Persistent state, evolving identity, accumulated learning — for any LLM.

Most LLM agents reset to zero every turn. Nūr keeps the room lit.

Memory, beliefs, drives, mood, learned skills, and the long arc of a relationship live as runtime state — inspectable, persistent, decaying, testable. The LLM still writes the words. Nūr changes the state those words come from.

In the agent-memory reference class (MemGPT, Letta, mem0, LangGraph state), Nūr is the one that also tracks identity continuity (constitution, beliefs, drives, self-traits) and relational continuity (rupture, repair, open commitments) alongside conventional memory.

Quickstart · Overview · Architecture · Admin & Deploy · Privacy · Changelog

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Honest scope. Nūr is an experimental runtime — not a therapist, not an AGI claim, not a consciousness claim. It is engineered scaffolding that holds the state behind an LLM's words across turns, sessions, and time. Memory and identity state live under data/ — use it with consent when other people are involved. See PRIVACY.md for inspection, export, and deletion.

What State Looks Like Across Turns

Same runtime, two different continuity arcs:

─── Relational arc (relationship memory + arc tracking) ───
Day 1
You:  You completely misunderstood me.
Nūr:  You're right. I missed what mattered there.
                                  # rupture recorded · trust drops · open loop created

Day 3
You:  I think I was too harsh earlier.
Nūr:  I remember that moment. We don't have to ignore it.
                                  # repair detected · loop begins to close

Day 10
You:  This feels easier now.
Nūr:  It does. There was tension here before, and it softened.
                                  # relationship arc persists across sessions

─── Identity arc (life-history ingest + belief revision + drive drift) ───
Mon
You:  Learn from this paper: <url>
Nūr:  Recorded. Three beliefs shifted, one drive intensified.
                                  # life-history event written · evolution trace emitted

Wed (no further input)
                                  # metabolism tick: weak beliefs decayed
                                  #                  recurring theme promoted to belief
                                  #                  drive-gap question opened

Fri
You:  Why did you push back on that?
Nūr:  A belief I formed Monday is shaping how I read this. Want to see it?
                                  # constitution + current beliefs surfaced for "why"

The LLM writes language. Deterministic state — memory retrieval, belief revision, decay, safety gates — lives outside the model. The assistant's stance accumulates instead of resetting.

Runtime Layers

Layer	What it does	Where it lives
Short-term memory	Hot turn-level memory inside the running session	in-process
Long-term memory	Distilled summaries with valence + spike flags; retrieval is valence-weighted	data/<user>/nur.db
Relationship arc	Rupture, repair, recurring tension, commitments, open loops; cross-session	data/<user>/nur.db
Semantic memory	Preferences, decisions, facts; topic-scoped retrieval	data/<user>/nur.db
Life History	Identity-level experience ledger; beliefs, drives, evolution events, themes	data/shared/life_history.db
Constitution	Operator-set stable orientation rendered above evolving beliefs every prompt	data/shared/life_history.db
Self-evolution	Wall-clock metabolism: belief decay, theme→belief promotion, drive-gap detection	runtime/life_history.py
Open questions	Reflection-emitted queue (contradiction / low-confidence / drive-gap) with operator lifecycle	data/shared/life_history.db
Ask-user surfacing	Drive-gated mid-conversation question with daily budget (LearningBudget)	runtime/learning/surface.py
Trigger-time skills	applies_when-filtered skill loading by chat hint	runtime/skills.py
Modulators	Six-dim affective state (arousal, valence, certainty, bonding, energy, resolution); decay over time, shift on events	core/emotion, session state file
Tool gates	Read-only by default; assisted autonomy needs confirmation; shell is a separate opt-in	core/dual_process/tool_loop.py

Every layer above is inspectable through /settings#observability and the OpenAPI surface at /docs.

Where Nūr Sits

Nūr is in the cognitive runtime / agent memory reference class, not the conversational-AI / companion class. The LLM still writes language; Nūr is the engineered layer that holds what to write against.

Reference class	What they store	Nūr also stores
Vector RAG	document chunks · embeddings	—
MemGPT / Letta / mem0	conversation summaries · facts · preferences	✓ via long-term + semantic memory
LangGraph (with persistence)	turn-graph state · scratchpad	✓ via session state + per-turn debug trace
Nūr — the additional layers
Relational continuity		rupture · repair · commitments · open loops
Identity continuity		constitution · beliefs · drives · self-traits
Self-evolution		wall-clock decay · theme→belief promotion · drive-gap detection
Affective state		six modulators with deterministic decay · valence-weighted retrieval
Open-question lifecycle		epistemic gaps surfaced for operator review

Every layer above is inspectable, decayable, and testable. None of them require the LLM to "feel" anything.

Life History and identity continuity — formative experiences that bias future behavior

Nūr has an early Life History layer for formative material: pasted texts, notes, essays, browser-uploaded text/Markdown files, and explicit chat requests like "learn from this project: ". This is not just a summarizer. It records an experience, then writes an inspectable evolution trace: belief shifts, drive changes, self-trait observations, and future behavior tendencies.

That means the project has two distinct continuity layers:

• Relational continuity — how Nūr remembers people, tension, repair, and unfinished business.
• Identity continuity — how Nūr records experiences that may change its worldview, motivations, and self-model over time.

It is observable in /settings → Life History and stored under data/shared/life_history.db. The settings workspace includes an evolution snapshot: first/latest experience, strongest drive drift, dominant drive pressure, and change-type mix. Runtime sessions load a compact slice of current beliefs, shifted drives, and recent evolution into generation, so formative experiences can bias Nūr's perspective without dumping raw source material into every prompt.

When a user explicitly asks Nūr to learn from a URL, the runtime fetches readable text, preserves the source reference, writes the Life History event, and appends a short learning receipt. Ordinary search, browsing, and casual conversation do not mutate identity-level Life History.

Quickstart

Nūr is not published on PyPI yet. Install from the GitHub repository:

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install "git+https://github.com/balfiky/nur.git"
nur-setup

nur-setup is terminal-native by default. It prompts for model backend, identity, Telegram, and tool settings, then creates runtime_config.yaml, local data/workspace folders, and marks setup complete. It does not launch a browser.

Start the browser UI only when you ask for it:

nur-web --config runtime_config.yaml

If you prefer the browser wizard instead of terminal prompts, run it explicitly:

nur-setup --web --config runtime_config.yaml

For a LAN/public bind, run the same command with a public host:

nur-web --host 0.0.0.0 --port 8000 --config runtime_config.yaml

No API token is required by default. If you later set api_key in /settings, the browser settings workspace has an API Token button for that hardened mode.

To remove a local Nūr workspace:

nur-uninstall --dry-run
nur-uninstall
python3 -m pip uninstall project-nur

nur-uninstall removes the runtime config and local data directory after a confirmation prompt. It keeps external tool workspaces and $NUR_CONFIG_DIR identity files unless you explicitly pass the removal flags shown in nur-uninstall --help.

To keep your customized agent identity across upgrades:

export NUR_CONFIG_DIR=~/.config/nur
mkdir -p "$NUR_CONFIG_DIR"

From Source

git clone https://github.com/balfiky/nur.git
cd nur
python3 -m pip install -e ".[dev]"
nur-web                    # web UI at :8000
nur-setup                  # terminal setup
nur-setup --web            # browser setup wizard
nur-uninstall              # remove local config/data after confirmation
nur-validate --mode full   # local release-readiness validation
nur                        # console runtime

What's new — recent self-evolution work (full notes in CHANGELOG.md)

• Learning schedule — LearningBudget caps and metabolism min-elapsed-days are now operator-tunable in /settings.
• Constitution layer (/admin/identity/constitution) — stable operator-set orientation rendered above evolving beliefs every prompt.
• Open questions queue — reflection now emits epistemic gaps (contradiction, low-confidence, drive-gap) for operator review.
• Self-evolution metabolism — wall-clock decay, weak-belief revocation, theme→belief promotion, drive-gap detection.
• Ask-user autonomy (Sprint 5) — Nūr can surface an open question to the user when budget and drives align.
• No mock backend — every LLM call goes to a real model (Ollama, Codex CLI, hosted OpenAI-compatible).

Start Here

Need	Document
Product/concept overview	docs/OVERVIEW.md
Reviewer study guide	docs/STUDY_GUIDE.md
Runtime architecture	docs/ARCHITECTURE.md
Install, admin, deployment	docs/DEPLOYMENT_AND_ADMIN.md
Security model	SECURITY.md
Privacy and data deletion	PRIVACY.md
Release history	CHANGELOG.md
Contributing	CONTRIBUTING.md

Validation

Use nur-validate before publishing or tagging releases. In a source checkout it runs repository checks; in an installed workspace it automatically switches to installed-package checks.

nur-validate --mode quick    # static metadata/docs/config checks
nur-validate --mode full     # quick + pytest + wheel/package-data checks
nur-validate --mode release  # full + tag and install-smoke checks

CI runs nur-validate --mode ci plus the full pytest suite on Python 3.10, 3.11, and 3.12.

Configure An LLM

The setup wizard or /settings workspace is the preferred path.

Backend	Use case
provider	Hosted OpenAI-compatible gateways
openai_compatible	Local/remote servers (Ollama, LM Studio, vLLM)
codex	Local Codex CLI session using your existing Codex login

Nūr always calls a real LLM. There is no mock or offline backend — configure one of the options above before running.

For codex, install/login to the Codex CLI first. Nūr runs codex exec in read-only ephemeral mode. In /settings, choosing Codex CLI loads the installed Codex model catalog into the model dropdown; leave the model blank to use Codex's default.

Runtime config is runtime_config.yaml in the current working directory. Identity is config/soul.yaml, or $NUR_CONFIG_DIR/soul.yaml when the override is set.

Architecture At A Glance

Three bands: Channels speak in (Web UI, Telegram, Console, REST API) → Persistent Cognitive Core holds state across turns (Emotions · Memory · Identity · Self-evolution) → Capabilities act, each gated by intent and autonomy level (Skills · Web search · File ops · Shell).

A single turn runs five stages internally: deterministic pre-pass → gated deliberation (inner dialogue, tool loop, defense shaping) → master LLM generation → rule + LLM self-check → post-processing (memory writes, energy drain, debug trace). The LLM writes language; deterministic state, memory, safety gates, and retrieval happen around it.

For the full code-level component map (LLM backends, persistence files, tool executor wiring) and the per-turn cognitive flow diagram, see docs/ARCHITECTURE.md.

API Quick Tour

# Health, no auth required
curl http://127.0.0.1:8000/v1/health

# Chat, auth optional unless api_key is configured
curl -X POST http://127.0.0.1:8000/v1/chat \
  -H 'Content-Type: application/json' \
  -d '{"message":"hello","user_id":"demo","chat_id":"main"}'

# With auth enabled
curl -X POST http://127.0.0.1:8000/v1/chat \
  -H 'Authorization: Bearer YOUR_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{"message":"hello","user_id":"demo","chat_id":"main"}'

OpenAPI docs at http://127.0.0.1:8000/docs when the server is running.

Production Posture

Before exposing Nūr beyond localhost:

• Set api_key so admin and chat endpoints require bearer auth
• Keep tools_enabled: false and shell_tool_enabled: false unless explicitly needed
• Use first-class read-only tools for routine host facts; shell is a separate local-command surface
• Set Telegram allowlists before enabling a bot
• Treat data/, runtime_config.yaml, and backups as sensitive
• Read docs/DEPLOYMENT_AND_ADMIN.md, SECURITY.md, and PRIVACY.md

Common Commands

make test                                        # 1830 unit/integration tests, ~3 min
make uat                                         # 64 UAT tests against a live LLM, ~14 min
make uat-comprehensive                           # one PASS/FAIL aggregator over the full UAT suite
python3 -m pytest tests/test_interface.py -q     # focused interface tests
python3 -m evals --backend openai_compatible --tag phase11  # behavioral eval pack
python3 -m build --sdist --wheel                 # build wheel and sdist

make uat defaults to the Codex CLI backend. Override with NUR_UAT_BACKEND=openai_compatible NUR_UAT_BASE_URL=... NUR_UAT_MODEL=... make uat. For backend selection, headed mode, artifact paths, and the comprehensive aggregator pattern, see docs/UAT.md.

Repository Layout

config/     YAML config and prompt templates
core/       Cognitive state, appraisal, memory, profiles, dual process
runtime/    Runtime config, channels, session lifecycle, tool factory
interface/  FastAPI app, /v1 API, client, bundled web UI
evals/      Behavioral scenario and ablation harness
tests/      Unit, integration, regression, runtime, and API tests
docs/       Public docs, design docs, diagrams, research notes

Current Status

Version 0.28.13. Reproducible eval evidence is intentionally narrow: relationship memory remains load-bearing under Phase 11, Life History now has bounded structural influence through LifeInfluence under Phase 13, and semantic memory has a dedicated structural scenario suite. The web and Telegram surfaces expose this state through presentation-only introspection. End-to-end behavior is exercised by 64 live-LLM UAT tests covering chat flows, admin surfaces, browser interactions, file uploads, tool calling, skill acquisition, and the self-evolution mechanics (including the operator-tunable learning schedule). These are structural/inspectable results, not proof of human-likeness, therapeutic value, consciousness, or psychological validity.

Known Gaps

• character_independence is a runtime config flag with no enforcement code yet — wizard toggles persist correctly but do not freeze identity edits.
• No skill-from-URL download. Skills are imported via paste, file path, or zip upload only.
• No automatic conversation→life-history ingestion. Drives and beliefs only shift from operator-curated /admin/life/experiences/* ingest, not from chat content.