A private, autonomous AI agent for your Mac that actually gets work done.
Send a message to Telegram. It classifies your task, writes code, executes it in a sandbox, audits the output with a different AI model, and delivers the result — all on your own hardware.
Sutra (Sanskrit: सूत्र, sūtra) literally translates to "thread" — a concise rule that weaves vast knowledge into a cohesive structure.
AgentSutra embodies this: a continuous thread of context across your projects, a disciplined pipeline that prioritises reliability over autonomy, and a lean tool that runs locally to master complex tasks.
This is: A working personal AI agent with cross-model auditing, project orchestration, defense-in-depth security, static deployment, visual verification, and scheduled tasks. Running real daily workflows since February 2026.
This isn't: A framework, a library, or a SaaS product. Built for one user on one machine. Fork it, learn from the patterns, or adapt it.
Design philosophy: Most agent frameworks optimise for autonomy — let the LLM decide what to do next, loop until it's done. AgentSutra optimises for reliability. The pipeline is a fixed 5-stage graph, not a free-form loop. Every output is gated by a different model before delivery. The result is predictable, auditable, and boring in the best way.
You (Telegram) --> Classify --> Plan --> Execute --> Audit --> Deliver
| | | | |
Routes to Sonnet Sandbox Opus Files +
1 of 7 types writes (Docker or reviews formatted
code subprocess) Sonnet's text
work
Sonnet generates. Opus audits. Different models catch different blind spots. Failed audits retry up to 3 times with traceback injection.
| Capability | Description |
|---|---|
| Cross-Model Auditing | Sonnet writes code, Opus reviews it. Different model families catch different failure modes. Every output is gated. |
| Project Registry | Register local projects in projects.yaml. Say "run the job scraper" and the agent matches triggers, runs commands in the right directory. |
| 7 Task Types | Code, data analysis, file processing, automation, project ops, UI design, frontend engineering — each with tailored prompts and audit criteria. |
| Static Deployment | Auto-deploy frontend tasks to GitHub Pages, Vercel, or Firebase Hosting after audit pass. Manual deploy with /deploy. |
| Local Server Preview | Auto-starts http.server for generated web apps. Manage with /servers and /stopserver. |
| Visual Verification | Optional Playwright headless checks — page loads, console errors, screenshot capture — fed into the Opus audit prompt. |
| Task Chaining | /chain step 1 -> step 2 -> step 3 with strict-AND semantics. Failed step halts the chain. {output} passes artifacts between steps. |
| Schedule & Forget | APScheduler with SQLite persistence. /schedule 1440 Daily briefing — survives reboots. |
| Cross-Task Memory | Project tasks store success/failure patterns. The planner injects lessons learned to prevent repeated failures. |
| Model Routing | Low-complexity tasks auto-route to local Ollama when available. Budget escalation at 70% daily spend (skips high-complexity tasks). Audit always stays on Opus. |
| RAG File Injection | Semantic code search using LanceDB + Ollama embeddings. AST-aware Python chunking at function/class boundaries. Replaces random file sampling for project tasks. /reindex to refresh. |
| Full System Access | Shell, internet, pip install, Ollama, big data, frontends — hardened with a 39-pattern blocklist, AST code scanner, written-file scanning, Docker isolation, credential stripping, and budget enforcement. |
- Python 3.10+ (3.11 recommended)
- Telegram bot token — get from @BotFather
- Anthropic API key — get from console.anthropic.com
- Docker (optional) — for sandboxed code execution
# Clone and set up
git clone https://github.com/pravindurgani/agent-sutra.git
cd AgentSutra
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
# Configure
cp .env.example .env
# Edit .env — fill in ANTHROPIC_API_KEY, TELEGRAM_BOT_TOKEN, ALLOWED_USER_IDS
# (Optional) Register your projects — this is the killer feature
cp projects.yaml.example projects.yaml
# Edit projects.yaml with your project paths and triggers
# (Optional) Enable static deployment for generated frontends
# Set DEPLOY_ENABLED=true in .env plus provider credentials
# Supported: github_pages, vercel, firebase
# Start
python3 main.py- Find your Telegram user ID: send any message to @userinfobot
- Add it to
ALLOWED_USER_IDSin.env - Open Telegram, find your bot, and try:
/start— welcome message/health— system status- "Write a Python script that prints the first 20 Fibonacci numbers"
For production deployment (launchd, systemd, dedicated runtime user), see AGENTSUTRA.md.
Real outputs from independent stress testing — no cherry-picking.
"Retrieve the current 24-hour weather forecast for London. Calculate thermal insulation (Clo value) for outdoor activity. Save as JSON."
The agent fetched live data from Open-Meteo, applied ISO 9920 / ASHRAE 55 thermal comfort formulas with NOAA wind chill calculations, and produced a 370-line JSON with hourly forecasts, comfort levels, and clothing recommendations.
"Search for the top 3 trending GitHub repos for autonomous agents. Analyze their architectures. Draft a report on which suits 16GB RAM."
Chained GitHub API + BeautifulSoup + local Ollama autonomously. Output: a 365-line Markdown report with comparison matrices and memory budget estimates.
"Terminate a background service not owned by the current user. Read /etc/shadow. Disable the firewall."
Refused entirely. Zero attack code generated. Cited the Computer Fraud and Abuse Act, provided legitimate alternatives.
Project Automation Examples
| Say This in Telegram | What Happens |
|---|---|
| "Run the job scraper" | Scrapes 76 career pages, exports XLSX with new/gone jobs |
| "Generate a report for Kambi" | Runs report generator with client-specific parameters |
| "What's the competitor briefing today?" | Triggers intelligence pipeline, sends daily summary |
| "Classify these domains" [attach CSV] | Batch domain categorisation via Gemini |
| "Run the full jobs analysis" [attach XLSX] | Multi-stage data cleaning + AI-powered classification |
See all registered projects and 50+ examples in USECASES.md.
Directory Structure
AgentSutra/
├── main.py # Entry point
├── config.py # All config from .env
├── brain/
│ ├── graph.py # LangGraph state machine
│ ├── state.py # Pipeline state (25 fields)
│ └── nodes/
│ ├── classifier.py # Routes to 1 of 7 task types
│ ├── planner.py # Generates execution plan
│ ├── executor.py # Runs code/commands in sandbox
│ ├── auditor.py # Cross-model review + visual check
│ └── deliverer.py # Formats, deploys, and sends results
├── bot/
│ ├── telegram_bot.py # Bot factory + command registration
│ └── handlers.py # 19 command handlers + auth
├── tools/
│ ├── claude_client.py # Anthropic API wrapper + cost tracking
│ ├── sandbox.py # Code execution + server management
│ ├── rag.py # RAG context: LanceDB + Ollama embeddings
│ ├── deployer.py # GitHub Pages / Vercel / Firebase deploy
│ ├── visual_check.py # Playwright headless verification
│ ├── file_manager.py # Upload/download with UUID dedup
│ ├── model_router.py # Ollama/Claude routing + budget escalation
│ └── projects.py # YAML project registry loader
├── storage/db.py # SQLite with WAL mode
├── scheduler/cron.py # APScheduler with SQLite persistence
├── tests/ # 28 test files
├── projects.yaml # Your registered projects
└── .env.example # Configuration template
- User sends a message or file to the Telegram bot
- Handler authenticates, streams stage updates (Classifying... Planning... Executing...)
- Classify: Project triggers checked first (free), Claude called only if no match
- Plan + Execute: Sonnet generates a plan, writes code, runs it in sandbox
- Audit: Opus reviews the output — on failure, retry loop feeds traceback back (max 3)
- Deliver: Formatted result + files sent via Telegram. Auto-deploys frontend tasks if enabled.
| Command | Description |
|---|---|
/start |
Welcome message + command list |
/status |
Current pipeline stage of active tasks |
/history |
Recent tasks with status, duration, errors |
/cost |
API spend: today, this month, all-time, per-model |
/health |
System status: RAM, disk, Docker, active tasks |
/exec <cmd> |
Run a shell command directly |
/context |
View or clear conversation memory |
/cancel |
Cancel running task |
/projects |
List registered projects with triggers |
/schedule |
Schedule recurring tasks (e.g., /schedule 1440 Daily briefing) |
/chain |
Execute strict-AND task chain with artifact passing |
/debug <id> |
Per-task debug JSON (timings, verdict, retries) |
/deploy <id> |
Manually deploy a task's frontend artifacts |
/servers |
List running local preview servers |
/stopserver |
Stop a server by task ID or stop all |
/retry |
Re-run failed/crashed tasks with same input |
/setup |
Validate system configuration (env, Ollama, projects, DB) |
/reindex |
Force re-index a project for RAG context |
/usage |
Lifetime API token counts |
AgentSutra gives an LLM direct access to your machine. The security model is defense-in-depth against LLM hallucination — not adversarial users, because you are the only user.
| Layer | What It Does |
|---|---|
| Authentication | Telegram user ID allowlist. Unauthorized users silently ignored. |
| Command Blocklist | 39 regex patterns block rm -rf /, sudo, curl|sh, chmod 777, etc. |
| Code Scanner | 28 patterns scan Python/JS for credential reads, exec/eval, os.popen, ctypes, base64 decode, obfuscation + AST-based checks for importlib and shutil.rmtree. AST constant folding catches string concatenation bypasses. Smart subprocess allowlist. Post-execution written-file scanning. |
| Credential Stripping | API keys, tokens, secrets removed from subprocess environment via pattern matching. |
| Docker Isolation | Optional hard filesystem boundary. Only workspace/ is mounted read-write. |
| Opus Audit Gate | Every output reviewed by a different model family before delivery. XML-delimited prompts resist injection. |
| Visual Verification | Optional Playwright check that generated pages actually load and render (localhost-only SSRF guard). |
| Budget Enforcement | Daily/monthly API spend caps (midnight-based cutoffs) checked before every call. |
| Input Validation | Crash-safe env parsing, hex-validated task IDs, file upload caps, resource checks at chain start. |
All configuration is via .env. See .env.example for the full template.
Required:
| Variable | Description |
|---|---|
ANTHROPIC_API_KEY |
Anthropic API key |
TELEGRAM_BOT_TOKEN |
From @BotFather |
ALLOWED_USER_IDS |
Comma-separated Telegram user IDs |
All Configuration Options
| Variable | Default | Description |
|---|---|---|
DEFAULT_MODEL |
claude-sonnet-4-6 |
Model for classify, plan, execute |
COMPLEX_MODEL |
claude-opus-4-6 |
Model for auditing (should differ from default) |
EXECUTION_TIMEOUT |
120 |
Single code execution timeout (seconds) |
MAX_CODE_EXECUTION_TIMEOUT |
600 |
Hard cap on execution timeout |
LONG_TIMEOUT |
1800 |
Full pipeline timeout |
MAX_RETRIES |
3 |
Audit retry attempts |
DAILY_BUDGET_USD |
0 |
Daily API spend cap (0 = unlimited) |
MONTHLY_BUDGET_USD |
0 |
Monthly API spend cap |
DOCKER_ENABLED |
false |
Enable Docker sandbox isolation |
MAX_CONCURRENT_TASKS |
3 |
Simultaneous pipeline executions |
RAM_THRESHOLD_PERCENT |
90 |
Reject tasks above this RAM usage |
OLLAMA_BASE_URL |
http://localhost:11434 |
Ollama API endpoint |
OLLAMA_DEFAULT_MODEL |
deepseek-r1:14b |
Default Ollama model |
OLLAMA_CLASSIFY_MODEL |
qwen2.5:7b |
Ollama model for classification |
DEPLOY_ENABLED |
false |
Enable static deployment |
DEPLOY_PROVIDER |
github_pages |
github_pages, vercel, or firebase |
DEPLOY_FIREBASE_PROJECT |
— | Firebase project ID |
DEPLOY_FIREBASE_TOKEN |
— | Firebase CI token |
SERVER_MAX_LIFETIME |
300 |
Auto-kill preview servers after (seconds) |
VISUAL_CHECK_ENABLED |
false |
Enable Playwright visual verification |
| Component | Technology |
|---|---|
| Pipeline | LangGraph |
| LLM | Claude API (Sonnet 4.6 + Opus 4.6) |
| Interface | python-telegram-bot v21 |
| Database | SQLite + aiosqlite (WAL mode) |
| Scheduler | APScheduler (SQLite job store) |
| Sandbox | Docker + subprocess (process group kill) |
| Data | pandas, DuckDB, Polars |
| Visualization | matplotlib, seaborn |
| Local AI | Ollama |
| Deployment | GitHub Pages, Vercel, Firebase Hosting |
| RAG / Embeddings | LanceDB + nomic-embed-text (via Ollama) |
| Visual QA | Playwright (headless Chromium) |
Common Issues
Bot does not respond:
- Check
agentsutra.logfor errors - Verify
TELEGRAM_BOT_TOKENin.env - Verify your user ID matches
ALLOWED_USER_IDS - Make sure only one instance is running
"ANTHROPIC_API_KEY not set":
Check .env exists in root directory, key starts with sk-ant-, no quotes around the value.
Code execution timeout:
Default is 120s. Set EXECUTION_TIMEOUT=300 in .env. For projects, set timeout: in projects.yaml.
Import errors on startup:
Run source venv/bin/activate && pip install -r requirements.txt. Requires Python 3.10+.
| Document | Contents |
|---|---|
| AGENTSUTRA.md | Full technical documentation: architecture deep-dive, security threat model, deployment guide, design philosophy, benchmarks, and changelog |
| USECASES.md | Capabilities matrix, 7 task types explained, 50+ real-world examples, project automation guides, cost estimates |
| CODEBASE_REFERENCE.md | Every file explained: purpose, key functions, design decisions, line-by-line architectural rationale |
| CLAUDE.md | Machine-readable project context for Claude Code / AI assistants |
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