timbal 2.2.1

Simple, performant, battle-tested framework for building reliable AI applications

Timbal

Simple, performant, battle-tested framework for building reliable AI applications.

Timbal gives you Agents (autonomous reasoning) and Workflows (explicit pipelines) behind one interface. No hidden magic: async functions, Pydantic validation, and event-driven streaming. If you know async/await, you already know how it works.

Documentation: docs.timbal.ai

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Quickstart

pip install timbal

import asyncio

from timbal import Agent
from timbal.tools import WebSearch

agent = Agent(
    name="assistant",
    model="anthropic/claude-sonnet-4-6",
    tools=[WebSearch()],
    max_tokens=1024,
)

async def main():
    result = await agent(prompt="What's new in AI this week?").collect()
    print(result.output)

asyncio.run(main())

Set ANTHROPIC_API_KEY (or the key for your chosen provider) in a .env file or your environment.

Workflows use the same interface:

import asyncio
import httpx

from timbal import Workflow
from timbal.state import get_run_context

async def fetch(url: str) -> str:
    async with httpx.AsyncClient(follow_redirects=True) as client:
        return (await client.get(url)).text

workflow = (
    Workflow(name="scraper")
    .step(fetch)
    .step(
        lambda content: len(content),
        content=lambda: get_run_context().step_span("fetch").output,
    )
)

async def main():
    result = await workflow.collect(url="https://timbal.ai")
    print(result.output)

asyncio.run(main())

See the Quickstart for the full app flow (timbal create → timbal start).

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Why Timbal

The most performant agent framework. In overhead benchmarks against LangGraph, CrewAI, the OpenAI Agents SDK, PydanticAI, and Agno (observability on both sides, faked LLMs), Timbal runs agent loops several times faster with a fraction of the memory. See Benchmarks.

Small and hackable. The core framework is under 10k lines. Easy to read, modify, and fork. Other frameworks are bloated with legacy, indirection, and abstraction.

One interface. Agents, Workflows, and Tools share the same calling convention and event stream. Compose them freely.

Human in the loop. Approval gates and suspend() pause any run for a human, persist state, and resume across process restarts. Works on agents, workflow steps, and tools.

Provider-agnostic. Swap models by changing a string (anthropic/claude-sonnet-4-6 → openai/gpt-5.5). Built-in FallbackModel chains providers for automatic failover.

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Features

Memory & compaction	Persistent context with strategies to stay under the context window
Tools & MCP	Built-in tool library, your own functions, any MCP server
Structured output	Typed Pydantic models instead of raw text
Skills	Reusable tool packages the agent loads on demand
Tracing	Full span traces, exportable over OTLP
Evals	Declarative YAML evaluation suite with built-in validators
Deployment	Run locally with timbal start, ship to the platform or self-host

Install extras as needed:

pip install 'timbal[server]'      # HTTP serving
pip install 'timbal[documents]'   # PDF, Excel, Word
pip install 'timbal[evals]'       # evals CLI
pip install 'timbal[all]'         # everything

---

Benchmarks

Pure framework-overhead benchmarks: trivial handlers, faked LLM calls, observability on both sides.

Metric (single tool call)	Timbal	LangGraph + LangSmith	CrewAI
p50 latency	1.1 ms	5.2 ms	3.2 ms
memory / run	2.2 KB	110 KB	10 KB
throughput @ c=10	1716/s	224/s	31/s

Reproduce with benchmarks/README.md. Full suite covers LangGraph, CrewAI, Agno, PydanticAI, OpenAI Agents SDK, and Google ADK.

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Full app

The CLI scaffolds and runs a complete application (UI + API + workforce of Python agents/workflows):

timbal create my-project
cd my-project
timbal start

Deploy by connecting the repo to the Timbal Platform, or self-host the components yourself. See deployment docs.

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Development

git clone https://github.com/timbal-ai/timbal.git
cd timbal
uv sync --dev
uv run pytest

Contributor reference: CLAUDE.md, benchmarks/README.md.

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Documentation

docs.timbal.ai

Contributing

Pull requests and issues welcome.

License

Apache 2.0. See LICENSE.