now has 30 tools: remember, recall context (plus full, grounded, and proactive recall), proactive context, grounded answer alias, why, timeline, forget, pin, correct, promote, ingest, consolidate, index_repo, search/code path/impact/export, privacy receipts, PostgreSQL schema ingestion, link, record_event, start/end_session, stats, and check_update. See the MCP tools table below.

For unattended jobs, engraphis_start_session, engraphis_remember, and engraphis_record_event use workspace default when workspace is omitted.

pip install "engraphis[code]"
engraphis-graph index -w acme -r api --root .
engraphis-graph search -w acme -r api "UserService"
# `query`/`explain` blend code search with your stored memories: query matches symbol
# and file NAMES (a full question sentence won't match anything), and explain's answer
# is drawn from memories recorded against the repo; both are empty on a fresh index.
engraphis-graph query -w acme -r api "UserService"
engraphis-graph explain -w acme -r api "why does deploy depend on approval?"
engraphis-graph path -w acme -r api UserService DatabasePool
engraphis-graph impact -w acme -r api --root . --git-range origin/main...HEAD
engraphis-graph prs -w acme -r api --base main --head HEAD
engraphis-graph export -w acme -r api -o engraphis-graph-out
engraphis-graph install-merge-driver --root .

The export contains graph.json, a self-contained graph.html, and GRAPH_REPORT.md. Indexing supports Python, JavaScript, TypeScript, Go, Rust, Java, C#, C, C++, SQL, and Terraform. Tree-sitter is used when available; the dependency-free regex backend remains a functional fallback. Definitions, methods, calls, imports, ownership, variables, inheritance/implementation, and docstrings/comments are indexed. Indexing is incremental by content hash, honors .engraphisignore, and does not follow file symlinks outside the repository root. Call edges are name-based and best-effort rather than type-resolved. The optional Git merge driver validates bounded graph JSON and deterministically unions nodes and edges instead of choosing one export side.

For a read-only recall and graph API that can be shared without exposing write operations:

pip install "engraphis[server]"
engraphis-graph-server                 # API at http://127.0.0.1:8720; schema at /openapi.json

A non-loopback bind fails closed unless ENGRAPHIS_GRAPH_TOKEN (or ENGRAPHIS_API_TOKEN) is set. See the v3 architecture/design document.

from engraphis.service import MemoryService

mem = MemoryService.create("engraphis.db")
mem.remember("Auth migrated from JWT to PASETO.", workspace="acme", repo="api")
hit = mem.recall("why did we change auth?", workspace="acme", repo="api")
print(hit["context"])

The same MemoryService backs the dashboard and the MCP server.

For an agent prompt, prefer engraphis_recall_context: it returns one hard-budget packed context plus compact sources, deterministic usage accounting (budget_tokens, context_tokens, source_tokens, saved_tokens, savings_ratio, packed_count, omitted_count, and token_counter), and optional diagnostics. Accounting is exact for the named counter; inject the reader's tokenizer when reader-model token parity is required. engraphis_recall remains the compatible full-recall surface; use response_mode="compact" when the packed context is enough and full memory bodies would duplicate it. Both default to the balanced retrieval profile; auto remains opt-in.

For bi-temporal reads, valid_at selects what was true at a Unix timestamp and known_at selects what Engraphis had learned then. as_of remains a compatibility alias for valid_at; supplying both is allowed only when they match.

For a mutable claim, pass a stable subject_key and optional claim_kind, such as subject_key="api.rate_limit", claim_kind="configured_value". Matching claim identities make supersession deterministic; when similarity suggests a relationship but not a contradiction, Engraphis keeps both memories and returns op="relate".

Engraphis separates automatic write resolution from explicit human governance:

Manual N→1 merge is available through MemoryService.merge() and POST /api/merge:

a = mem.remember("Deploys happen Friday at 3pm.", workspace="acme")
b = mem.remember("We deploy Fridays around 15:00.", workspace="acme")

merged = mem.merge(
    [a["id"], b["id"]],
    "Deploys ship every Friday at approximately 15:00.",
    workspace="acme",
    reason="deduplicate the deployment schedule",
)
print(merged["compaction"])

All sources must belong to the named workspace. The result inherits the strictest source sensitivity, remains untrusted if any source was untrusted, and stays pinned if any source was pinned. The full multi-predecessor chain remains visible through inspection, Why, and Timeline.

The core engine, local dashboard, MCP server, and manual consolidation are Apache-2.0 and free. Pro and Team are services, not hidden modes in this package: a subscription authorizes the official hosted service, whose private control plane, relay, compute, billing, and Team identity run in a private repository. Pro is $10/mo ($100/yr); Team is $20/seat/mo ($200/seat/yr). The email-confirmed, no-card trial lasts exactly 3 active days.

After a denial, workspace_write_grace may retain only private-service-approved hosted-account continuity operations for at most 24 hours. It never extends trial or subscription expiry or grants Cloud Sync, Analytics, Automation, Auto Dreaming, Auto Consolidation, Team access, seats, or credentials. Then recovery_read_only provides recovery and data export. Neither state restricts local dashboard, MCP tools, or local writes. Cloud Sync encrypts eligible shared-workspace changes end-to-end; managed compute is a separate readable-snapshot service. See docs/LICENSING.md and docs/SYNC.md for the full boundaries.

Subscribe to Pro to support the project and add hosted services.

Memory relationships, extracted entities, and code structure stay normalized in one SQLite database. Edges are tagged as temporal, entity, causal, or semantic, so callers can select a logical overlay without maintaining separate graphs. Schema-v3 migration is additive and idempotent: existing memories and bi-temporal history remain in place, while legacy edge layers are inferred once.

MemoryService.intent_remember(), intent_link(), and intent_recall() provide a transport-neutral agent protocol while the existing engraphis_remember, engraphis_link, and engraphis_recall tools remain the canonical MCP vocabulary. Explicit links can persist both a layer and a durable rationale. Intent recall maps explain, summarize_history, and locate_code to appropriate layer filters; code intents also return matching symbols when a repository is supplied.

The operation-receipt chain is deliberately content-free. It records bounded operation metadata and chained hashes, while excluding raw memory/query text, workspace names, memory IDs, and actor identities from exported receipt payloads. Use engraphis_receipts, engraphis_verify_receipts, and engraphis_export_receipts to inspect the chain or compare it with a previously saved head/count anchor. A separately maintained local count/head anchor and persistent integrity marker make interior edits, reordering, and tail truncation detectable.

See the v3 architecture document for the data flow and SECURITY.md for the trust boundaries.

Cloud Sync is a hosted Pro/Team service. The private service owns relay storage, organization authorization, credential rotation, scheduling, isolation, and operations. This Apache package contains only the customer protocol, deterministic merge implementation, and one-shot client needed to participate after the hosted service authorizes an installation. No environment switch turns the public image into an Engraphis relay.

The merge remains a state-based CRDT: every field resolves by a commutative, idempotent rule so merge(A, B) == merge(B, A). The current format carries memories and memory-to-memory links; entity/code graph reconciliation is not yet part of sync. secret memories and all live or invalidated session-scoped memories are device-local and excluded from every exported sync bundle; links are exported only when both endpoints remain. Inbound bundles cannot create or overwrite session state. Cloud Sync encrypts eligible shared-workspace changes end-to-end before they leave this device; the relay stores ciphertext and cannot read bundle contents.

Cloud Sync fails closed without its client-held workspace key: install engraphis[cloud-sync] on Python 3.10+ and set the same 32-byte URL-safe-base64 ENGRAPHIS_SYNC_E2EE_KEY on each authorized device using a secure out-of-band transfer. The relay and Engraphis Cloud never receive that key. docs/SYNC.md includes the key-generation command and the --relay-e2ee-key one-off CLI alternative.

For development, backup interchange, and offline testing, the public client retains an explicit one-shot folder exchange. That manual primitive is not the official Cloud Sync product and has no hosted identity, seat, managed-storage, availability, or support guarantees. See docs/SYNC.md for the exact boundary, security model, and client usage.

The public runtime and its hosted-service clients enforce:

• Single-user local access: loopback is the default; an optional constant-time-checked bearer protects a remotely exposed customer node. Local Team accounts, invitations, roles, seats, password handling, and organization administration are not shipped here.
• Hosted authorization boundary: Cloud Sync, Analytics, Automation, Team identity, and cost-bearing work require current authorization from the private service. Any bounded workspace_write_grace and later recovery_read_only state is enforced by that private service for hosted account continuity; neither state grants cloud access or account growth, and neither restricts the free local core.
• SQLite transaction safety: shared v2 connections serialize complete write transactions; a failed statement that opened a transaction rolls it back and releases its lock. Legacy decay is frequency-independent, and sync preserves future bi-temporal validity horizons.
• Customer-client isolation: workspace allow-lists are enforced while applying fetched data, and device-local secret memories cannot be uploaded or remotely overwritten, invalidated, or downgraded. Bundle size and record counts are bounded before application; hosted tenant and storage enforcement remains private service responsibility.
• Hostile-input handling: sync-folder peers, graph merge inputs, repository walks, resource files, and PostgreSQL selectors are treated as untrusted; traversal, symlink/replace races, oversized/deep payloads, malformed rows, and non-finite JSON are rejected.
• Proxy and network hardening: default loopback CORS follows ENGRAPHIS_PORT; proxy-reported HTTPS produces Secure session cookies, and redirects use the configured dashboard URL rather than a caller-controlled Host header. Managed-service clients reject insecure or malformed endpoints and never forward bearer credentials across HTTP redirects.

See SECURITY.md for supported versions, deployment requirements, known gaps, and the vulnerability-reporting process.

Set ENGRAPHIS_DB_KEY (or ENGRAPHIS_DB_KEY_FILE) and install the extra:

pip install "engraphis[encryption]"

The entire main memory database file is transparently encrypted with AES-256 via SQLCipher; full-text search, the graph, and every query keep working unchanged. Customer authentication and managed-service state use their respective deployment protections. When a key is set for the main database, Engraphis fails loud rather than silently falling back to plaintext. Generate a strong key:

python -c "import secrets; print(secrets.token_hex(32))"

An existing plaintext database cannot be opened with a key: migrate it (dump → import into a fresh keyed DB). See .env.example for all encryption options.

Drag-and-drop or server-side import, access-controlled and bounded:

• Dashboard upload: accepts text, Markdown, code, JSON/CSV/HTML, DOCX, and exported Google Workspace documents directly; optional adapters add PDF text extraction, image OCR, and audio/video transcription. Native .gdoc pointer files contain no document body, so export them as DOCX, PDF, HTML, or plain text before local ingestion.
• Server-side folder import: MemoryService.import_folder() reads a directory on the machine running Engraphis. Large resources are chunked deterministically even when the configured extractor is none; path-traversal guards still apply.
• PostgreSQL: engraphis_ingest_postgres_schema, POST /api/resources/postgres, or engraphis-graph postgres converts tables, columns, constraints, and foreign keys into a schema memory and entity graph. The DSN is never persisted.
• MCP ingest: engraphis_ingest accepts raw text and applies the configured extractor (chunk, llm, or llm_structured); with none it stores one verbatim memory.
• Sub-file chunking: set ENGRAPHIS_EXTRACTOR=chunk to split long, multi-topic documents into retrieval-sized, structure-aware pieces (headings start new chunks; ~256-token target with sentence-level overlap) without an LLM. Each chunk becomes its own memory, so recall returns the relevant passage instead of a whole file, a big context-reduction win on long docs. Works across all three ingest paths (dashboard upload, import_folder, and engraphis_ingest). Measure the payoff with the bundled eval: python -m eval.chunking_eval --dataset eval/datasets/longdoc.jsonl --k 5 (whole-file vs. chunked, same recall pipeline, offline).
• Structured LLM extraction: ENGRAPHIS_EXTRACTOR=llm_structured validates typed facts, entities, relations, and keywords before storage. Its preserved entity/relation metadata feeds the knowledge graph automatically. A successful dashboard connection test enables this mode by default; the Settings switch can disable or re-enable it immediately.

Files imported through the dashboard or import_folder() are marked untrusted by default; MCP ingest remains an authenticated agent write.

Manual consolidation is free and remains local. Use the dashboard's Consolidate tab, MemoryService.consolidate, POST /api/consolidate, engraphis_consolidate, or python -m scripts.consolidate. Dry-run is the default.

Pro and Team add hosted Auto Consolidation and Auto Dreaming. On a connected workspace, the first Automation view automatically starts the recommended daily maintenance policy and uploads its bounded snapshot; there is no separate enable step. Customers can later pause or tune that policy. The public Automation tab displays reviewable jobs or proposals. The scheduling, analytics, dreaming, and consolidation automation algorithms run in Engraphis Cloud; this repository ships no premium background loop, cron wrapper, or worker.

Secret-class and session-scoped memories are excluded before a managed snapshot is serialized; secret-class rows are rejected again by the hosted service. The encoded payload is capped at 16 MiB. A connected installation sends that bounded, non-secret snapshot to Engraphis Cloud over HTTPS, where the hosted service must read it to produce a proposal; this is not end-to-end-encrypted processing. Local-only installations send nothing. Managed compute is enabled by default once an installation is connected to Engraphis Cloud. Connecting accepts the terms that cover it, and it stays off for a local-only installation with no cloud session; cloud entitlement is also required. ENGRAPHIS_MANAGED_COMPUTE_CONSENT=0 opts a connected installation back out. A managed proposal never silently rewrites the local database.

Manual consolidation can also use schema-validated LLM output through MemoryService.consolidate, POST /api/consolidate, engraphis_consolidate, or python -m scripts.consolidate --structured. Source memories remain live by default; supersede_sources / --supersede-sources closes them only after validated replacement facts are written.

All via environment (or .env):

See .env.example for the full customer-runtime and managed-service client options.

engraphis/
├── engraphis/
│   ├── core/                # v2 engine: interfaces, store, recall, scoring, schema, sync
│   ├── backends/            # pluggable embedder / vector index / reranker / codegraph / sync transports / encryption
│   ├── service.py           # validated MemoryService facade
│   ├── mcp_server.py        # MCP server: 30 tools
│   ├── dashboard_app.py     # dashboard WebUI (FastAPI)
│   ├── dashboard_assets/    # primary Ledger interface + graph engine
│   ├── classic_assets/      # selectable full operator dashboard backup
│   ├── read_only_api.py     # token-protected recall/repository-graph HTTP surface
│   ├── hosted_client.py     # hosted URLs, plan labels, and endpoint validation only
│   ├── licensing.py         # compatibility facade for hosted presentation metadata
│   ├── cloud_session.py     # rotating hosted customer-session client
│   ├── cloud_features.py    # consented managed-feature protocol client
│   ├── config.py / app.py   # env settings / REST server
│   └── static/              # compatibility dashboard asset paths
├── eval/                    # offline retrieval eval harness + datasets
├── tests/                   # pytest suite (300+ tests, offline numpy-only core)
├── scripts/                 # start_dashboard, inspector, cli, init, consolidate, sync
├── docs/                    # SYNC.md, KILO_CODE_INTEGRATION.md
├── Dockerfile / docker-compose.yml
└── pyproject.toml

New capability belongs in the v2 path (engraphis/core/, engraphis/backends/, and MemoryService) behind the interfaces in core/interfaces.py. The flat-namespace v1 server under engraphis/app.py, routes/, stores/, and engines/ remains a compatibility/reference surface; engraphis-dashboard, the MCP server, and the Python quickstart above use v2.

The offline quality gate (no network, no API key):

pip install numpy pytest ruff
python -m pytest tests/ -q
python -m eval.harness --dataset eval/datasets/sample.jsonl --k 5
python -m eval.harness --dataset eval/datasets/codemem.jsonl --k 5
python -m eval.ablation
ruff check .

Numbers, not assertions: the offline harness is a correctness floor (deterministic embedder). LoCoMo / LongMemEval adapters and the pinned LongMemEval-V2 reader profile are available for approved official evaluation runs: see BENCHMARKS.md.

Each tagged release includes release-evidence.json and a reproducible CycloneDX JSON SBOM as GitHub Release assets. The evidence binds the matching tag and commit to the built wheel and source distribution hashes, SBOM hash, source-input hashes, and the completed release-gate checks. It is intentionally limited: it does not attest to publication, hosted services, payments, deployments, or runtime data; the SBOM describes the build job's Python environment rather than an operating-system or container image.

Apache-2.0. See LICENSE and NOTICE. "Engraphis" is a trademark of the Engraphis project; the license does not grant trademark rights. Code already distributed under Apache-2.0 keeps that grant; later releases cannot retroactively withdraw it. The official hosted control plane, its production credentials and records, managed operations, support, and future separately delivered commercial modules are outside the public source grant. See docs/LICENSING.md for the complete boundary.