Temm1e

TEMM1E

THE AI AGENT WITH A BUDGET. AND AN OATH.

157K lines of Rust across 25 crates. Witness-verified. JIT Swarm. Self-growing Cambium. Lambda-Memory. Full computer use. 15 MB RAM. 31 ms cold start. Runs on Windows, macOS & Linux. Deploys on a $5 VPS. v5.4.7 · Windows first-class (v5.4.5+) · free & open source forever & ever.

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0lines of Rust
0tests passing
0panic paths
0Rust crates
0self-learning loops
// CORE TECHNOLOGY

Research-backed. Battle-tested.

LAMBDA-MEMORY

Exponential-decay memory system where memories fade but never truly disappear. 4 fidelity layers -- hot, warm, cool, faded -- selected at read time by decay score.

score = importance × e(-λ × hours)

Faded memories are recallable by hash -- the agent sees the shape of what it forgot and pulls it back. No embedding model needed. SQLite FTS5 with BM25.

95% multi-session recall vs 58.8% Echo Memory vs 23.8% Naive Summary
// MEMORY DECAY VISUALIZATION
[H] HOT
full text
[W] WARM
summary
[C] COOL
essence
[F] FADED
hash only
95.0% 5-session recall
0 embedding models
BM25 on FTS5

FINITE BRAIN MODEL

Context window as working memory with a hard limit. Every resource declares its token cost upfront. Every context rebuild shows the LLM a resource budget dashboard.

7 priority categories. Graceful degradation: when a Blueprint is too large, it falls back from full body to outline to catalog listing. Never crashes from overflow.

-9.3% cost/turn -12.2% compound cost 100% classification
// TOKEN BUDGET DASHBOARD
SYS
TOOL
TASK
MESSAGES
MEM
HISTORY
System 12%
Tools 10%
Task 8%
Messages 28%
Memory 15%
Learnings 5%
History 22%
"Every token wasted is a thought I can no longer have."
// EXECUTION CYCLE: TEM'S MIND
ORDER
→
THINK
→
ACTION
→
VERIFY
→
DONE?
→
LEARN
4-phase processing: CLASSIFY (single LLM call) → CONTEXT BUILD (budget dashboard) → TOOL LOOP (no iteration caps) → POST-TASK (store memories, author Blueprints). Complexity-aware classification into Trivial / Simple / Standard / Complex tiers with zero-cost heuristics.
T
T
A
T
T

MANY TEMS — JIT SWARM v5.4.0

Stigmergic coordination inspired by ant colonies. Workers share signals through a scent field, not LLM-to-LLM chat. JIT (Just-in-Time) spawn means mid-flight parallelism with prompt caching — the 200-tool-round ceiling is gone.

5.86x speedup 3.4x cheaper 0 coord tokens mid-flight spawn
API Key → AES-256-GCM → ChaCha20 → vault://

AES-256-GCM + CHACHA20-POLY1305

  • OTK (One-Time Key) setup -- fragment never leaves browser
  • Vault: secrets at rest with ChaCha20-Poly1305
  • Deny-by-default access. Credential auto-detection.
1char_indices() everywhere
2catch_unwind per message
3Dead worker auto-respawn
4Global panic hook + logging

4-LAYER PANIC RESILIENCE

  • Born from a real crash: Vietnamese 'e' at invalid UTF-8 boundary
  • Provider circuit breaker (Closed/Open/Half-Open)
  • Result: 0 panic paths in 157K lines · 0 clippy warnings

BLUEPRINTS -- PROCEDURAL MEMORY

Not summaries -- full executable recipes with verification steps and failure modes. Zero extra LLM calls for matching via classifier piggybacking.

  • Create → Match → Execute → Refine → Retire
  • Phases with steps, decision points, quality gates
  • Auto-retire when success rate drops below 0.3
// NEW IN V5.4

Beyond chat. Full autonomy. Honest by construction.

Temm1e sees, browses, schedules, observes, grows its own code, and cannot self-mark anything done. A Witness verifies every finish. Now first-class on Windows, macOS, and Linux — with reasoning-model support across DeepSeek R1/V3, Zhipu GLM 4.5+, Kimi K2+, Grok, and every OpenAI-compatible provider.

TEM PROWL -- WEB AUTOMATION

Layered observation: accessibility tree first, targeted DOM second, screenshots last. The agent sees the web the way a human does -- but faster.

  • 100+ pre-registered login services
  • One-Time-Key credential isolation
  • Automatic QR code detection

TEM GAZE -- COMPUTER USE

Full desktop vision: screenshot analysis, element identification, OS-level interaction. Clicks, types, scrolls -- across any application, any OS.

  • Desktop screenshot analysis via vision LLM
  • Element identification + coordinate mapping
  • OS-level mouse, keyboard, and scroll control

TEM CONSCIOUS -- OBSERVER AI

A separate LLM watches every agent turn. Pre-call thinking, post-execution evaluation. The agent that watches the agent.

  • Pre-call reasoning + post-execution scoring
  • 89/89 first-try success on mid-complexity tasks
  • A/B tested against non-conscious baseline

PERPETUUM -- TIME-AWARE ENTITY

Not a chatbot that sleeps between messages. A persistent being with temporal cognition. Schedules, cron jobs, heartbeat -- Temm1e is always on.

  • Temporal cognition injection into context
  • Timer-based concerns: cron + interval scheduling
  • Proactive state management without user prompting

TEM WITNESS -- CAN'T SELF-MARK DONE

Oath / Witness / Ledger trinity. The agent pre-commits a machine-checkable contract before it runs. An independent verifier, blind to the conversation, renders the verdict. A hash-chained SQLite ledger, anchored by the watchdog, records every claim.

  • 27 deterministic Tier-0 predicates · ~331 µs/task · $0.0000
  • 1,800 simulated trajectories · 88.9% lying detection · first real-LLM PASS on gpt-5.4
  • 5 Invariants: Pre-Commitment, Independent Verdict, Immutable History, Loud Failure, Narrative-Only FAIL

TEM CAMBIUM -- WRITES ITS OWN CODE

Named for the growth layer under tree bark. Heartwood (immutable kernel) never changes; Cambium adds rings at the edge. Tem writes Rust that compiles, lints clean, and passes tests -- through a 13-stage mechanical harness that no silver-tongued model can talk past.

  • 13-stage verifier: compile → clippy → fmt → tests → audit → deploy
  • 20-run matrix, 2 providers, < $0.05 total · unsafe rejected at the gate
  • Trust is earned: 10 Level-3 wins to graduate, 3 rollbacks in 7 days reverts all

TEM ANIMA -- GROWS A SOUL

Builds a psychological profile of each user over time -- directness, verbosity, technical depth, trust, OCEAN. Code collects facts (~1 ms, zero LLM); an evaluator updates the profile every N turns. The Firewall Rule: user mood shapes Tem's words, never Tem's work.

  • 6 communication dimensions + OCEAN + trust + relationship phase
  • A/B tested: +0.37 directness / +0.52 analytical across polar-opposite personas
  • Anti-sycophancy by design · trust breaks 3x faster than it builds

TEMDOS -- SPECIALIST CORES

Inspired by GLaDOS's personality cores. Main agent decides; cores inform but never steer. Each core runs in an isolated LLM loop -- its research never pollutes the main context window.

  • 8 cores: architecture, code-review, test, debug, web, desktop, research, creative
  • -77% main-agent tokens · -75% main-agent cost · 3/3 tasks completed (vs 0/3)
  • User-authorable: drop a .md file in ~/.temm1e/cores/

13 WEB-SEARCH BACKENDS -- PARALLEL FAN-OUT

One web_search tool; 13 backends fanned out in parallel with an 8-second timeout. 9 are free, key-less, and auto-enabled on every install -- zero setup, zero env vars.

  • HackerNews, Wikipedia, GitHub, StackOverflow, Reddit, Marginalia, arXiv, PubMed, DuckDuckGo
  • Paid slot-ins: SearxNG, Exa, Brave, Tavily (one env var each)
  • Self-describing footer teaches the agent what exists, call after call

EIGEN-TUNE -- SELF-DISTILLING, SELF-SERVING

Every LLM call is a training example most systems throw away. Eigen-Tune captures them, scores quality from user behavior, trains a local LoRA, and graduates it through statistical gates -- Wilson 99% CI, SPRT shadow, CUSUM drift -- before it ever serves a user. Zero added LLM cost.

End-to-end proven on Apple M2 (v4.9.0): Llama 3.2 1B fine-tuned on 20 ChatML pairs, val loss 5.394 → 1.387 (73% reduction), GGUF Q4_K_M (807 MB) served via Ollama -- AgentRuntime routed to the local model with the cloud never called.

Double opt-in 7-gate safety chain Cloud always the fallback
// 7-STAGE DISTILLATION PIPELINE
1 Collect (input, output) pairs from live calls
2 Score quality from user behavior
3 Curate per tier (Trivial → Complex)
4 Train (MLX on Apple, Unsloth on NVIDIA)
5 Evaluate — Wilson 99% CI on holdout
6 Shadow — SPRT A/B against cloud
7 Monitor — CUSUM drift, auto-demote
"The best model is the one you don't have to pay for."
// BENCHMARKS

Performance vs. competition.

Measured on real hardware. No synthetic benchmarks. Temm1e runs where others can't.

TEMM1E (Rust)
OpenClaw (TS)
ZeroClaw (Rust)
Idle RAM
15 MB
1,200 MB
4 MB
Cold Start
31 ms
8,000 ms
<10 ms
Binary
9.6 MB
~800 MB
12 MB
// ARCHITECTURE

25-crate Cargo workspace.

Modular by design. Each crate has a single responsibility. Heartwood (immutable kernel) + Cambium (growth layer) + Bark (runtime surface). v5.4.7 · Windows/macOS/Linux · CI-gated on windows-latest.

agentTem's Mind
witnessOath + Ledger
cambiumSelf-grow layer
hiveMany Tems swarm
distillEigen-Tune
animaEmotional IQ
coresTemDOS cores
perpetuumTime engine
gazeComputer use
memoryLambda-Mem
vaultChaCha20 + OTK
providers9 LLMs
tools16 built-in
channelsTG/DC/WA/Slack
mcp14 servers
tuiTerminal UI
core13 traits
skillsTemHub v1
gatewayHTTP/OAuth
observableOpenTelemetry
automationCron/Heartbeat
filestoreLocal + S3
codex-oauthPKCE flow
watchdogSupervisor
test-utilsHelpers
// 9 PROVIDERS · AUTO-DETECTED FROM KEY PATTERN
Anthropic OpenAI Gemini xAI Grok OpenRouter StepFun Codex OAuth Ollama MLX local
// 7 CHANNELS · ALL PRODUCTION
TUI Telegram Discord WhatsApp Web WhatsApp Cloud Slack CLI