Tvastar

The full-stack framework for building, running, and operating AI agents in production.

From a single prompt to fleet-scale autonomous systems — one framework handles the entire lifecycle.

Agent = Model + Harness
Loop  = Agent + Schedule + Verify + Handoff
Fleet = Loops + Routing + Budget + Observability

---

Quickstart

pip install tvastar

from tvastar import create_agent, Harness, default_toolset
from tvastar.model import AnthropicModel

agent = create_agent(
    "my-agent",
    model=AnthropicModel("claude-sonnet-4-6"),
    tools=default_toolset(),
)
result = await Harness(agent).run("Fix the failing tests")

print(result.ok)              # Did it actually work?
print(result.quality.grade)   # PASS / ACCEPTABLE / FAIL
print(result.quality.summary) # What went wrong (if anything)

Benchmark: 3,651 failed agent trajectories from tau2-bench. Tvastar detected 100%. Traditional monitoring detected 0%.

---

The Five Layers

┌─────────────────────────────────────────────────────────┐
│  5. FLEET — Multi-agent coordination, routing, budget   │
├─────────────────────────────────────────────────────────┤
│  4. LOOP — Autonomous: schedule, verify, retry, handoff │
├─────────────────────────────────────────────────────────┤
│  3. QUALITY — Score every run. Detect silent failures.  │
├─────────────────────────────────────────────────────────┤
│  2. HARNESS — Sessions, tools, sandbox, durability      │
├─────────────────────────────────────────────────────────┤
│  1. AGENT — Model + config. Any provider.               │
└─────────────────────────────────────────────────────────┘

Each layer is independently useful. Use just the Harness, or go all the way up to Fleet.

Layer	Problem it solves
Agent	One create_agent() call, works with any model (Anthropic, OpenAI, 100+ via LiteLLM)
Harness	Sessions, tools, sandbox, memory, compaction, structured output, MCP — you don't write this from scratch
Quality	Agents lie about success. 8 detectors catch what monitoring can't. Scores every run 0–100.
Loop	Agents run unattended — on schedules, with retry, with escalation when stuck
Fleet	Budget governance, semantic routing, alerting, versioned deploys across many agents

---

Loop in 60 Seconds

from tvastar.loop import Loop, LoopConfig

loop = Loop(agent, LoopConfig(
    name="ci-fixer",
    schedule="*/15 * * * *",   # every 15 min
    max_iterations=3,          # retry up to 3 times
    cancel_after=300.0,        # 5 min timeout per run
))
await loop.start()  # Runs forever. Fixes CI. Alerts you if stuck.

The Loop runs the agent, verifies the result, retries with exponential backoff, and escalates (Slack, email, webhook) only when it cannot fix something itself.

---

Sessions Survive Crashes

Sessions are event-sourced by default. With a persistent Store, they survive process restarts:

from tvastar.memory.store import FileStore

harness = Harness(agent, store=FileStore("./data"))
result = await harness.run("Fix auth tests", session_id="my-session")

# Later — even after restart:
session = harness.resume("my-session")

---

What Makes Tvastar Different

Competitor	What they miss
LangGraph	No loops, no fleet, no quality scoring
CrewAI	No autonomous loops, no verification
AWS AgentCore	No quality scoring, no loop engineering
LangSmith	Shows what happened — doesn't judge correctness

Everyone else does one layer. We do the full stack.

---

Key Numbers

Metric	Value
Python	3.10+
Core dependencies	Zero (all providers are optional extras)
Tests	2,500+
Detection accuracy	100% on tau2-bench (vs 0% traditional monitoring)
Model backends	Anthropic, OpenAI, LiteLLM (100+), Mock
Sandbox types	Virtual, Local, Docker, Remote
Storage backends	InMemory, File, SQLite
Built-in detectors	8

---

CLI

tvastar serve agent.py:agent          # HTTP server
tvastar chat agent.py:agent           # Interactive REPL
tvastar quality agent.py:agent "task" # Score a run (exit 1 if FAIL)
tvastar-fix --test-cmd "pytest"       # Auto-fix failing tests
tvastar-ci                            # Autonomous CI monitor
tvastar-comply audit loop.py:loop     # Compliance check

---

Documentation

Doc	What it covers
Getting Started	Install, first agent, first loop
Usage Guide	Sessions, tools, sub-agents, structured output
API Reference	Every public symbol, field, and signature
Cookbook	40+ patterns, recipes, and full examples
Loop Engineering	Schedules, retry, handoff, circuit breaker
Fleet	Multi-agent coordination, routing, budget
Architecture	ADRs and design decisions
Error Handling	What raises, what swallows, and why
SLOs	Reliability promises
Failure Modes	FMEA table — top 10 failure modes
Runbooks	Operational response for each alert type
Threat Model	Actors, boundaries, security primitives
Contributing	How to contribute

---

Install Extras

pip install tvastar[anthropic]   # Anthropic Claude models
pip install tvastar[openai]      # OpenAI models
pip install tvastar[litellm]     # 100+ providers via LiteLLM
pip install tvastar[all]         # Everything

---

Environment Variables

Variable	Purpose
ANTHROPIC_API_KEY	Anthropic model access
OPENAI_API_KEY	OpenAI model access
TVASTAR_STORE_PATH	Default FileStore location
TVASTAR_LOG_LEVEL	Structured log verbosity (DEBUG/INFO/WARNING/ERROR)

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License

Apache 2.0