ContextLattice

io.github.sheawinkler由 GitHub 帳號 sheawinkler 發布v1.0.0更新於 Nov 2, 2026

Private-by-default, local-first memory/context/task orchestrator for MCP apps and agents.

已驗證Streamable HTTP可網頁執行效率與工作流知識與記憶

概覽

AI 產生的概覽

本地優先的記憶與上下文層,為 AI 代理提供跨工作階段、跨工具的持久連續性、可解釋檢索與可攜上下文。

可以做什麼
  • 從持久檢查點重新開啟並接續目前目標,不必重播整段對話
  • 依範圍檢索並排序證據,產生附來源與覆蓋回執的精簡上下文包
  • 依專案儲存、更正並結束持久記憶項目
  • 透過 Agent Packet 與 Passport 在代理與機器之間傳遞簽章的最小權限上下文
  • 以唯讀方式搜尋技能索引,不必載入每個技能檔案即可找到能力
  • 執行僅供建議的持續認知狀態與評估呼叫,回傳證據參照
適用情境
適合代理需要在不同對話、模型、工具或電腦之間持續工作,並要求任務目標、決策與證據得以延續的情境。也適合希望使用本地、可檢查且附檢索回執的記憶,而不想依賴強制雲端儲存的團隊。
執行需求
需要本地執行環境:macOS、Linux 或透過 WSL2 的 Windows,相容 Compose v2 的容器執行環境,以及 gmake、jq、rg、python3 與 curl。可透過原始碼搭配 gmake quickstart 安裝,或使用 Homebrew cask、DMG、MSI、Linux 安裝包。公開本地通道免帳號;維運路由以 API 金鑰保護。資源等級從 2-4 vCPU、4-8 GB 記憶體起。
安裝前請注意
安裝會將受管理的區塊寫入儲存庫中的代理指令檔,但會保留原有文字。維運路由以 API 金鑰保護,寫入入口預設會過濾疑似金鑰的內容。付費製品涵蓋受控協作、工作區操作與託管散布。macOS DMG 未簽章,預期會出現 Gatekeeper 警告。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 ContextLattice,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.sheawinkler%2Fcontextlattice/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "contextlattice": {
      "type": "http",
      "url": "https://contextlattice.io/mcp"
    }
  }
}

README

ContextLattice

[ContextLattice editorial website hero showing a live local context field]

Open an agent. Already there.

The local-first intelligence layer that gives AI agents durable continuity, explainable retrieval, portable context, and verified learning across harnesses.

[CLI first] [ContextLattice v5.0.2] [Local-first runtime] [Apache License 2.0]

Quickstart · How it works · Agent setup · Architecture · Docs · Updates

Stop replaying the brief

Models can reason. Harnesses can act. Neither reliably retains the mission when a chat, model, tool, account, or computer changes.

ContextLattice gives that work a durable, inspectable context layer. It reconstructs the active objective, selects the evidence that matters, carries it safely, and records what actually worked—without turning every prompt into a transcript dump or making cloud storage mandatory.

CapabilityWhat changes
Durable continuityReopen the objective, decisions, repository state, risks, proof, and next move as one bounded packet.
Explainable retrievalRank evidence by impact per token and expose source coverage, omissions, opposition, degradation, and receipts.
Portable contextMove signed, least-privilege continuation across agents and machines while keeping execution and transport caller-owned.
Verified skill evolutionDiscover skills without loading every file, evaluate repeated wins on holdouts, and require review before promotion.
Privacy-bounded Aggregate SignalLearn from explicitly opted-in, clipped statistics while raw memory remains local; production activation stays hard-blocked pending independent privacy and utility review.

The CLI is the primary interface. The dashboard makes behavior and proof visible. HTTP and MCP are companion integration surfaces for applications and harnesses.

How it works

StageContextLattice does
01 · ReopenReconstructs the one active mission from durable checkpoints and current state.
02 · SelectRetrieves high-signal evidence into a compact Context Pack with provenance.
03 · MoveCarries signed, bounded context through Agent Packets, Passports, and encrypted continuation envelopes.
04 · EarnRecords outcomes and promotes reusable behavior only after deterministic proof and human approval.
05 · CompoundImproves future retrieval while preserving corrections, contradictions, freshness, and retirement semantics.

ContextLattice does not replace your agent harness, choose goals from retrieved text, or execute imported context. Local tools remain execution surfaces; memory and remote content remain evidence.

Quickstart

Requirements: macOS, Linux, or Windows through WSL2; a Compose v2-compatible container runtime; and gmake, jq, rg, python3, and curl. The tested macOS baseline uses OrbStack through its explicit Docker context; see the container runtime decision.

1. Install

zsh
git clone https://github.com/sheawinkler/ContextLattice.gitcd ContextLatticecp .env.example .envgmake quickstart

gmake quickstart is the prescribed technical install path; installers are bootstrap alternatives. The command prepares environment wiring, asks for a runtime profile, launches the selected local stack, and validates initial readiness.

2. Verify the runtime and retrieval path

zsh
curl -fsS http://127.0.0.1:8075/health | jqcontextlattice doctor --prettycontextlattice state status --prettycontextlattice context "verify this ContextLattice installation" \  --project contextlattice \  --pretty

Healthy containers are only the first check. The state command verifies the canonical gateway-owned storage inventory; the final command exercises the actual context path and reports source coverage, degradation, evidence, and next actions. Existing installs can use the explicit, reversible procedure in gateway state migration.

For a fuller lifecycle proof from the repository:

zsh
scripts/agent/agent-runtime-proof-pack --prettyscripts/agent/agent-adoption-proof-matrix \  --skip-provider-smoke \  --progress \  --pretty

Connect your agents

Run integration from the repository that should use ContextLattice:

zsh
cd /path/to/your/project
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

The integration command writes bounded managed blocks while preserving existing instruction text. It does not install Codex, Claude Code, OpenCode, Hermes, OMP, Mercury, Pi, Droid, or another third-party agent harness.

External provider discovery is network-free. Provider execution remains explicit and caller-authorized; see the external-provider boundary.

If an agent is performing the installation, it should follow the quickstart directly, avoid cloning a second checkout when already inside one, report the exact failing command and path, and rerun the deterministic check after any repair.

The daily agent loop

zsh
# Confirm readiness when the environment is uncertain.contextlattice doctor --pretty
# Retrieve scoped context before substantial work.contextlattice context "debug the current release regression" \  --project contextlattice \  --pretty
# Save concise, durable progress.contextlattice remember \  "Root cause verified; regression test added; focused checks pass." \  --project contextlattice \  --pretty
# Resume without replaying the transcript.contextlattice resume --project contextlattice --pretty
# Repair stale or wrong recall without silently rewriting history.contextlattice correct \  "The prior deployment record is stale." \  --category stale \  --project contextlattice \  --pretty
# Close the loop with the verified outcome.contextlattice finish \  "Regression fixed and verified." \  --success \  --project contextlattice \  --pretty
# Project the next bounded move or bind a completed response to durable proof.contextlattice_continuous_cognition status "prepare the next verified move" \  --project contextlattice --session-id <session-id> --agent-id codex_gpt5 \  --task-id <task-id> --objective-id <objective-id> --as-of <rfc3339> --prettycontextlattice_continuous_cognition evaluate "verify the completed response" \  --project contextlattice --session-id <session-id> --agent-id codex_gpt5 \  --task-id <task-id> --task-identity-id <task-identity-id> --as-of <rfc3339> --pretty
# Prepare context for an external worker without exposing its one-shot claim.contextlattice agent-fit context-prep-schedule --project contextlattice \  --session-id <session-id> --agent-id codex_gpt5 --payload-file prep-request.json --rawcontextlattice agent-fit context-prep-claim --project contextlattice \  --session-id <session-id> --agent-id codex_gpt5 --prep-id <prep-id> \  --worker-id <worker-id> --claim-token-file prep.claim --rawcontextlattice agent-fit context-prep-complete --project contextlattice \  --session-id <session-id> --agent-id codex_gpt5 --prep-id <prep-id> \  --claim-token-file prep.claim --payload-file prep-artifact.json --rawcontextlattice agent-fit context-prep-use --project contextlattice \  --session-id <session-id> --agent-id codex_gpt5 --prep-id <prep-id> \  --task-id <task-id> --effective-profile-digest <sha256-digest> \  --source-generation <generation> --raw

Continuous Cognition is advisory-only: each invocation makes one bounded request, returns opaque evidence references, and never dispatches a runner or performs an external mutation. Context-preparation claims stay in an owner-only file and cross the completion/failure boundary only through the protected header; successful explicit use consumes the artifact once.

Find a capability without loading every skill body:

zsh
contextlattice_skills_index search "browser automation" --pretty

The active Skills Index scans configured Codex, Hermes, Hermes Ultra, and shared agent roots read-only. It reports each harness and root inventory separately, collapses byte-identical SKILL.md files by SHA-256 digest while retaining every source path as provenance, and requires discriminating query-term coverage instead of ranking generic words such as skill, index, or agent. Quarantine discovery remains separate, read-only by default, and never auto-promotes retrieved content.

Architecture

The default local control path is:

text
Agent or application        │        ▼ContextLattice CLI / HTTP / MCP        │        ▼Gateway :8075        ├── durable write and outbox fanout        ├── scoped retrieval and source receipts        ├── session, objective, graph, and outcome state        └── dashboard-visible proof and operations

Writes are validated and durably persisted before fanout. Retrieval merges the available sources, ranks bounded evidence, and reports missing or degraded coverage instead of hiding it.

The active application path is Go and Rust. Python remains in build, development, migration, and audit tooling rather than the live request path. The exact runtime and toolset choices are recorded in the v4 runtime decision and container decision.

Public and paid boundaries

The public local lane is account-free and useful on its own. It includes the CLI-first memory lifecycle, Context Packs, sessions, graph and claim surfaces, Skills Index discovery, Agent Packets, public Passport and Mesh contracts, and local proof tooling.

Paid artifacts add governed collaboration, protected activation, workspace operations, advanced analytics, and hosted distribution. They do not turn local memory into a mandatory cloud dependency.

See plans and distribution boundaries for the current contract.

Install options

macOS technical preview: unsigned DMG bootstrap launcher; expect Gatekeeper warnings until Developer ID notarization is configured, and prefer the source/CLI path.

PathBest forStatus
Source + gmake quickstartTechnical users and terminal-capable agentsRecommended
brew tap sheawinkler/contextlattice && brew install --cask contextlatticemacOS convenience bootstrapAvailable
macOS universal DMGGuided macOS bootstrapUnsigned technical preview; expect Gatekeeper warnings
Windows x64 MSIGuided Windows bootstrapAvailable
Linux bootstrap bundleGuided Linux bootstrapAvailable

Resource profiles

ProfileCPURAMStorage
Hugging Face / Glama lite2–4 vCPU4–8 GB20–50 GB SSD
Local Lite core2–4 vCPU8–12 GB25–80 GB SSD
Local Lite advanced4–6 vCPU12–16 GB80–140 GB SSD
Local Full6–8 vCPU12–20 GB100–180 GB SSD

For heavier ingest, model storage, or the spike-lab adapters, read the installation and storage guidance before selecting a profile.

Security and privacy

  • Local-first and account-free in the public local lane.
  • API-key protection for operational routes.
  • Deterministic secret-like content filtering at write ingress: redact by default, block when configured, and allow only by explicit operator choice.
  • Provenance and trust isolation on retrieved memory.
  • Signed portable context and encrypted continuation envelopes.
  • Dry-run-first graph repair, source backfill, and quarantine workflows.
  • No automatic execution of retrieved instructions or imported continuation content.

Security reports follow SECURITY.md.

Documentation

NeedStart here
Product overviewcontextlattice.io
InstallationInstallation guide
CLI and agent lifecycleCLI reference
Harness and app integrationIntegration guide
Architecture and scalingArchitecture · Scaling memory
TroubleshootingTroubleshooting guide
Current behavior and release evidenceUpdates · v5.0.2 release notes
RoadmapPublic roadmap
Agent hooksAgent hook contract
Retrieval trustRetrieval receipts
Skills and verified learningSkill efficacy review · Skill Foundry
Portable contextContext Passport and Mesh
Local inferenceLocal model options
Full repository-backed manualPublic field manual

The current release baseline is v5.0.2.

License

ContextLattice's public lane is licensed under the Apache License 2.0.

來源:README.md,提交 e0daa91

工具

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版本歷史

1
  1. v1.0.0最新Sep 16, 2026

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