cognitiveos 0.1.4

Local-first cognitive graph middleware with CLI, MCP, and Docker runtime targets.

CognitiveOS

Local-first cognitive graph runtime for Codex, Claude Code, Gemini CLI, and other MCP-capable hosts.

PyPI | Releases | Docs

[!IMPORTANT] v0.1.4 is live on PyPI: pip install cognitiveos

For compact host mounts, prefer --profile compact-core. compact-core exposes search, read, add, update, link, and dream, while intentionally omitting unlink.

This repository is a production-oriented project skeleton for a local-first agent memory runtime.

At a glance:

Need	Use	Why
Install CognitiveOS quickly	pip install cognitiveos	Fastest path to a working local runtime
Mount into a compact host surface	cognitiveos-mcp --profile compact-core	Smaller tool surface, clearer prompts
Run a general MCP host	cognitiveos-mcp --profile host-core	Full core memory workflow including unlink
Explore docs and releases	GitHub docs and release pages	Best entry point for release notes and implementation details

CognitiveOS focuses on:

• installable Python packaging via pip
• CLI operations for database lifecycle and graph manipulation
• an installable MCP server runtime
• Docker-based deployment for HTTP MCP serving
• extensible provider and extractor boundaries

Start Here

Package Install

Use this when you just want a local runtime:

pip install cognitiveos
cognitiveos init-db
cognitiveos-mcp --transport stdio --profile host-core

Compact Host Mount

Use this when CognitiveOS is being mounted into a host that benefits from a smaller tool surface:

cognitiveos-mcp --transport stdio --profile compact-core

To make Codex load the repository's memory skill for new conversations, copy the bundled skill into your user-level Codex skills directory:

macOS / Linux:

mkdir -p ~/.codex/skills
cp -R ./skills/cognitiveos-memory-ops ~/.codex/skills/cognitiveos-memory-ops

Windows PowerShell:

New-Item -ItemType Directory -Force "$HOME\.codex\skills" | Out-Null
Copy-Item -Recurse -Force .\skills\cognitiveos-memory-ops "$HOME\.codex\skills\cognitiveos-memory-ops"

Repository Development

Use this when you are developing CognitiveOS itself:

python -m venv .venv
. .venv/bin/activate
pip install -e ".[dev]"

MCP Profile Guide

Profile	Intended use	Public tools
compact-core	Compact host mounts	search, read, add, update, link, dream
host-core	General MCP hosts	search, read, add, update, link, unlink, dream
operator	Maintenance / graph operations	core tools plus relationship and provider operations
bootstrap	Onboarding and host install flows	bootstrap-only tools
full	Everything	all public tools

Public Entry Points

• PyPI: https://pypi.org/project/cognitiveos/
• Releases: https://pypi.org/project/cognitiveos/#history
• Repository docs: docs/

✨ Design Goals

CognitiveOS is not intended to be a universal knowledge vault.

Its primary goal is to give an agent host a compact, durable, searchable memory layer that remains useful across sessions without forcing the runtime to mirror every source in full.

The practical goals are:

• preserve durable user preferences, profile facts, goals, lessons, and decisions
• preserve useful source anchors such as files, folders, repositories, and URLs
• make those memories searchable through both keyword and semantic retrieval
• keep the public memory model compact enough that hosts can reason about it reliably
• support long-lived local operation with minimal infrastructure
• keep the host-facing tool surface small and stable

🧭 Design Philosophy

Several design choices in this repository follow that goal directly.

Local-first:

• memory lives in a local user-controlled runtime and does not require a hosted backend by default
• SQLite is the default runtime store
• MCP and CLI surfaces are designed to work well on a single machine

Compact public model:

• the runtime prefers a small number of structural node types over many specialized ones
• files are stored as source_document
• folders and repositories are stored as source_collection
• deep live inspection of codebases, folders, and remote resources remains the host agent's job

Searchability over verbatim chat copying:

• stored memory should be normalized into retrieval-friendly language
• durable facts should be explicit about actor, object, and time when known
• the point is future recall quality, not transcript fidelity

Source anchors over recursive mirroring:

• large repositories and folders should usually be remembered as compact roots
• the runtime stores enough summary and metadata to make the source re-openable
• the host agent can inspect the live source later when detailed exploration is needed

Operational simplicity:

• prefer predictable CLI and MCP semantics over feature sprawl
• prefer bounded metadata over unbounded recursive ingestion
• prefer internal hardening, such as SQLite stability settings and background logs, over expanding host-visible tooling

✅ Status

The current milestone is an MVP foundation with working runtime, retrieval, and host bootstrap flows.

Area	Included
Core runtime	SQLite-backed nodes, edges, audit logs, sqlite-vec, FTS5
Retrieval	keyword, semantic, and hybrid ranking with bounded neighbor traversal
Ingestion	content, local files, folders, remote URLs, remote snapshot preservation
Graph updates	add, update, link, audit tracing, canonical node and edge semantics
Dream flow	clustering, chat-first compaction, host fallback tasks, MEMORY.MD generation
Host integration	CLI, MCP, bootstrap bundle generation, managed host install support
Operations	health checks, provider smoke tests, embedding reindexing, background jobs

Highlights:

• content, file, and folder ingestion use durable source_document and source_collection roots
• remote extraction is Trafilatura-first and preserves typed source metadata plus snapshots
• folder inspection stays root-only in v1 to keep memory compact and predictable
• first-start onboarding can seed pinned system-profile memory through structured profile metadata
• background dream execution is available for runtime-only compaction paths

🚀 Quick Start

Fast Path

python -m venv .venv
. .venv/bin/activate
pip install -e ".[dev]"
cognitiveos init-db
cognitiveos-mcp --transport stdio --profile host-core

Common Commands

Goal	Command
Initialize the workspace database	cognitiveos init-db
Add raw text memory	cognitiveos add --type content --payload "..." --tag tech
Add a file or URL	cognitiveos add --type file --payload "..."
Add a folder root	cognitiveos add --type folder --payload ".\\path"
Search by keyword	cognitiveos search --keyword CognitiveOS --top-k 5
Search by semantic query	cognitiveos search --query "graph memory runtime"
Start MCP over stdio	cognitiveos-mcp --transport stdio --profile host-core
Start compact MCP profile	cognitiveos-mcp --transport stdio --profile compact-core

Add Memory

cognitiveos add --type content --payload "CognitiveOS stores graph-shaped memory." --tag tech --tag memory --tag graph

Add Source Anchors

cognitiveos add --type folder --payload ".\\Pictures\\Japan Trip 2025" --name "Japan Trip 2025 Photos" --tag travel --tag japan --tag photos

Add a remote source document and preserve a local snapshot:

cognitiveos add --type file --payload "https://example.com/article" --name "Example Article" --tag article --tag reference

Add a browser-exported local capture with a sidecar manifest:

cognitiveos add --type file --payload ".\\exports\\article.html"

[!TIP] Use repeated --tag for retrieval labels. Use --name when the node should keep a stable human-readable title. Profile-oriented writes such as identity, communication preferences, workspace context, and engineering preferences are merged into canonical profile nodes instead of creating overlapping duplicates.

Source-handling notes:

• remote http and https URLs are preserved by default under .cognitiveos/snapshots/, using Markdown for readable text/page captures and binary files for formats such as PDF
• when a page requires login or browser state, use the host's existing browser tools to open the accessible page first; CognitiveOS v1 does not automate browser capture itself
• adding the same remote URL again warns on conflict by default; use force=true to refresh that source in place
• host-assisted inputs can be: the final accessible URL, a browser-exported local HTML file, or a host-prepared Markdown file
• for browser-exported local HTML or Markdown, add a sidecar file named <stem>.cognitiveos-source.json beside the export to preserve the original remote URL and capture metadata

[!WARNING] include_neighbors, include_content, and include_evidence can expand returned context quickly. Start narrow, then widen only when the initial result is clearly insufficient.

For routine recall, prefer include_neighbors=0 or 1, include_content=false, and include_evidence=false.

Search

cognitiveos search --keyword CognitiveOS --top-k 5 --include-neighbors 1

Run hybrid search:

cognitiveos search --query "graph memory runtime" --keyword CognitiveOS --top-k 5

MCP Transport

cognitiveos-mcp --transport stdio --profile host-core

For compact host mounts, use the reduced tool surface:

cognitiveos-mcp --transport stdio --profile compact-core

Run the MCP server over Streamable HTTP:

cognitiveos-mcp --transport streamable-http --profile host-core --host 0.0.0.0 --port 8000 --path /mcp

🔌 Provider Configuration

Embedding and chat providers are optional.

What Changes When Providers Are Enabled

• add performs similarity probing before insert unless force=true
• search --query ... performs semantic search even without keywords
• search --query ... --keyword ... performs hybrid ranking
• missing node embeddings are lazily backfilled

Provider Support Matrix

Provider	Chat	Embeddings	Notes
ollama	yes	yes	local-first default option
openai	yes	yes	hosted API
gemini	yes	yes	hosted API
anthropic	yes	no	chat only
local_huggingface	yes	yes	requires pip install 'cognitiveos[local-hf]'

Environment Variables

COGNITIVEOS_EMBEDDING_PROVIDER_TYPE=ollama
COGNITIVEOS_EMBEDDING_MODEL_NAME=nomic-embed-text
COGNITIVEOS_EMBEDDING_BASE_URL=http://localhost:11434/api
COGNITIVEOS_CHAT_PROVIDER_TYPE=ollama
COGNITIVEOS_CHAT_MODEL_NAME=gemma3
COGNITIVEOS_CHAT_BASE_URL=http://localhost:11434/api
COGNITIVEOS_SIMILARITY_THRESHOLD=0.6
COGNITIVEOS_ENTITYLESS_UNION_SIMILARITY_THRESHOLD=0.8
COGNITIVEOS_MAX_PROJECTED_SUPER_NODES=5
COGNITIVEOS_HYBRID_SEMANTIC_WEIGHT=0.65
COGNITIVEOS_HYBRID_KEYWORD_WEIGHT=0.35

Example Provider Setups

# OpenAI
COGNITIVEOS_EMBEDDING_PROVIDER_TYPE=openai
COGNITIVEOS_EMBEDDING_MODEL_NAME=text-embedding-3-small
COGNITIVEOS_EMBEDDING_API_KEY=...
COGNITIVEOS_CHAT_PROVIDER_TYPE=openai
COGNITIVEOS_CHAT_MODEL_NAME=gpt-5-mini
COGNITIVEOS_CHAT_API_KEY=...

# Anthropic chat only
COGNITIVEOS_CHAT_PROVIDER_TYPE=anthropic
COGNITIVEOS_CHAT_MODEL_NAME=claude-3-7-sonnet-latest
COGNITIVEOS_CHAT_API_KEY=...

# Gemini
COGNITIVEOS_EMBEDDING_PROVIDER_TYPE=gemini
COGNITIVEOS_EMBEDDING_MODEL_NAME=text-embedding-004
COGNITIVEOS_EMBEDDING_API_KEY=...
COGNITIVEOS_CHAT_PROVIDER_TYPE=gemini
COGNITIVEOS_CHAT_MODEL_NAME=gemini-2.5-flash
COGNITIVEOS_CHAT_API_KEY=...

🐳 Docker

Build

docker build -t cognitiveos:latest .

Run

docker run --rm -p 8000:8000 -v $(pwd)/data:/app/data cognitiveos:latest

Use a Local .env

cp .env.example .env
docker compose up --build

🛠 Operations

Runtime Checks

Goal	Command
Health report	cognitiveos doctor
Live provider smoke test	cognitiveos providers-test
Rebuild the vector index	cognitiveos reindex-embeddings
Refresh one source_document	cognitiveos refresh-source-document <node-id>

cognitiveos doctor

Live provider smoke test:

cognitiveos providers-test

Rebuild the vector index from current node embedding inputs:

cognitiveos reindex-embeddings

Refresh one source_document from its original source path or URI:

cognitiveos refresh-source-document <node-id>

Host Bootstrap

cognitiveos bootstrap-host

This command generates local runtime bootstrap artifacts under .cognitiveos/bootstrap/. They are environment-specific and are not intended to be committed to the repository.

Supported host kinds:

• generic as the default portable path
• codex with project-level auto-install support
• claude-code
• claude-desktop
• gemini-cli
• cursor

Inspect host bootstrap status:

cognitiveos host-bootstrap-status --host-kind generic

Submit first-start onboarding answers:

cognitiveos submit-host-onboarding \
  --answer display_name=Alex \
  --answer role_team="Sr. Data Engineer" \
  --answer preferred_language=Chinese \
  --answer response_style="Concise, direct, pragmatic" \
  --answer workspace_goal="Build CognitiveOS as a host-facing memory runtime"

Install the managed cold-start mount into project files for a supported host target:

cognitiveos bootstrap-host --host-kind codex --install

Optionally install the bundled skill into Codex so new conversations can recall and save CognitiveOS memory through the documented workflow:

macOS / Linux:

mkdir -p ~/.codex/skills
cp -R ./skills/cognitiveos-memory-ops ~/.codex/skills/cognitiveos-memory-ops

Windows PowerShell:

New-Item -ItemType Directory -Force "$HOME\.codex\skills" | Out-Null
Copy-Item -Recurse -Force .\skills\cognitiveos-memory-ops "$HOME\.codex\skills\cognitiveos-memory-ops"

Dream Operations

Goal	Command
Queue dream in background	cognitiveos dream --background
Inspect dream status	cognitiveos dream --inspect status
Inspect dream runs	cognitiveos dream --inspect runs
Inspect pending tasks	cognitiveos dream --inspect tasks

cognitiveos dream --background

Inspect current dream status:

cognitiveos dream --inspect status

Inspect recent dream runs:

cognitiveos dream --inspect runs

List pending host dream compaction tasks:

cognitiveos dream --inspect tasks

Resolve one pending dream compaction with host-authored compressed content:

cognitiveos dream --task-id <task-id> \
  --title "Compressed cluster title" \
  --description "Compressed description" \
  --content "Compressed synthesis"

Resolve one pending compaction with heuristic fallback in the background:

cognitiveos dream --task-id <task-id> --use-heuristic

🧱 Data Model

Canonical Node Rules

Field	Meaning
node_type	structural type only
durability	lifecycle tier only
tags	open retrieval labels
metadata	structured factual payload

Structural Node Types

• memory
• source_document
• source_collection
• super_node

Durability Tiers

• working
• durable
• pinned
• ephemeral

Common add Parameters

• payload: the primary input body or locator
• name: optional human-readable title stored on the node
• tag: repeated retrieval labels
• durability: optional lifecycle override
• force: bypass similarity or duplicate blocking where supported

File Ingestion Defaults

• local files keep extracted raw text in source_document.content
• remote URLs keep a compact preservation note in source_document.content
• source_document.description stores a retrieval-oriented description
• source_document.tags carries user tags plus generated retrieval tags
• document embeddings are built from description + tags
• keyword search does not index full raw document content for source_document
• remote URL source kinds include remote_page, remote_document, remote_feed_item, remote_video, and remote_binary
• remote snapshots are preserved under .cognitiveos/snapshots/
• read --include-content on a remote source_document returns the preserved Markdown snapshot body when available
• binary remote snapshots stay preserved on disk and read --include-content falls back to the stored note instead of emitting raw bytes
• browser-exported local HTML / Markdown with a sibling <stem>.cognitiveos-source.json manifest is ingested as a remote source instead of a plain local_file

Browser-Capture Sidecar Example

{
  "source_kind": "remote_page",
  "requested_url": "https://example.com/login",
  "final_url": "https://example.com/article",
  "title": "Captured Article",
  "capture_method": "browser_export_html",
  "captured_at": "2026-04-09T10:00:00+08:00",
  "http_status": 200,
  "exported_from": "chrome-devtools"
}

Folder Ingestion Defaults

• source_collection.content stores a compact root summary, not a recursive file dump
• source_collection.description stores a retrieval-oriented collection summary
• source_collection.metadata.source.ref stores the normalized folder path
• source_collection.metadata.collection.class is one of repository, media_collection, document_collection, or workspace_bundle
• folder scanning is root_only in v1, with bounded sample entries, file-type counts, and important markers
• folder embeddings are built from description + tags + compact collection hints
• update revises the stored summary only; it does not rescan the live folder

Canonical Edge Rules

• strength_score is the only runtime edge-strength field
• edge lifecycle is controlled through status values such as active, weak, and stale
• link no longer accepts a public weight parameter

🌙 Dream Policy

Trigger Conditions

Condition	Dream becomes due when
Event threshold	10 new memory events have accumulated since the last completed dream
Age threshold	more than 24 hours have passed since the last completed dream and at least 5 new events have accumulated

Runtime Behavior

• when a chat model is configured, CognitiveOS auto-triggers dream on the next memory operation after the threshold is met
• when that auto-trigger path can complete entirely inside the runtime, CognitiveOS queues dream in the background instead of blocking the current operation
• when the age window is reached but fewer than 5 new events exist, CognitiveOS emits a deferred reminder instead of auto-running dream

Compaction Priority

1. chat provider
2. host agent
3. heuristic fallback

Host Fallback

• when no chat model is configured, or when chat compaction fails during a dream run, CognitiveOS returns pending host compaction tasks with source data, a prepared digest, and a prompt for the host agent
• when no chat model is configured and dream is due, CognitiveOS emits a reminder on the next memory operation instead of auto-running dream
• the reminder points the host to run dream, inspect pending compactions, and submit compressed clusters back through dream

Public dream Entry Point

• running a new dream job
• inspecting current dream status
• inspecting recent runs
• inspecting pending compaction tasks
• resolving one pending compaction task

Heuristic fallback can also run in the background after the host explicitly selects it during compaction resolution.

📁 Layout

skills/          repository-local agent skill instructions
src/cognitiveos/
  cli/          CLI entrypoint
  db/           SQLite schema and repository
  extractors/   File and URL extraction pipeline
  mcp/          MCP server entrypoint
  providers/    Provider abstractions

📝 Notes

Runtime Paths

• default runtime paths are user-level and host-agnostic under ~/.cognitiveos/
• passing --db-path keeps MEMORY.MD, logs, and snapshots anchored to the same runtime root
• bootstrap artifacts remain project-local by default under ./.cognitiveos/bootstrap/
• runtime locations can be overridden with COGNITIVEOS_HOME, COGNITIVEOS_DB_PATH, and COGNITIVEOS_MEMORY_OUTPUT_PATH

Bootstrap and Install

• bootstrap-host --host-kind generic is the default bootstrap path and generates local host guidance under .cognitiveos/bootstrap/
• bootstrap-host --host-kind codex --install writes a managed block into project-root AGENTS.md and .codex/config.toml
• the managed install uses the reduced compact-core profile and pins --project-root, --db-path, and --memory-output-path; for Codex, the mounted memory file is ~/.codex/MEMORY.MD instead of a project-root MEMORY.MD
• named host bootstrap targets remain registered memory outputs so future snapshot and dream sync can fan out across installed host surfaces
• on first startup, the host should call get_host_bootstrap_status; when onboarding is required, ask the generated questions and submit them with submit_host_onboarding
• files under .cognitiveos/bootstrap/ are runtime-generated local artifacts and are not meant to be repository-tracked source files

Project Conventions

• Durable engineering docs in this repository are kept in English.