ContextLattice
ContextLattice is a local-first memory orchestration system for AI systems. It offers the following capabilities:
Health Check: Query the orchestrator's health status to verify it's operational.
Write Memory: Store new memory items by providing a project name, file name, content, and optional topic path for hierarchical organization.
Search Memory: Search contextual memory entries by project and query, with optional filters for agent ID, topic path, grounding info, and retrieval debug details.
Durable Storage: Orchestrates memory writes with outbox fanout to specialized sinks (e.g., Qdrant, Mongo, MindsDB, Letta) targeting 100+ messages/second throughput.
Intelligent Retrieval: Multi-source recall with result merging, ranking, and a learning loop for continuous improvement.
Code Context Enrichment: Reranks code context based on symbol overlap, file-path proximity, and recency.
Agent Task Management: Queue, route, and manage task lifecycles (create, status, replay, recover leases) for external/internal agent runners.
Context Expansion: Dynamically expands agent context with budgeted layers (factual snippets, topic rollups, raw file refs) and async deep escalation.
Telemetry & Maintenance: Access fanout/retention telemetry, clean up low-value memory, and purge telemetry data.
Security Controls: Enforce secret storage policies (redaction, blocking, or allowing) with API key authentication.
Web3 Integration: Supports Web3 messaging surfaces like IronClaw, OpenClaw, and ZeroClaw.
ContextLattice
What ContextLattice Does
ContextLattice provides a single memory contract for agentic systems:
Unified write/read contract for memory and context.
Durable fanout across retrieval/storage lanes.
Staged retrieval (fast now, deep continuation when needed).
Agent sessions that turn prior work, objective lineage, graph touches, skills, checkpoints, and handoffs into prompt-ready packages and exportable run cards.
Go/Rust runtime ownership for the active application path.
Legacy Python runtime archived under
archive/services/orchestrator_legacy_pythonfor tooling/test compatibility only.Local-first deployment with optional hosted surfaces.
Related MCP server: copilot-memory-store
Current Public Baseline
v3.10.2 is the current public agent operating-layer baseline: Go-native feedback submit, durable memory writes, agent-actionable installation, optional Pi/Droid task-runner adapters, detected OMP/Mercury instruction hooks, bounded runner-quality telemetry, advisor-only runner recommendations, tokenizer-exact prompt economics, bounded token-impact ledger persistence, Context Pack Quality Ledger, observed outcome telemetry, dashboard visibility, universal adapter lifecycle, native agent sessions, objective runtime state, scoped context packs, async recall steering, impact-per-token context allocation, Skills Index discovery, runtime policy, template conformance, release installers, Homebrew tap metadata, storage-governance hardening, and local session-store diagnostics behind one local contract.
Public Runtime Stack (v3.10)
Ingress:
gateway-go.Core memory + retrieval lanes: Go + Rust services.
Retrieval policy: staged fast-return, async continuation lifecycle, and impact-per-token allocation.
Primary interface: CLI helpers such as
contextlattice_adopt,contextlattice_agent_adapter,contextlattice_pack,contextlattice_checkpoint,contextlattice_agent_session, andcontextlattice_runner_quality.Companion surfaces: dashboard for visibility, HTTP for app integration, and MCP-compatible clients for host/harness integrations.
Single-container lite builds (
Dockerfile.hf-lite) also rungateway-go(no Python runtime dependency).Public single-container lite vector default:
topic_rollupsonly.Public local lite core default:
topic_rollups + qdrant; pgvector and memory-bank spike adapters are not started by default.Public local lite advanced: optional public adapter lab via
gmake mem-up-lite-advanced; not the default quickstart and not a paid/private boundary.Full/operator stacks: Qdrant remains the primary vector-native lane; pgvector stays supported for SQL-co-located vector workloads.
Quickstart
Agent Quickstart: Install + Integrate
If you are an AI agent reading this README because a user asked you to install or integrate ContextLattice, follow this section directly. Do not wait for the user to copy another prompt back to you.
Human users can also paste the block below into Codex, Claude Code, Hermes, OpenCode, OMP, Mercury, or another terminal-capable agent.
You are integrating ContextLattice.
Repository:
https://github.com/sheawinkler/ContextLattice.git
Goals:
- Install and launch ContextLattice locally.
- Verify the runtime is healthy.
- Integrate the target repo with ContextLattice-supported agent profiles.
- Do not install third-party agent harness binaries unless the user explicitly asks.
Important:
- `contextlattice_adopt integrate` writes managed instruction blocks for supported profiles.
- Installer flows may add managed instruction hooks for detected OMP and Mercury user-instruction files; they do not install OMP, Mercury, Codex, Claude Code, Hermes, Hermes Ultra, Pi, Droid, or other agent binaries.
- Optional Pi/Droid runner execution requires their CLIs (`brew install pi-coding-agent`, `brew install --cask droid`).
- Preserve existing user text in repo instruction files.
- If you are already inside a ContextLattice checkout, do not clone a duplicate repo.
Install ContextLattice:
git clone https://github.com/sheawinkler/ContextLattice.git
cd ContextLattice
cp .env.example .env
gmake quickstart
Verify ContextLattice:
curl -fsS http://127.0.0.1:8075/health | jq
contextlattice_adopt status --pretty
Integrate the target repo:
cd /path/to/target/repo
contextlattice_adopt integrate --repo . --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --pretty
contextlattice_adopt integrate --repo . --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --check --pretty
contextlattice_doctor --repo . --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --skip-provider-smoke --pretty
If any step fails:
- Report the exact failing command and path.
- Fix only the local setup issue needed for that failure.
- Rerun the check.
- If ContextLattice is unreachable, continue in degraded-memory mode and say so explicitly.For the full reusable agent contract, see docs/public_overview/templates/agents/universal.md.
1) Clone and configure
git clone git@github.com:sheawinkler/ContextLattice.git
cd ContextLattice
cp .env.example .env2) Launch (recommended)
gmake quickstartgmake quickstart prompts for runtime profile and then launches the selected stack.
3) Verify
curl -fsS http://127.0.0.1:8075/health | jq
scripts/agent/agent-runtime-proof-pack --pretty
scripts/agent/agent-adoption-proof-matrix --skip-provider-smoke --progress --prettyExpected:
/healthreturns{"ok": true, ...}agent-runtime-proof-packcompletes bootstrap, scoped recall, checkpoint, handoff, completion, status, prompt context package, and runtime telemetry phases.agent-adoption-proof-matrixverifies configured agent profiles and reports the skills, context, session, graph, and handoff evidence shaping each run, with trace commands for run-card export.
Model Runtime
Task inference defaults to ORCH_INFER_PROVIDER=auto. gateway-go detects the host profile and probes local backends before selecting a route.
Apple Silicon default priority:
mlx,vllm-metal,ane_sidecar,llama-cpp,ollama.CUDA/ROCm default priority:
sglang,vllm,openai-compatible,llama-cpp,lmstudio,ollama.Generic CPU default priority:
openai-compatible,llama-cpp,lmstudio,ollama.Supported provider ids include
sglang,vllm,vllm-metal,mlx,mtplx(alias for MLX),openai-compatible,lmstudio,llama-cpp,tgi,tensorrt-llm,ane_sidecar, andollama./v1/inference/runtime-policyreturns live provider health plus resource-aware model guidance. If host memory/VRAM is not identifiable, it falls back to generic local advice: start with Q4/IQ4 7B-9B models, benchmark, then scale up.The current opt-in local model shortlist lives in
docs/runtime/local-model-options.md; it includes small/medium MLX, GGUF, and HF/safetensors candidates plus frontier-provider connection guidance. GGUF models use an external llama.cpp-compatible connector; ContextLattice does not start or bundle llama.cpp in Lite.Large Qwen3.6 Dream Mode models are opt-in only; ContextLattice does not bundle or pull them by default. The default GGUF recommendation is
mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-MTP-GGUFfor llama.cpp-compatible advanced users. Abliterated variants are private-eval only behindCONTEXTLATTICE_DREAM_ALLOW_PRIVATE_EVAL_MODELS=true(GO_DREAM_ALLOW_UNCENSORED_MODELS=trueremains a legacy alias).Inference runtimes must emit final assistant content through their API. Reasoning-only responses fail with repair instructions instead of being accepted. For MLX Qwen thinking templates, use
scripts/inference_mlx_server.sh --model /path/to/mlx/model --template-profile qwen-final-content, then verify withscripts/inference_template_conformance.sh --provider mlx --model /path/to/mlx/model.Dream Mode reflects on LLM-generated hypotheses by default and performs one bounded deepening pass when the best output misses the sigma target (
GO_DREAM_REFLECT_ENABLED=true,GO_DREAM_DEEPEN_ON_WEAK_OUTPUT=true,GO_DREAM_REFLECTION_MIN_SCORE=0.74). If structured LLM synthesis is unavailable, Dream Mode returnsdream_unavailable; non-LLM evidence packaging belongs to context-pack or review.Ollama remains a compatibility fallback, not the preferred always-on embedding path.
Local helpers enforce one active LLM backend by default (
CONTEXTLATTICE_SINGLE_ACTIVE_INFER_BACKEND=true).
Inspect live routing and benchmark configured backends:
scripts/inference_runtime_policy.sh
scripts/benchmark_inference_backends.sh
scripts/inference_template_conformance.sh --provider mlx --model /path/to/mlx/modelEmbedding defaults to the Rust fastembed-rs sidecar. Ollama stays available as an explicit compatibility fallback, not the preferred embedding path.
Useful model runtime knobs:
ORCH_INFER_PROVIDER=auto
ORCH_INFER_PROVIDER_PRIORITY=mlx,vllm-metal,ane_sidecar,sglang,vllm,openai-compatible,llama-cpp,ollama
ORCH_INFER_AUTO_PROBE_ENABLED=true
SGLANG_BASE_URL=http://127.0.0.1:30000
VLLM_BASE_URL=http://127.0.0.1:8000
VLLM_METAL_BASE_URL=http://127.0.0.1:8000
MLX_API_BASE=http://127.0.0.1:18087/v1
LLAMA_CPP_BASE_URL=http://127.0.0.1:8080Agent CLI
Installer and quickstart paths install agent helpers under $HOME/.contextlattice/bin.
contextlattice_agent_adapter profiles
contextlattice_adopt status --pretty
contextlattice_doctor --agents codex --skip-provider-smoke --pretty
contextlattice_agent_start --soft --compact
contextlattice_agent_trace --session-id <session-id> --tree
contextlattice_pack "what should the next agent know?" --project my-project --pretty
contextlattice_search -h
contextlattice_write -h
contextlattice_checkpoint -h
contextlattice_skills_index search "browser automation" --prettycontextlattice_agent_adapteris the first-class lifecycle helper for bootstrap, context-pack, checkpoint, handoff, state, event, and completion flows.contextlattice_agent_adapter state --state working|awaiting_user|blocked|done --session-id <id> --prettyreports semantic agent lifecycle state with authority, source, TTL, native session id, task id, repo, worktree, branch, cwd, and user/blocker fields.contextlattice_agent_discover --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --repo . --prettyreports best-effort local process/profile/hook/repo-instruction evidence with explanations. Discovery is diagnostic evidence; explicit hook or adapter state remains authoritative.contextlattice_adoptis the zero-friction front door for local readiness, install guidance, profiles, and lifecycle proof;contextlattice_doctorcombines readiness, proof, and trace evidence in one bounded report.contextlattice_agent_startruns the lightweight startup guard for agents.contextlattice_agent_tracerenders the bounded run-shaping trail as a terminal tree, JSON, or Markdown run card.contextlattice_packcompiles a bounded prompt-ready packet with ranked evidence, files to inspect, risks, checks, source coverage, and areference_prompt.contextlattice_checkpointwrites a checkpoint and verifies readback.contextlattice_skills_indexdiscovers capabilities without loading every skill into startup context.contextlattice_source_backfillis an optional development helper, installed withscripts/install_global_agent_tools.sh --include-dev-python-tools, for bounded data imports.Hook pack details:
docs/agent-hooks.md.
Agent Runtime Sessions
ContextLattice tracks live agent work as first-class sessions, independent of the runner or model provider.
Start/list/read sessions through
GET|POST /v1/agents/sessionsandGET /v1/agents/sessions/{session_id}.Emit normalized events through
POST /v1/agents/sessions/eventorPOST /v1/agents/sessions/{session_id}/events.Inspect a bounded run trace through
GET /v1/agents/sessions/{session_id}/trace; the trace reports context, skills that may be helpful, source coverage, graph touches, handoffs, checkpoints, and timeline events without raw provider payloads.Read live runtime telemetry from
GET /telemetry/agents/runtime.Compile task context through
POST /memory/context-pack,POST /tools/context_pack, or globalcontextlattice_pack; responses includecontext_compiler, ranked evidence, deterministicagent_guidancefor themes/risk markers/candidate attention links, prompt sections, and a boundedreference_prompt.Watch long-running recall through
scripts/agent/contextlattice-session watch --session-id <id> --continuation-token <token>; continuation responses includeretrieval_progress.v1, dashboard status links, and agent-visible steering when async work is ready.Preflight, context-pack, and Dream Mode return
objective_runtime_state.v1withobjective_state,action_executed,evidence,objective_delta,risk_or_blocker, andnext_action.Use
scripts/agent/contextlattice-agent-adapteror globalcontextlattice_agent_adapteras the first-class product path for agent bootstrap, context-pack, checkpoint, handoff, state, event, and completion flows.Use
contextlattice_agent_adapter state --state working|awaiting_user|blocked|done --session-id <id> --prettyto report semantic agent lifecycle state with authority, source, TTL, native session id, task id, repo, worktree, branch, cwd, and user/blocker fields.Use global
contextlattice_agent_discover --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --repo . --prettyfor best-effort local process/profile/hook/repo-instruction discovery. Discovery explains why ContextLattice believes an agent is idle, working, waiting, blocked, or integrated; it does not replace explicit hook or adapter state.Use
scripts/agent/contextlattice-adoptor globalcontextlattice_adoptbefore handing ContextLattice to a new agent/account;doctorcombines gateway health, helper install state, shell PATH, storage posture, session store, profile coverage, best-effort discovery, runtime-doctor checks, lifecycle proof, and run trace evidence into one bounded report.Run
contextlattice_adopt integrate --repo . --agents codex,claude-code,opencode,hermes-agent,hermes-ultra,omp,mercury-agent,pi,droid --prettyinside a target repo to write managedAGENTS.md,CLAUDE.md,HERMES.md,MERCURY.md,PI.md, andDROID.mdinstruction blocks without overwriting user text.Run
contextlattice_doctor --agents codex --skip-provider-smoke --prettyfor the fastest new-agent adoption proof.The same doctor works for other agent profiles:
contextlattice_doctor --agents claude-code --skip-provider-smoke --pretty,contextlattice_doctor --agents opencode --skip-provider-smoke --pretty, orcontextlattice_doctor --agents codex,claude-code,opencode --skip-provider-smoke --pretty.Run
scripts/agent/agent-runtime-proof-pack --prettyor globalcontextlattice_agent_runtime_proof --prettyfor a one-command live proof that bootstrap, scoped recall, checkpoint, handoff, completion, status, and runtime telemetry are wired end to end.Use
scripts/agent/contextlattice-sessionfor CLI start/event/complete/fail/status/runtime/trace flows.Use
scripts/agent/agent-run-trace --session-id <id> --treeor globalcontextlattice_agent_trace --session-id <id> --treeto see the terminal trace, then--markdownto export the run card.Use global
contextlattice_runner_quality --prettyto inspect bounded runner-quality telemetry for adapter success/block/failure rates, context-pack quality linkage, exact prompt-token savings, modeled inference-avoidance signals, and advisor-only runner recommendations. Repo-localscripts/agent/runner-quality --prettyremains available for development fallback.Use
scripts/agent/contextlattice-session sweep-stale-audits --all-projects --prettyfor dry-run-first cleanup of stale objective-runtime audit/preflight sessions; add--confirmonly after reviewing matches.scripts/agent/contextlattice-pack,scripts/agent/contextlattice-dream,scripts/agent/writeback, and compaction hooks auto-start or recover a session whenCONTEXTLATTICE_SESSION_IDis absent.Pass
--session-idorCONTEXTLATTICE_SESSION_IDto force a specific session. SetCONTEXTLATTICE_AUTO_SESSION_DISABLED=1to disable automatic session creation.
Canonical event families include session.started, agent.state.working, agent.state.awaiting_user, agent.state.blocked, agent.state.done, context_pack.completed, retrieval.continuation.progress, retrieval.continuation.ready, retrieval.continuation.degraded, dream.completed, graph.neighbors_returned, graph.edge_touched, decision.made, test.ran, handoff.created, writeback.completed, and session.completed.
Agent lifecycle and retrieval lifecycle are intentionally separate. agent_lifecycle.state describes the actor (idle, working, awaiting_user, blocked, done); retrieval_lifecycle.status describes source-fetch progress. This prevents async retrieval warming from being misread as a blocked or degraded agent.
Runner quality is intentionally separate from both lifecycle surfaces. It is advisor-only telemetry for operator selection, not automatic dispatch. See docs/runtime/runner-quality-loop.md for the compact adapter measurement loop and its interpretation limits.
Download Installers
macOS DMG:
https://github.com/sheawinkler/ContextLattice/releases/latest/download/ContextLattice-macOS-universal.dmgmacOS signing/notarization operator notes:
docs/releases/macos-signing-notarization.mdHomebrew cask:
brew tap sheawinkler/contextlattice && brew install --cask contextlatticeWindows MSI:
https://github.com/sheawinkler/ContextLattice/releases/latest/download/ContextLattice-windows-x64.msiLinux bundle:
https://github.com/sheawinkler/ContextLattice/releases/latest/download/ContextLattice-linux-bootstrap.tar.gz
Resource Profiles
Profile | CPU | RAM | Storage |
Lite core |
|
|
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Lite advanced |
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Full |
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Optional constrained-disk guard: set QDRANT_HOT_STORAGE_MAX_BYTES to make launch/storage verification fail before the Qdrant hot lane exceeds your chosen byte ceiling. This is a guardrail, not a filesystem quota.
Memory Graph
GET|POST /v1/memory/edgespersists explicit typed relationships.POST /v1/memory/edges/backfillaudits or applies deterministic retroactive edges and opt-in same-projectinferred_relatedscoring. It is dry-run by default.POST /v1/memory/neighborsreturns explicit/inferred edge neighbors merged with semantic/topic neighbors.
./scripts/agent/memory-edge-backfill
./scripts/agent/memory-edge-backfill --include-inferred --min-confidence 0.90
./scripts/agent/memory-edge-backfill --write
./scripts/agent/memory-edge-inferred-retrofill --all-projects
./scripts/agent/memory-edge-inferred-retrofill --all-projects --profile exploratory
./scripts/agent/memory-edge-inferred-retrofill --all-projects --profile exploratory --write --confirm-retrofill ALL_PROJECTS
./scripts/agent/memory-edge-inferred-retrofill --project hermes-agent-ultra --corpus disk --profile exploratorySource Backfill
Bring existing data into ContextLattice without changing the ingest boundary.
Backfill is dry-run by default, writes go through /memory/write, and writes
require --write --confirm-write <project>.
./scripts/agent/source-backfill-memory --source jsonl --path exports/tasks.jsonl --project my-project --pretty
./scripts/agent/source-backfill-memory --source sqlite --path app.db --table notes --project my-project --pretty
./scripts/agent/source-backfill-memory --source parquet --path warehouse/events.parquet --project my-project --pretty
./scripts/agent/source-backfill-memory --source postgres --dsn "$DATABASE_URL" --query "select id,title,body from notes limit 100" --project my-project --pretty
./scripts/agent/source-backfill-memory --source jsonl --path exports/tasks.jsonl --project my-project --write --confirm-write my-project --apply-edges --prettySupported adapters: files/directories, JSONL, JSON, CSV, SQLite, DuckDB, Parquet
via DuckDB, and Postgres via optional psycopg. Import caps cover records, row
bytes, document bytes, total bytes, and structured-list items. Secret-like
fields are redacted by default, and graph edge repair is optional and bounded.
Skills Index And Quarantine Discovery
ContextLattice exposes active skills as a native Go Skills Index so agents can discover relevant capabilities without loading every SKILL.md into prompt context. In local installs, the active index mounts ${HOME}/.codex/skills read-only by default. Quarantined/vendor skill discovery remains a separate read-only lane and does not auto-load quarantined skills.
Active index endpoint:
GET|POST /v1/skills/index/searchActive index tool:
GET|POST /tools/skills_index_searchActive index status/reindex endpoint:
POST /v1/skills/index/reindex(live native scan; no prompt loading)Search endpoint:
GET|POST /v1/skills/quarantine/searchTool alias:
GET|POST /tools/skills_quarantine_searchReindex endpoint:
POST /v1/skills/quarantine/reindex(off by default; enable explicitly)
Runtime knobs:
ORCH_SKILLS_QUARANTINE_ENABLED=true
ORCH_SKILLS_QUARANTINE_HOST_BIN_DIR=${HOME}/.local/bin
ORCH_SKILLS_INDEX_HOST_ACTIVE_ROOT_DIR=${HOME}/.codex/skills
ORCH_SKILLS_INDEX_HOST_SYSTEM_ROOT_DIR=${HOME}/.codex/skills/.system
ORCH_SKILLS_INDEX_ROOTS=/opt/contextlattice/skills_active:/opt/contextlattice/skills_system
ORCH_SKILLS_QUARANTINE_HOST_ROOT_DIR=${HOME}/.codex/skills_quarantine
ORCH_SKILLS_QUARANTINE_SEARCH_CMD=/opt/contextlattice/skills/bin/codex-skills-quarantine-search
ORCH_SKILLS_QUARANTINE_REINDEX_CMD=/opt/contextlattice/skills/bin/codex-skills-quarantine-reindex
ORCH_SKILLS_QUARANTINE_TIMEOUT_SECS=8
ORCH_SKILLS_QUARANTINE_DEFAULT_LIMIT=20
ORCH_SKILLS_QUARANTINE_MAX_LIMIT=100
ORCH_SKILLS_QUARANTINE_REINDEX_ENABLED=false
CODEX_SKILLS_QUARANTINE_ROOT=/opt/contextlattice/skills_quarantine
CODEX_SKILLS_QUARANTINE_INDEX_DIR=/opt/contextlattice/skills_quarantine/index
CODEX_SKILLS_QUARANTINE_INDEX=/opt/contextlattice/skills_quarantine/index/skills_index.jsonlSecurity and Privacy
Local-first by default.
API-key protected operational routes.
Secret-like content redaction controls.
Premium billing/provider route maps are intentionally kept out of public docs.
Docs Index
Overview:
https://contextlattice.io/Architecture:
https://contextlattice.io/architecture.htmlLocal AI workspace comparison:
https://contextlattice.io/local-ai-workspaces.htmlScaling memory:
https://contextlattice.io/scaling-memory.htmlWiki:
https://contextlattice.io/wiki.htmlInstallation:
https://contextlattice.io/installation.htmlIntegrations:
https://contextlattice.io/integration.htmlTroubleshooting:
https://contextlattice.io/troubleshooting.htmlUpdates:
https://contextlattice.io/updates.htmlRelease notes, newest first; older entries are historical:
docs/releases/v3.10.2.md(Go-native feedback submit, idempotency, preference projection, and strict ownership coverage)docs/releases/v3.10.1.md(detected OMP/Mercury instruction hooks and default adoption coverage)docs/releases/v3.10.0.md(optional Pi/Droid runner adapters, runner-quality advisor, and CLI-first public surface)docs/releases/v3.9.1.md(dashboard contrast, settings clarity, public-site alignment, and current-version guidance)docs/releases/v3.9.0.md(agent-operable context-pack outcome telemetry and observed provider usage)docs/releases/v3.8.0.md(MongoDB driver v2 migration and Context Pack Quality Ledger)docs/releases/v3.7.1.md(MongoDB driver security patch for the v3.7 train)docs/releases/v3.7.0.md(tokenizer-exact prompt economics, bounded token-impact ledger, and release-note hygiene)docs/releases/v3.6.2.mddocs/releases/v3.5.0.mddocs/releases/v3.4.25.mddocs/releases/v3.4.14.mddocs/releases/v3.4.13.mddocs/releases/v3.4.12.mddocs/releases/v3.4.11.mddocs/releases/v3.4.10.mddocs/releases/v3.4.5.mddocs/releases/v3.4.2.mddocs/releases/v3.4.1.md
Local model options:
docs/runtime/local-model-options.md
License
Business Source License 1.1 (LICENSE).
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