tename 0.1.0

Open-source, model-agnostic production runtime for AI agents. Durable sessions, sandbox isolation, and per-model YAML profiles.

Tename

The open-source production runtime for any agent framework. Durable sessions, sandbox isolation, and per-model YAML profiles — so the same agent code runs well on Claude today and any other model tomorrow.

---

What is Tename?

Tename is an open-source runtime that sits beneath your agent framework — whether that's Deep Agents, Claude Agent SDK, or your own custom code. It provides the infrastructure layer most teams end up rebuilding from scratch:

• Durable sessions that survive crashes. Kill the process mid-stream, restart, and the agent resumes cleanly from an append-only event log.
• Per-model optimization via YAML profiles. Same agent code, different model, one config change. Each profile encodes caching strategy, context budget, tool format, and known quirks.
• Sandbox isolation for LLM-generated code. Docker containers with CPU, memory, and PID limits. Credentials never enter the sandbox.
• Framework adapters so you bring your existing code. Tename doesn't replace Deep Agents — it makes Deep Agents production-grade.

30-second look

from tename import Tename

with Tename() as client:
    agent = client.agents.create(
        name="coding-agent",
        model="claude-opus-4-6",
        system_prompt="You are a careful software engineer.",
        tools=["python", "bash", "file_read", "file_write"],
    )

    session = client.sessions.create(agent_id=agent.id)

    for event in session.send("Compute the 50th Fibonacci number in Python."):
        if event.type == "assistant_message" and event.payload.get("is_complete"):
            print(event.payload["content"])

When GPT-5 ships a profile, switching models is one field change:

agent = client.agents.create(
    model="gpt-5",   # ← just this
    ...
)

The runtime handles the rest — different caching strategy, different tool format, different context management — all from the YAML profile.

Why Tename exists

Teams building production AI agents face a forced choice:

Option	Pros	Cons
Proprietary runtimes (Anthropic Managed Agents, AWS Bedrock AgentCore)	Production-grade infrastructure	Locked to one model provider
Open frameworks (Deep Agents, Claude Agent SDK)	Model flexibility, code ownership	You build all the infrastructure yourself

Tename is the third option: production-grade infrastructure that works with any model and any framework.

Architecture

Your Code → Python SDK → Harness Runtime → Model Router → Any Provider
                               ↕                ↕
                         Session Service     Sandbox (Docker)
                          (Postgres)            ↕
                                          Tool Proxy + Vault

Three decoupled interfaces (brain, hands, state) that fail and recover independently. See docs/architecture/overview.md.

What's in v0.1

• Session Service with append-only event log, idempotent writes, and advisory-lock-serialized concurrent emitters
• Stateless Harness Runtime with crash-safe resume
• Model Router with Anthropic provider and streaming
• YAML profile system with inheritance, validation, and a bundled Claude Opus 4.6 profile
• Docker sandbox with six built-in tools (bash, python, file_read, file_write, file_edit, file_list)
• Vault (PBKDF2 + Fernet) for encrypted credential storage
• Tool Proxy that injects credentials at call time — credentials never reach the sandbox or the session log
• Deep Agents framework adapter
• Vanilla adapter (no-framework fallback)
• Python SDK (sync + async)
• tename vault CLI for credential management
• 5 benchmark tasks for profile validation
• 3 worked examples

Coming in v0.2: GPT-5 and Gemini profiles, Claude Agent SDK adapter, summarization compaction, OpenTelemetry tracing, TypeScript SDK.

Getting started

Prerequisites

• Python 3.12+
• Docker (for Postgres and the code sandbox)
• An Anthropic API key

Install

pip install tename

Spin up Postgres and apply the schema

git clone https://github.com/deadLemonX/Tename
cd Tename
make dev         # docker compose up postgres
make migrate     # apply schema

Run the hello-world

export ANTHROPIC_API_KEY=sk-ant-...
python examples/01-hello-world/main.py

See docs/QUICKSTART.md for the full 10-minute walkthrough, including troubleshooting.

Examples

• 01-hello-world — simplest possible agent
• 02-research-agent — Deep Agents planning + web_search (Tavily)
• 03-coding-agent — writes and runs Python in the sandbox

Documentation

Doc	What it covers
QUICKSTART	Clone → install → running agent in 10 minutes
Product vision	What Tename is and why it exists
Principles	Non-negotiable architectural commitments
Architecture overview	How the system fits together
Profile format	YAML profile schema reference
SDK design	Python SDK API reference
Deployment	Running Tename in production

Contributing

We welcome contributions! The fastest way to contribute is a new model profile — one YAML file. See CONTRIBUTING.md for how to set up the dev environment, run tests, and submit PRs.

How it compares

	Tename	Anthropic Managed Agents	AWS Bedrock AgentCore	Deep Agents
Type	Open-source runtime	Proprietary managed service	Proprietary managed service	Open-source framework
Models	Any	Claude only	Bedrock catalog	Any (no tuning)
Per-model tuning	Yes (profiles)	Yes (internal)	Limited	No
Durable sessions	Yes	Yes	Yes	No (DIY)
Sandbox isolation	Yes (Docker)	Yes (microVMs)	Yes	No (DIY)
Credential isolation	Yes (vault + proxy)	Yes	Yes (IAM)	No (DIY)
Self-hosted	Yes	No	AWS only	N/A (library)
License	Apache 2.0	Proprietary	Proprietary	MIT

Tename doesn't compete with Deep Agents — it runs beneath it. Use Deep Agents for orchestration, Tename for infrastructure.

License

Apache 2.0. See LICENSE.

Status

v0.1 is the initial public release. The runtime is feature-complete for single-developer local use. Please file issues and PRs — the project is maintained as an individual side-project and feedback genuinely shapes v0.2.