Show HN: Lians AI, Token-bounded memory and evidence for AI workflows

Lians AI has launched Lians, a cross-platform decision evidence and reconstruction layer for regulated AI workflows, providing token-bounded memory and evidence records. The platform captures and reconstructs agent decisions with content-addressed receipts, supporting Bedrock, Azure OpenAI, Anthropic, and open-source runtimes, and offers self-hosted and cloud options. It aims to address compliance needs in financial, medical, and legal environments by ensuring point-in-time knowledge and auditability.

Website https://www.lians.ai/ - Docs https://github.com/Lians-ai/Lians/tree/master/docs - Install /Lians-ai/Lians/blob/master/docs/install.md - Quickstart https://github.com/Lians-ai/Lians self-hosted-quickstart - Star Lians Reproducible benchmark evidence and offline quality gates RIAD-1: decision reconstruction benchmark /Lians-ai/Lians/blob/master/docs/benchmarks/riad-1.md · CI receipts Lians https://github.com/Lians-ai/Lians is the cross-platform decision evidence and reconstruction layer for regulated AI . It gives compliance, model-risk, and operational-risk teams one record of what an agent knew, what it retrieved, which policy governed it, which tools ran, who reviewed it, and what changed later. The durable moat is neutrality. A firm can run agents across Bedrock, Azure OpenAI, Anthropic direct, and open-source runtimes while keeping one portable evidence record outside every provider. Every write is preserved as a governed temporal record and compiled into a typed memory artifact. Every recall can run in fast , deep , or reconstruct mode and returns a content-addressed receipt that can bind automatically to a Decision Envelope. See decision evidence and reconstruction /Lians-ai/Lians/blob/master/docs/decision-evidence.md , the normative completeness grades /Lians-ai/Lians/blob/master/docs/completeness-grades.md , Evidence Pack signing key custody /Lians-ai/Lians/blob/master/docs/evidence-signing-key-custody.md , the governed memory engine /Lians-ai/Lians/blob/master/docs/memory-engine.md and reproducible evidence gates /Lians-ai/Lians/blob/master/docs/benchmarks/README.md . The platform exposes one evidence workflow: Capture : open a Decision Envelope and bind memory, traces, policy decisions, prompts, tools, and human review as the action happens. Reconstruct : reproduce the point-in-time knowledge and execution path even when exact deterministic replay is impossible. Verify : grade every decision as Recorded, Reconstructable, Verifiable, or Replayable, with every missing requirement named. Monitor : when a source, policy, or model changes, identify every exposed decision and emit a blast-radius alert. Memory remains a core evidence source and performance primitive. It is not the commercial category by itself. | Library | Self-Hosted Server | Cloud | | |---|---|---|---| Best for | Testing, prototyping | Regulated teams, private deployments | Zero-ops production early access | Setup | pip install lians-sdk local | docker compose up --build | pip install lians-sdk + API key | Database | SQLite zero setup | Postgres 16 + pgvector | Managed | Audit chain | Yes | Yes | Yes | Crypto-shred erasure | Yes | Yes | Yes | Information barriers | Local checks | PostgreSQL RLS | Managed policy | Air-gap capable | No | Yes | No | Lians gives agents a durable memory loop across facts, context, decisions, outcomes, and reviewed lessons. The Memory product keeps context current and useful; the Records product captures behavior and oversight in an open, verifiable event format. Most memory layers stop at storage and retrieval. Lians is built for teams that also need to know what the agent knew, when it knew it, where the fact came from, which outcomes followed, who was allowed to see it, and whether stale or erased content was kept out of future context. That is the gap between a memory demo and a memory system teams can trust in production, especially in financial, medical, and legal environments. Generic agent memory optimizes for personalization and recall. Regulated agent memory has a different job: it must keep the agent's context correct, current, segregated, reproducible, and defensible under review. Lians is designed for the failure modes that matter in institutions: Stale fact contamination - old rates, old guidance, old medication doses, old damages estimates, or old client facts must not silently enter context. Point-in-time reconstruction - an examiner, clinician, partner, or risk committee may ask what the agent knew at a specific timestamp. Information barriers - one desk, care team, or matter team must not read another team's memory because of an application-layer bug. Erasure with audit survival - private content must be removable without breaking custody records, audit hashes, or legal retention evidence. Relational compliance checks - conflicts of interest, related-party exposure, and referral networks are graph questions, not plain vector search. The short competitive frame: Runtime vendors explain their own cloud. Lians preserves portable decision evidence across all of them. | Vertical | What Lians proves | Product primitives | |---|---|---| Financial institutions | No stale or future facts influenced a decision; desk barriers held; audit state is reconstructable | Bitemporal recall, backtest contamination checks, SEC/FINRA audit export, RLS information barriers, related-party graph paths | Healthcare organizations | PHI access is scoped; care-team memory is reconstructable; patient erasure is provable | Per-subject encryption, crypto-shred certificates, HIPAA safeguard mapping, care-network graph, air-gap mode | Legal institutions | Matter walls held; privilege cutoffs are reproducible; chain-of-custody survives erasure | Matter-level barriers, recall at for privilege dates, audit reconstruction, conflict-of-interest graph paths | Procurement and technical review materials: Institutional proof kit /Lians-ai/Lians/blob/master/docs/institutional-proof-kit.md Vertical pitch guide /Lians-ai/Lians/blob/master/docs/verticals.md Competitive landscape /Lians-ai/Lians/blob/master/docs/competitive-landscape.md Security whitepaper /Lians-ai/Lians/blob/master/docs/security-whitepaper.md SOC 2 / HIPAA readiness /Lians-ai/Lians/blob/master/docs/soc2-hipaa-readiness.md Threat model /Lians-ai/Lians/blob/master/docs/threat-model.md Production deploy checklist /Lians-ai/Lians/blob/master/docs/deploy.md Lians is listed on the official MCP Registry https://registry.modelcontextprotocol.io/?q=io.github.ebeirne%2Flians . Any MCP-compatible host - Claude Desktop, Cursor, VS Code, Windsurf, and others - can use local persistent memory immediately or connect to a hosted Lians server. No SDK code, custom adapter, Docker service, URL, or API key is required for local mode. Your agents get eight tools automatically: | Tool | What it does | |---|---| remember | Store a fact with event time and metadata | recall | Retrieve current non-stale facts by semantic query | recall at | Point-in-time recall — what did we know on date X? | reconstruct | Full audit reconstruction for regulatory submissions | list conflicts | Surface facts where two sources disagree | memory lineage | Full supersession history of any fact | fact history | Time-series view of a ticker+metric e.g. AAPL EPS | backtest check | Detect lookahead bias before a backtest runs | Add to your claude desktop config.json or equivalent MCP config : { "mcpServers": { "lians": { "command": "uvx", "args": "--from", "lians-sdk mcp ", "lians-mcp" } } } Restart your client and Lians memory tools appear immediately. Local mode persists to ~/.lians/mcp.db . To use a hosted deployment instead, set LIANS URL , LIANS API KEY , and optionally LIANS AGENT ID . uvx --from 'lians-sdk mcp ' lians-mcp No environment variables are needed for local mode. Set LIANS URL , LIANS API KEY , and optionally LIANS AGENT ID to use a remote server. pip install lians-sdk local SQLite plus real local semantic embeddings, no Docker python from lians import LocalLiansClient from datetime import datetime, timezone mem = LocalLiansClient mem.add agent id="analyst-1", content="NVDA FY2026 revenue guidance raised to $40B", event time=datetime 2025, 11, 19, 16, tzinfo=timezone.utc , metadata={"ticker": "NVDA", "metric": "revenue guidance"}, Superseded facts are excluded at the DB layer — never reach the LLM results = mem.recall agent id="analyst-1", query="NVDA revenue guidance" Deeper multi-facet recall for planning and research results = mem.recall agent id="analyst-1", query="What changed in the guidance and why?", mode="deep", Point-in-time: what did we know on March 1? compliance-grade answer results = mem.recall at agent id="analyst-1", query="NVDA revenue guidance", as of=datetime 2025, 3, 1, tzinfo=timezone.utc , Every result includes receipt sha256, provenance coverage, and the resolved serving mode and latency budget. Switch to the hosted server with one line: from lians import LiansClient as LocalLiansClient python from datetime import datetime, timezone from lians import AsyncLiansClient async with AsyncLiansClient base url=LIANS URL, api key=LIANS API KEY as lians: envelope = await lians.open decision envelope agent id="underwriter-1", decision type="credit application", regime="ECOA REG B", completeness profile="regulated recordkeeping", knowledge as of=datetime.now timezone.utc , context = await lians.recall agent id="underwriter-1", query="verified applicant income", decision envelope id=envelope "id" , sealed = await lians.seal decision envelope envelope "id" , outcome="manual review", decided at=datetime.now timezone.utc , input hash=INPUT SHA256, output hash=OUTPUT SHA256, No overclaiming: every missing requirement names the grade it blocks. print sealed "completeness" LiansMemoryHarness wraps the two operations every memory-augmented agent needs — recall-before and remember-after — into one object, with the compliance scoping subject, source, event-time, information barrier regulated deployments require. Works with any sync client LiansClient or LocalLiansClient and any model. python from lians import LiansClient, LiansMemoryHarness harness = LiansMemoryHarness mem, agent id="research-desk", domain="finance" One call: recall context, run your model, persist the response. answer = harness.run turn "What is NVDA's current revenue guidance?", generate=lambda context, query: call model f"{context}\n\nUser: {query}" , Or control each step: context = harness.recall context "NVDA revenue guidance" ready to inject harness.remember "Desk note: guidance now $40B" write after the turn Regulated scoping ties every write to one data subject and an information barrier: harness = LiansMemoryHarness mem, agent id="care-team-3", subject id="MRN-00042", per-subject key — the crypto-shred target barrier group="oncology", information-barrier tag domain="healthcare", Runnable end-to-end demo: agentmem/examples/harness demo.py /Lians-ai/Lians/blob/master/agentmem/examples/harness demo.py . Some compliance checks are graph queries. Lians stores bitemporal relationship edges alongside facts — same audit chain, same information barriers, no graph database — so you can answer them point-in-time: Legal — conflict-of-interest reachability ABA 1.7/1.9 : is an attorney connected to an adverse party? Finance — related-party / beneficial-ownership SEC, AML/KYC : is a counterparty within N hops of a restricted entity? Healthcare — care-network / referral-pattern anti-kickback analysis. mem.relate "analyst-1", src entity="Attorney", rel type="represented", dst entity="ClientX", event time=datetime 2026, 1, 1, tzinfo=timezone.utc mem.relate "analyst-1", src entity="ClientX", rel type="adverse to", dst entity="PartyY", event time=datetime 2026, 1, 1, tzinfo=timezone.utc Conflict-of-interest check — is there a connection, and through what? path = mem.path "analyst-1", src entity="Attorney", dst entity="PartyY" → {"connected": True, "hops": 2, "path": ... } Point-in-time: who was connected on the day of the trade? mem.neighbors "analyst-1", entity="FundA", depth=2, as of=datetime 2025, 6, 1, tzinfo=timezone.utc Graph-proximity reranking — boost recalls about entities near an anchor mem.recall near "analyst-1", query="earnings", near entity="FundA", near key="ticker" Endpoints: POST /v1/graph/relate · /v1/graph/unrelate · /v1/graph/extract text → edges, rule-based or opt-in LLM · GET /v1/graph/neighbors · /v1/graph/path all as of -capable . Inspired by Zep/Graphiti /Lians-ai/Lians/blob/master/docs/compare-zep.md , built on our compliance spine. Give any coding agent persistent, compliance-grade memory: | Host | How | |---|---| Claude Code | Plugin with slash commands /lians-remember , /lians-recall , /lians-audit , /lians-integrate and a compliance subagent — integrations/lians-plugin | Codex | Drop-in AGENTS.md + MCP config — integrations/codex | Skills standard | npx skills add https://github.com/Lians-ai/Lians --skill lians — works in Claude Code, Codex, Cursor — skills/ | Any MCP host | One-time config; eight native memory tools — see | Institutional AI agents accumulate facts that change over time : rate decisions supersede prior ones, guidance gets revised, medication doses change, care plans evolve, damages estimates move, and matter facts are corrected during discovery. Systems that return every version with equal rank contaminate the LLM context with stale facts. Lians fixes this with a bitemporal model: event time — when the fact happened business time valid from / valid to — when it was known system time Superseded facts are excluded at the database layer. Every write is recorded in a tamper-evident SHA-256 hash chain; physical immutability and SEC 17a-4 deployment claims require separately configured WORM storage and policy controls. Per-subject keys can be destroyed for governed erasure while the audit trail survives. Information barriers are enforced at PostgreSQL RLS, not only at the application layer. Temporal memory is no longer unique: Graphiti documents a bitemporal knowledge graph, Mem0 documents temporal reasoning and history, Hindsight documents query-time temporal recall and audit controls, and Supermemory documents content versioning and a temporal graph. Lians should be evaluated on the compound decision-evidence boundary it implements: - reconstruct a named decision at both event-time and knowledge-time cutoffs; - enumerate the source versions included and excluded at those cutoffs; - detect post-cutoff leakage before a result is accepted; - emit a content-addressed Evidence Pack that can be verified offline; and - preserve the surrounding chain when subject content is crypto-erased. The repository's regulated-memory harness is useful product evidence, not an independent general-product leaderboard. Current leadership language remains gated on production load, isolation, restore, failure-injection, public benchmark, and independent-reproduction evidence. See docs/competitive-landscape.md /Lians-ai/Lians/blob/master/docs/competitive-landscape.md and the runnable claim policy in agentmem/benchmarks/release claims.py /Lians-ai/Lians/blob/master/agentmem/benchmarks/release claims.py . → Lookahead-bias demo — the same agent backtest with naive vs point-in-time retrieval Sharpe 4.6 vs −0.6, every leak logged : ebeirne/lookahead-bias-demo https://github.com/ebeirne/lookahead-bias-demo · in-repo /Lians-ai/Lians/blob/master/demo/lookahead-bias/README.md → Full benchmark numbers: docs/benchmark.md /Lians-ai/Lians/blob/master/docs/benchmark.md → Regulated-eval head-to-head five compliance invariants, Lians 5.0 / Zep–Graphiti 2.0 / mem0 0.5 : docs/regulated-eval-results.md /Lians-ai/Lians/blob/master/docs/regulated-eval-results.md — Lians, Graphiti OSS, and mem0 OSS all executed live in their default configurations per-cell evidence in the appendix ; remaining columns scored from their public API surface via runnable adapters you can re-run with keys. Lians maintains client implementations across five languages . Public package versions currently differ by ecosystem; use the explicit coordinates below and verify the machine-readable published release status /Lians-ai/Lians/blob/master/docs/published-release-status.json . | Language | Install | Client | Docs | |---|---|---|---| Python 0.4.2 | pip install lians-sdk==0.4.2 | from lians import LiansClient | | TypeScript / Node 0.4.0 npm install @lians-ai/lians@0.4.0 import { LiansClient } from "@lians-ai/lians" sdk/typescript /Lians-ai/Lians/blob/master/agentmem/sdk/typescript Go 0.4.1 go get github.com/Lians-ai/Lians/agentmem/sdk/go@v0.4.1 lians.NewClient url, key sdk/go /Lians-ai/Lians/blob/master/agentmem/sdk/go Java 0.4.1 JVM 11+ ai.lians:lians-sdk:0.4.1 Maven Central new LiansClient opts sdk/java /Lians-ai/Lians/blob/master/agentmem/sdk/java C 0.4.1 C99 + libcurl v0.4.1 source tag lians client new ... sdk/c /Lians-ai/Lians/blob/master/agentmem/sdk/c → One-page install + 30-second quickstart for every language: docs/install.md All five cover core memory operations. Python and TypeScript currently expose a broader advanced surface than Go, Java, and C; verify the client you plan to use against the OpenAPI contract before a pilot. | Framework | Install | Import | |---|---|---| LangChain | pip install lians-sdk langchain | from lians.langchain integration import LiansChatHistory, build tools | LangGraph | pip install lians-sdk langgraph | from lians.langgraph integration import create recall node, create remember node | CrewAI | pip install lians-sdk crewai | from lians.crewai integration import build crewai tools | OpenAI Agents SDK | pip install lians-sdk openai-agents | from lians.openai agents integration import build openai agent tools | AutoGen v0.4 | pip install lians-sdk autogen | from lians.autogen integration import build autogen tools | TypeScript / Node | npm install @lians-ai/lians | import { LiansClient } from "@lians-ai/lians" | git clone https://github.com/Lians-ai/Lians.git && cd Lians/agentmem cp .env.demo .env docker compose up --build -d python scripts/seed demo.py prints a demo API key; open demo/index.html Deploy to Fly.io, Kubernetes, or bare Docker: docs/deploy.md /Lians-ai/Lians/blob/master/docs/deploy.md python All three clients share the same API surface from lians import LiansClient sync, connects to hosted/self-hosted server from lians import AsyncLiansClient async, for FastAPI / async frameworks from lians import LocalLiansClient local SQLite, no server needed client.add agent id, content, event time, metadata={}, importance=0.5 client.add from messages agent id, messages= {"role": "user", "content": "..."} client.recall agent id, query, k=5 client.recall at agent id, query, as of=datetime ... point-in-time client.snapshot agent id, as of=datetime ... full state export client.backtest check agent id, simulation as of=... lookahead-bias detection client.erase subject id, request ref GDPR crypto-shred ┌──────────────┐ │ LLM / Agent │ └──────┬───────┘ │ REST / MCP ┌───────────▼────────────┐ │ Lians API │ FastAPI · rate-limit · OTEL └──┬────────────────┬────┘ ┌───────▼──────┐ ┌──────▼───────┐ │ memories │ │ event log │ │ encrypted │ │ hash chain │ │ bitemporal │ │ append-only │ └───────┬───────┘ └──────────────┘ │ ┌───────▼───────┐ │ subject keys │ AES-256-GCM per subject │ crypto-shred │ destroy key = content unrecoverable └───────────────┘ Postgres 16 + pgvector HNSW Redis recall hot cache Recall pipeline: BM25 + cosine Voyage Finance-2 → recency decay → validity gate valid to IS NULL for present; valid from ≤ as of < valid to for point-in-time Supersession pipeline: Stage 1 metadata key overlap → Stage 2 deterministic: SUPERSEDES / CONFIRMS / ADDS → Stage 3 optional LLM adjudication for paraphrase detection | Variable | Default | Description | |---|---|---| EMBEDDING PROVIDER | local | voyage · openai · sentence-transformers · local | VOYAGE API KEY | — | Required when EMBEDDING PROVIDER=voyage | MASTER ENCRYPTION KEY | — | Base64 32-byte key; blank disables PII encryption | KMS PROVIDER | env | env · aws · azure · vault | ADMIN SECRET | — | Protects /v1/admin/ — change in production | SUPERSESSION LLM STAGE | false | Enables Stage 3 LLM adjudication Claude Haiku | AIRGAP MODE | false | Hard-fails at startup if any config would send data externally | ADMISSION MODE | monitor | Admission control: off · monitor tag+audit · enforce reject injection/blocked source, hold PII/PHI/MNPI for review | SIEM URL | — | Stream every audit event to a SIEM collector Splunk HEC / Datadog / Elastic | WORM MODE | false | Attest write-once-read-many storage for SEC 17a-4 object-locked audit, no UPDATE/DELETE on event log | STRIPE API KEY | — | Enables per-namespace usage metering | Full reference: agentmem/.env.example /Lians-ai/Lians/blob/master/agentmem/.env.example | Method | Path | Description | |---|---|---| POST | /v1/memories | Add a memory admission control; supersession check; Idempotency-Key for exactly-once retries | GET / POST | /v1/admissions · /{id}/resolve | Review queue for held writes PII/PHI/MNPI — approve / reject | POST | /v1/memories/batch | Batch ingest | POST | /v1/recall | Hybrid BM25+cosine recall; optional as of , MMR rerank filters. rerank=mmr | POST | /v1/context | Token-budgeted, ready-to-inject context block point-in-time + MMR aware | POST | /v1/erase | GDPR crypto-shred by subject id | GET | /v1/audit/reconstruct | Reconstruct agent state at any past date | GET | /v1/admin/audit/verify | Verify SHA-256 hash chain integrity | GET | /v1/admin/audit/export | Export audit log SEC/FINRA/CFTC | GET | /livez | Liveness probe cheap; process up | GET | /readyz · /health | Readiness / deep health check DB + Redis | Interactive docs: http://localhost:8000/docs pip install -e ". dev " python scripts/test all.py Benchmarks only no API keys required PYTHONPATH=agentmem/src python -m pytest \ agentmem/tests/test supersession benchmark.py \ agentmem/tests/test recall quality.py -v See docs/testing.md /Lians-ai/Lians/blob/master/docs/testing.md for the six named invariants temporal soundness, audit immutability, erasure, etc. . Built to run in a regulated production environment, not just to demo: Exactly-once writes — Idempotency-Key on POST /v1/memories ; the SDKs send a stable key automatically, so a retried write never duplicates. Resilient clients — built-in retry with exponential backoff on transport errors / 5xx / 429. Kubernetes probes — cheap /livez liveness and deep /readyz readiness , so a dependency blip doesn't restart healthy pods. Rate limiting — per-API-key sliding window Redis , fails open. Access control — namespace-scoped keys, read / write / admin scopes, RBAC roles owner / analyst / compliance / readonly , and SSO via gateway forward-auth. DB-layer information barriers — RESTRICTIVE PostgreSQL RLS, proven in CI against a non-superuser role. Run the app as a non-superuser DB role — superusers bypass RLS. Memory admission control — govern what's allowed into memory: PII/PHI/MNPI detection, source-trust, prompt-injection quarantine, and a high-risk review queue ADMISSION MODE . No other memory layer does this. SIEM streaming — every audit event forwarded to Splunk HEC / Datadog / Elastic SIEM URL , fire-and-forget. Observability — Prometheus metrics + Grafana, OpenTelemetry traces, JSON access logs with a request ID. Evaluation — a judge-free memory-eval harness agentmem/benchmarks/memory eval.py in the LoCoMo/LongMemEval shape. Security & procurement docs: security-whitepaper.md /Lians-ai/Lians/blob/master/docs/security-whitepaper.md · threat-model.md /Lians-ai/Lians/blob/master/docs/threat-model.md · soc2-hipaa-readiness.md /Lians-ai/Lians/blob/master/docs/soc2-hipaa-readiness.md · sso.md /Lians-ai/Lians/blob/master/docs/sso.md · publishing.md /Lians-ai/Lians/blob/master/docs/publishing.md | Requirement | Feature | |---|---| | SEC 17a-4 tamper-evidence | SHA-256 hash chain on every audit row | | FINRA 4511 recordkeeping | Append-only event log | | GDPR Art. 17 erasure | AES-256-GCM per-subject keys; crypto-shred | | MiFID II point-in-time | Bitemporal: event time + valid from/valid to | | Information barriers | barrier group column; PostgreSQL RLS | | HIPAA §164.312 | Per-subject encryption, audit controls, transmission security | Scope of these claims:Lians provides thetechnical controlsmapped above — it is software, not a certification. Regulatory compliance is a property of your deployment and organization retention configuration, policies, attestations such as SOC 2 or a HIPAA assessment , and several controls require operator configuration WORM object-lock, non-superuser DB role, KMS . Every claim links to the doc that says exactly what is and isn't covered — start with soc2-hipaa-readiness.md . Full documentation: compliance.md /Lians-ai/Lians/blob/master/docs/compliance.md · hipaa.md /Lians-ai/Lians/blob/master/docs/hipaa.md · security-whitepaper.md /Lians-ai/Lians/blob/master/docs/security-whitepaper.md · threat-model.md /Lians-ai/Lians/blob/master/docs/threat-model.md · soc2-hipaa-readiness.md /Lians-ai/Lians/blob/master/docs/soc2-hipaa-readiness.md · sso.md /Lians-ai/Lians/blob/master/docs/sso.md · worm-storage.md /Lians-ai/Lians/blob/master/docs/worm-storage.md Access control: namespace-scoped API keys with read / write / admin scopes and RBAC roles owner / analyst / compliance / readonly ; SSO via gateway forward-auth any OIDC/SAML IdP . Lians is open-source and fully self-hostable — the entire feature set, including every compliance primitive, is in this repository under Apache 2.0. Paid packages sell deployment support, hardening review, and evidence packets around the open core, not license keys. A managed cloud is in early access for customers whose compliance posture allows hosted processing contact us ; regulated buyers should choose the package by deployment boundary and evidence requirements, not by a consumer-style monthly tier. | Package | Best for | Deployment | Commercial model | |---|---|---|---| Developer | Local prototypes, benchmarks, integrations | Local library or single-node server | Free / usage-based | Team | Internal pilots and non-production agent workflows | Docker or small Kubernetes deployment | Usage-based or team plan | Regulated Production | Sensitive, audited, time-dependent agent workloads | Customer cloud, private VPC, or on-prem | Annual contract | Enterprise / Air-Gap | Banks, hospitals, law firms, insurers, government | Private cloud, on-prem, or air-gapped | Custom annual contract | Managed Cloud | Zero-ops production where hosted processing is approved | Lians-managed environment | Contract or usage-based | Healthcare customers require an executed BAA before PHI is processed in a managed environment. Financial and legal customers may require customer-managed keys, private networking, regional residency, dedicated environments, or air-gapped deployment. Full packaging documentation: docs/pricing-tiers.md /Lians-ai/Lians/blob/master/docs/pricing-tiers.md and docs/billing.md /Lians-ai/Lians/blob/master/docs/billing.md Switching from another system? Migrate from mem0 /Lians-ai/Lians/blob/master/docs/migrate-from-mem0.md or Migrate from Zep CE /Lians-ai/Lians/blob/master/docs/migrate-from-zep.md Apache 2.0 — see LICENSE /Lians-ai/Lians/blob/master/LICENSE .