ContextLattice

io.github.sheawinklerPublished by GitHub account sheawinklerv1.0.0Updated Nov 2, 2026

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

VerifiedStreamable HTTPWeb executableProductivity & WorkflowKnowledge & Memory

Overview

AI-generated overview

Local-first memory and context layer that gives AI agents durable continuity, explainable retrieval and portable context across sessions and harnesses.

What you can do
  • Reopen and resume an active objective from durable checkpoints instead of replaying a transcript
  • Retrieve scoped, ranked evidence as a bounded Context Pack with provenance and coverage receipts
  • Save, correct and close out durable memory entries per project
  • Carry signed, least-privilege context between agents and machines via Agent Packets and Passports
  • Search a read-only Skills Index to find capabilities without loading every skill file
  • Run advisory continuous-cognition status and evaluation calls that return evidence references
When to use it
Useful when an agent must keep working across chats, models, tools or machines and needs the mission, decisions and proof to survive the switch. Also fits teams that want local, inspectable memory with retrieval receipts rather than a mandatory cloud store.
Requirements
A local stack: macOS, Linux, or Windows via WSL2, a Compose v2-compatible container runtime, and gmake, jq, rg, python3 and curl. Installed from source with gmake quickstart, or via Homebrew cask, DMG, MSI or Linux bundle. The public local lane is account-free; operational routes use API-key protection. Resource profiles range from 2-4 vCPU and 4-8 GB RAM upward.
Before you install
The install writes managed blocks into agent instruction files in your repository, though existing text is preserved. Operational routes are protected by an API key, and secret-like content is filtered at write ingress by default. Paid artifacts cover governed collaboration, workspace operations and hosted distribution. The macOS DMG is unsigned, so Gatekeeper warnings are expected.

Installation

In SourceWeft

  1. Open ContextLattice in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.sheawinkler%2Fcontextlattice/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "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.

Source: README.md at commit e0daa91

Tools

0
SourceWeft has not read this server's tools yet.

Version history

1
  1. v1.0.0LatestSep 16, 2026

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