A fast, lightweight runtime for building production AI agents in Python and TypeScript. ⚡

Agent RT gives agents a clear execution boundary for tools, streaming, structured output, permissions, approvals, memory, checkpoints, sandboxing, observability, and deployment—without requiring a heavyweight orchestration stack.

One runtime. Two languages. Any model provider. Production controls built in. 🧩

🚀 Quick Start

Python

pip install agent-rt

TypeScript / Node.js

npm install agent-rt

➡️ Installation · Quickstart

✨ Why Agent RT?

• 🪶 Lightweight and measurable — start with the runtime core and add integrations only when needed; reproducible cross-framework benchmarks live in comparison/.
• 🛡️ Controlled — permissions, composable action blockers, approvals, deterministic PII morphing, limits, guardrails, and sandbox boundaries are runtime concepts, not add-ons.
• 🔌 Provider-neutral, two languages — OpenAI, Anthropic, compatible endpoints, or custom providers, with aligned contracts in Python and TypeScript.
• 🔄 Migration-tested — ongoing compatibility field testing has 69 third-party repositories passing their test suites both before and after migration to Agent RT.

Decision models (such as Laya and other Jev-compatible models) answer typed choice/score questions for runtime control-plane gates instead of generating text. Toolbase can combine local, deferred, and MCP tools, remove unsafe definitions with a Decision model, pass only the relevant subset to each turn, and keep tool_search available for safe fallback discovery; the same Decision layer also gates memory, reranks retrieval results with optional top-k selection, and classifies failures.

⚖️ Quick Comparison

	⚡ Agent RT	🦜 LangChain	🗂️ LlamaIndex
🎯 Primary focus	🧠 Agent runtime	🧩 App/orchestration framework	📚 Data & RAG framework
Python + TypeScript	✅	✅	✅
🛠️ Tools & agent loop	✅ Built-in	✅	✅
🔐 Permissions & approvals	✅ Built-in	◐	◐
🛡️ Agent Action Guard	✅ Built-in integration (opt-in)	—	—
🧰 API & CLI tools	✅ Built-in (optional packages)	—	—
🌐 OpenAI + Anthropic compatible API	✅ Built-in server adapters	◐ Integration-dependent	◐ Integration-dependent
🧠 Decision-model control gates	✅ Tool pruning, memory/retrieval gates, failure classification	—	—
✂️ Tool catalog pruning before model call	✅ Built-in	◐ Integration-dependent	◐ Integration-dependent
📦 Sandboxing	✅ Fail-closed backend selection	◐	◐
♻️ Budget-preserving checkpoints	✅ Resume with prior turn/tool/token budgets	◐	◐
💾 Memory & checkpoints	✅ Built-in	✅	✅
🗃️ Switch vector DBs	✅ Simple env/registry switch	◐ Integration-dependent	◐ Integration-dependent
Post-retrieval reranking	Built-in provider-neutral hook + optional top-k	Available	Available
🔌 Provider-neutral	✅	✅	✅
🪶 Lightweight runtime focus	✅	◐	◐
Fresh install footprint	46.43 MiB	69.87 MiB	219.91 MiB
Warm latency	1.56 ms	9.03 ms	4.26 ms
Runtime memory	+5.86 MiB	+19.77 MiB	+22.42 MiB
Fresh install footprint	1.24 MiB	82.44 MiB	60.38 MiB
Warm latency	1.85 ms	2.60 ms	2.23 ms
Runtime memory	+14.33 MiB	+17.27 MiB	+16.79 MiB

✅ Built-in · ◐ Available / integration-dependent · — Not a primary focus

📊 See reproducible benchmarks →

🧪 See It in Action

A tool the model may call, restricted by a capability grant and a model-call budget (Python shown; see the TypeScript package for the npm quick start):

registry = ToolRegistry()
registry.register(
    ToolDefinition(
        name="get_weather",
        description="Get the current weather for a city.",
        input_schema={
            "type": "object",
            "required": ["city"],
            "properties": {"city": {"type": "string"}},
            "additionalProperties": False,
        },
        side_effect="read",
    ),
    handler=get_weather,  # async (arguments, cancellation_token) -> dict
)

loop = AgentLoop(
    load_model(),  # OpenAI, Anthropic, or any compatible endpoint
    tool_registry=registry,
    capability_grant=CapabilityGrant(tools=("get_weather",)),
    execution_budget=ExecutionBudget(ExecutionBudgetLimits(max_model_calls=5)),
)
result = await loop.run(agent, messages)

Arguments are validated against the schema, tools outside the grant never run, and the budget stops runaway loops. Full runnable versions are in the examples.

📚 Learn More

Topic	Documentation
Runtime model	How Agent RT executes agents
Tools & safety	Tools, policy, permissions, approvals, and guardrails
State & memory	State, memory, checkpoints, and artifacts
Production	Sandboxing, observability, evaluation, and deployment
Migration	LangChain, LlamaIndex, OpenAI, and Anthropic migration
Runtime guide	Providers, API server, CLI, sandboxes, skills, and migration entry points
Features	Features, defaults, and configuration
Architecture	Architecture and extension contracts
Development	Contributor setup and test workflows

🌐 Full documentation: agent-rt-pro.github.io/docs

📦 Packages

Python · TypeScript · Python examples · TypeScript examples

The inbound API server, terminal CLI, and AutoGen/CrewAI migration shims are Python-only.

📄 License

MIT — see LICENSE.

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