Everything I'm made of. Every module, every capability. 905 tests. 0 clippy warnings. Here's the full inventory.
| # | Feature | Module | Status |
|---|---|---|---|
| 0.1 | Graceful Shutdown | src/main.rs |
Done |
| 0.2 | Provider Circuit Breaker | temm1e-agent/src/circuit_breaker.rs |
Done |
| 0.3 | Channel Reconnection with Backoff | temm1e-channels/src/telegram.rs |
Done |
| 0.4 | Streaming Responses | temm1e-agent/src/streaming.rs |
Done |
| 0.5 | Raised max_turns/max_tool_rounds | temm1e-agent/src/runtime.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 1.1 | Verification Engine | temm1e-agent/src/runtime.rs |
Done |
| 1.2 | Task Decomposition | temm1e-agent/src/task_decomposition.rs |
Done |
| 1.3 | Persistent Task Queue with Checkpointing | temm1e-agent/src/task_queue.rs |
Done |
| 1.4 | Context Manager — Surgical Token Budgeting | temm1e-agent/src/context.rs |
Done |
| 1.5 | Self-Correction Engine | temm1e-agent/src/self_correction.rs |
Done |
| 1.6 | DONE Definition Engine | temm1e-agent/src/done_criteria.rs |
Done |
| 1.7 | Cross-Task Learning | temm1e-agent/src/learning.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 2.1 | Watchdog | temm1e-agent/src/watchdog.rs |
Done |
| 2.2 | State Recovery | temm1e-agent/src/recovery.rs |
Done |
| 2.3 | Health-Aware Heartbeat | temm1e-automation/src/heartbeat.rs |
Done |
| 2.4 | Memory Backend Failover | temm1e-memory/src/lib.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 3.1 | Output Compression | temm1e-agent/src/output_compression.rs |
Done |
| 3.2 | System Prompt Optimization | temm1e-agent/src/prompt_optimizer.rs |
Done |
| 3.3 | Tiered Model Routing | temm1e-agent/src/model_router.rs |
Done |
| 3.4 | History Pruning with Semantic Importance | temm1e-agent/src/history_pruning.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 4.1 | Discord Channel | temm1e-channels/src/discord.rs |
Done |
| 4.2 | Git Tool | temm1e-tools/ |
Done |
| 4.3 | Skill Registry (TemHub v1) | temm1e-skills/src/lib.rs |
Done |
| 4.4 | Slack Channel | temm1e-channels/src/slack.rs |
Done |
| 4.5 | Web Dashboard (Minimal) | temm1e-gateway/src/dashboard.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 5.1 | S3/R2 FileStore Backend | temm1e-filestore/src/s3.rs |
Done |
| 5.2 | OpenTelemetry Observability | temm1e-observable/src/ |
Done |
| 5.3 | Multi-Tenancy with Workspace Isolation | temm1e-core/src/tenant_impl.rs |
Done |
| 5.4 | OAuth Identity Flows | temm1e-gateway/src/identity.rs |
Done |
| 5.5 | Horizontal Scaling via Orchestrator | temm1e-core/src/orchestrator_impl.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 6.1 | Parallel Tool Execution | temm1e-agent/src/executor.rs |
Done |
| 6.2 | Agent-to-Agent Delegation | temm1e-agent/src/delegation.rs |
Done |
| 6.3 | Proactive Task Initiation | temm1e-agent/src/proactive.rs |
Done |
| 6.4 | Adaptive System Prompt — Self-Tuning | temm1e-agent/src/prompt_patches.rs |
Done |
| # | Feature | Module | Status |
|---|---|---|---|
| 7.1 | Vision / Image Understanding | temm1e-core/src/types/message.rs, temm1e-providers/, temm1e-agent/src/runtime.rs |
Done |
I trap SIGTERM/SIGINT, drain my active ChatSlot workers, and flush pending memory writes. Tasks that can't complete within 30s get checkpointed for resume. No silent deaths.
My circuit breaker runs a state machine: Closed, Open (after N failures), Half-Open (after cooldown). I apply exponential backoff with jitter on transient errors (429, 500, 503). When multiple providers are configured, I failover automatically.
I run a supervised retry loop with exponential backoff for Telegram long-poll. I health-check my connection via heartbeat and log every reconnection attempt.
StreamBuffer + StreamingConfig + StreamingNotifier. I use Provider::stream() for final text responses. On Telegram, I edit messages in-place (throttled at 30 edits/min). During tool rounds, I push status updates so the user knows I'm working.
max_turns=200, max_tool_rounds=200, max_task_duration=1800s. Configurable via AgentRuntime::with_limits(). I don't quit early.
After every tool execution, I inject a verification hint into the tool result: "Did the action succeed? What evidence confirms this?" Zero API call overhead — prompt injection only. I verify every action.
My TaskGraph builds SubTask nodes with dependency edges. I topologically sort for execution order. Each subtask tracks its own status (Pending/Running/Completed/Failed/Blocked). Cycle detection prevents infinite loops. I break problems into components.
SQLite-backed TaskQueue. My TaskEntry stores task_id, chat_id, goal, status, and checkpoint_data (serialized session JSON). After each tool round, I checkpoint my session state. I survive process restarts.
I budget tokens across 7 priority categories: system prompt (always), tool definitions (always), task state (if present), recent 4-8 messages (always), memory search (15% cap), cross-task learnings (5% cap), older history (fill remaining). When I drop messages, I inject summaries so nothing is truly lost.
My FailureTracker counts consecutive failures per tool name. After my threshold (default 2), I inject a strategy rotation prompt: "This approach has failed N times. Try a fundamentally different approach." I don't repeat mistakes.
I detect compound tasks (multiple verbs, numbered lists, "and"/"then" connectors). I inject DONE criteria for the LLM to articulate verifiable completion conditions. On the final reply, I append a verification reminder. A task isn't done until I can prove it's done.
My extract_learnings() analyzes completed history — tools used, failures, strategy rotations — and produces a TaskLearning with task_type, approach, outcome, and lesson. I store these in memory as LongTerm entries with learning:{uuid} IDs. My context builder searches and injects up to 5 past learnings (5% token budget) into the THINK step of future tasks. Verified working: a shell task produces a learning, and the next session's context allocation shows learnings=25 tokens injected. I get better over time >:3
I monitor my own subsystems (provider, memory, channel, tools). WatchdogConfig sets check intervals and failure thresholds. My HealthReport tracks per-subsystem status. When something degrades, I auto-restart it.
My RecoveryManager detects corrupted state (broken sessions, orphaned tasks). I generate a RecoveryPlan with actions: Restart, Rollback, Skip, Escalate. This integrates with my task queue checkpoints.
My heartbeat checks subsystem health via the watchdog. I report degraded or failed subsystems and adjust my check interval based on overall system health.
I automatically fail over from primary to secondary memory backend on failure. The primary/secondary pair is configurable. When my primary recovers, I switch back.
I compress large tool outputs before storing them in context. I extract key information and discard verbose noise. For shell output, I keep the first and last N lines with a summary in between.
My SystemPromptBuilder handles composable prompt construction. I inject workspace path, tool names, file protocol, verification rules, DONE criteria rules, and self-correction rules. Token estimation keeps it tight.
My ModelRouter routes tasks to a ModelTier (Fast/Standard/Premium) based on TaskComplexity analysis. Simple questions get cheap, fast models. Multi-step tasks get premium models. I spend tokens where they matter.
My score_message() assigns MessageImportance (Critical/High/Medium/Low) based on role, content, and tool results. prune_history() removes the lowest-importance messages first. I preserve conversation coherence.
Full Channel + FileTransfer implementation via serenity/poise. I handle slash commands, message splitting, allowlist enforcement, and attachment handling. Behind the discord feature flag.
I perform typed git operations: clone, pull, push, commit, branch, diff, log. Safety: I block force-push by default and require explicit confirmation for destructive operations.
My SkillRegistry scans ~/.temm1e/skills/ and the workspace skills/ directory. I parse YAML frontmatter from Markdown and do keyword-based relevance matching. When a skill is relevant, I inject its instructions into my system prompt.
SlackChannel implementing Channel + FileTransfer. I poll via conversations.list + conversations.history every 2s. I use chat.postMessage and files.upload. Message splitting at 4000 chars, allowlist enforcement, rate limiting. Behind the slack feature flag.
4 handlers: dashboard_page (HTML), dashboard_health (JSON), dashboard_tasks (JSON), dashboard_config (redacted JSON). HTMX-based, dark theme, under 50KB, polls health every 10s. I serve it at /dashboard.
My S3FileStore uses aws-sdk-s3. I support R2/MinIO (custom endpoint + force_path_style). Multipart upload for store_stream(), presigned URLs, paginated listing. Behind the s3 feature flag.
My MetricsCollector uses atomic counters, RwLock gauges, and histograms. OtelExporter wraps it with an OTLP endpoint. 6 predefined metrics: provider latency, token usage, tool success rate, and more.
My TenantManager implements the Tenant trait. I enforce per-tenant workspace isolation (workspace/, vault/, memory.db). Rate limiting with day rollover. ensure_workspace() creates the isolation directories.
My OAuthIdentityManager holds an in-memory user store with PKCE support. start_oauth_flow(), complete_oauth_flow(), refresh_token(). I support multiple providers (GitHub, Google, AWS). I send the OAuth URL in chat, the user clicks, the callback hits my gateway, and I store the token.
My DockerOrchestrator uses a DockerClient abstraction. I enforce a max instances safety limit with no privilege escalation. KubernetesOrchestrator stub is in place. create_orchestrator() factory dispatches the right one.
My execute_tools_parallel() uses a Semaphore-based concurrency limit (max 5). detect_dependencies() uses union-find grouping — read-read operations run independently, write-write and write-read operations are dependent, shell operations are always dependent.
My DelegationManager uses an AtomicUsize-based spawn counter. plan_delegation() decomposes tasks via 4 heuristic strategies (numbered lists, semicolons, "then", "and"). Each SubAgent gets scoped model/tools/timeout. My sub-agents cannot spawn further sub-agents — no recursion. Max 10 per task, max 3 concurrent.
My ProactiveManager runs a TriggerRule system with 4 trigger types: FileChanged, CronSchedule, Webhook, Threshold. Disabled by default (global opt-in required). I rate-limit to 10 actions/hour with per-rule cooldowns. The requires_confirmation flag gates destructive operations.
My PromptPatchManager handles 5 patch types (ToolUsageHint, ErrorAvoidance, WorkflowPattern, DomainKnowledge, StylePreference). All patches start as Proposed. I only inject Approved patches. Auto-approve kicks in only for low-risk types above 0.8 confidence. Underperforming patches auto-expire. Max 20 patches. I tune myself.
ContentPart::Image variant with base64 data + media_type. I detect image attachments (JPEG, PNG, GIF, WebP), read from workspace, base64-encode, and include them as image content parts in provider requests. Anthropic format: {"type": "image", "source": {"type": "base64", ...}}. OpenAI format: {"type": "image_url", "image_url": {"url": "data:...;base64,...", "detail": "auto"}}. My context budgeting estimates ~1000 tokens per image.