timestep 2026.0.6

Multi-model provider implementations for OpenAI Agents, supporting both OpenAI and Ollama models

Timestep (Python)

A clean, RISC-style abstraction layer over the OpenAI Agents SDK, providing durable execution and cross-language state persistence for AI agent workflows.

Architecture Philosophy

Timestep provides a RISC (Reduced Instruction Set Computer) approach to the OpenAI Agents SDK's CISC (Complex Instruction Set Computer) architecture. This means:

• Simpler API: Clean abstractions focused on essential operations
• Durable Execution: Built-in state persistence and resumable workflows
• Cross-Language Parity: Identical behavior with the TypeScript implementation
• Composable: Fundamental building blocks that can be combined as needed

Installation

pip install timestep

Or using uv:

uv add timestep

Quick Start

Durable Execution with State Persistence

The core feature of Timestep is durable execution with cross-language state persistence:

from timestep import run_agent, RunStateStore, consume_result
from agents import Agent, Session

# Create agent
agent = Agent(model="gpt-4")

# Create session and state store
session = Session()
state_store = RunStateStore("agent_state.json", agent)

# Run agent
result = await run_agent(agent, input_items, session, stream=False)
result = await consume_result(result)

# Handle interruptions
if result.interruptions:
    # Save state for later resume (even in TypeScript!)
    state = result.to_state()
    await state_store.save(state)
    
    # Load state and approve interruptions
    loaded_state = await state_store.load()
    for interruption in loaded_state.get_interruptions():
        loaded_state.approve(interruption)
    
    # Resume execution
    result = await run_agent(agent, loaded_state, session, stream=False)
    result = await consume_result(result)

Cross-Language State Transfer

Start execution in Python, interrupt for tool approval, and resume in TypeScript:

# Python: Start agent and save state at interruption
from timestep import run_agent, RunStateStore
from agents import Agent, Session

agent = Agent(model="gpt-4")
session = Session()
state_store = RunStateStore("cross_lang_state.json", agent)

# Run until interruption
result = await run_agent(agent, input_items, session, stream=False)
result = await consume_result(result)

if result.interruptions:
    # Save state - can be loaded in TypeScript!
    state = result.to_state()
    await state_store.save(state)
    print(f"State saved. Resume in TypeScript with session_id: {session._get_session_id()}")

Then in TypeScript:

// TypeScript: Load Python state and resume
import { runAgent, RunStateStore } from '@timestep-ai/timestep';
import { Agent, Session } from '@openai/agents';

const agent = new Agent({ model: 'gpt-4' });
const session = new Session();
const stateStore = new RunStateStore('cross_lang_state.json', agent);

// Load state saved from Python
const savedState = await stateStore.load();

// Approve interruptions
for (const interruption of savedState.getInterruptions()) {
  savedState.approve(interruption);
}

// Resume execution
const result = await runAgent(agent, savedState, session, false);

Multi-Model Provider Support

Timestep also provides multi-model provider support for OpenAI and Ollama:

from timestep import MultiModelProvider, MultiModelProviderMap, OllamaModelProvider
from agents import Agent, Runner, RunConfig
import os

# Create a provider map and add Ollama support
model_provider_map = MultiModelProviderMap()

if os.environ.get("OLLAMA_API_KEY"):
    model_provider_map.add_provider(
        "ollama",
        OllamaModelProvider(api_key=os.environ.get("OLLAMA_API_KEY"))
    )

# Create MultiModelProvider with OpenAI fallback
model_provider = MultiModelProvider(
    provider_map=model_provider_map,
    openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
)

# Create agent with model name
agent = Agent(model="gpt-4")  # Uses OpenAI by default
# Or: agent = Agent(model="ollama/llama3")  # Uses Ollama

# Run agent with RunConfig
run_config = RunConfig(model_provider=model_provider)
result = Runner.run_streamed(agent, agent_input, run_config=run_config)

Core Components

run_agent()

Simplified agent execution with built-in state management:

from timestep import run_agent
from agents import Agent, Session

agent = Agent(model="gpt-4")
session = Session()

# Run with streaming
result = await run_agent(agent, input_items, session, stream=True)

# Run without streaming
result = await run_agent(agent, input_items, session, stream=False)

RunStateStore

Persistent storage for agent state. Supports file-based storage (with database-backed storage coming via DBOS/PGLite):

from timestep import RunStateStore
from agents import Agent

agent = Agent(model="gpt-4")
state_store = RunStateStore("state.json", agent)

# Save state
await state_store.save(state)

# Load state
loaded_state = await state_store.load()

# Clear state
await state_store.clear()

consume_result()

Utility for handling both streaming and non-streaming results:

from timestep import run_agent, consume_result

result = await run_agent(agent, input_items, session, stream=True)
result = await consume_result(result)  # Ensures all stream events are consumed

# Now safe to access result properties
print(result.final_output)

InterruptionException

Exception raised when agent execution is interrupted for approval:

from timestep import InterruptionException

try:
    result = await run_agent(agent, input_items, session, stream=False)
except InterruptionException as e:
    # Handle interruption
    pass

Model Providers

MultiModelProvider

Automatically routes model requests to the appropriate provider based on model name prefixes:

from timestep import MultiModelProvider, MultiModelProviderMap, OllamaModelProvider

# Create a custom mapping
provider_map = MultiModelProviderMap()

# Add Ollama provider
if os.environ.get("OLLAMA_API_KEY"):
    provider_map.add_provider(
        "ollama",
        OllamaModelProvider(api_key=os.environ.get("OLLAMA_API_KEY"))
    )

# Use in MultiModelProvider
model_provider = MultiModelProvider(
    provider_map=provider_map,
    openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
)

Features:

• Automatic provider selection based on model name prefix
• Default fallback to OpenAI for unprefixed models
• Support for custom provider mappings

OllamaModelProvider

Provides access to Ollama models (local or cloud):

from timestep import OllamaModelProvider

# Create an Ollama provider for local Ollama instance
ollama_provider = OllamaModelProvider()  # Defaults to localhost:11434

# For Ollama Cloud, use the API key
cloud_provider = OllamaModelProvider(api_key="your-ollama-cloud-key")

Options:

• api_key (str, optional): API key for Ollama Cloud
• base_url (str, optional): Base URL for Ollama instance (defaults to http://localhost:11434 for local, https://ollama.com for cloud)
• ollama_client (Any, optional): Custom Ollama client instance

Features:

• Lazy client initialization (only loads when needed)
• Automatic cloud detection for models ending with -cloud
• Support for both local Ollama instances and Ollama Cloud
• Seamless switching between local and cloud models

OllamaModel

Direct model implementation that converts Ollama responses to OpenAI-compatible format:

Features:

• Converts Ollama API responses to OpenAI format
• Supports streaming responses
• Handles tool calls and function calling
• Compatible with OpenAI Agents SDK

MultiModelProviderMap

Manages custom mappings of model name prefixes to providers:

Methods:

• add_provider(prefix, provider): Add a prefix-to-provider mapping
• remove_provider(prefix): Remove a mapping
• get_provider(prefix): Get provider for a prefix
• has_prefix(prefix): Check if prefix exists
• get_mapping(): Get all mappings
• set_mapping(mapping): Replace all mappings

Durable Execution with DBOS/PGLite

Timestep uses DBOS (Database Operating System) and PGLite (PostgreSQL in WebAssembly) to provide durable execution:

• State Persistence: Agent run states are stored in PostgreSQL
• Cross-Language Compatibility: State format is identical between Python and TypeScript
• Resumable Workflows: Load any saved state and continue execution
• Database Schema: See database/README.md for the complete schema

The database schema supports:

• Agent definitions and configurations
• Run state snapshots for resumability
• Session history and conversation items
• Tool calls and interruptions
• Usage metrics and cost tracking

Model Naming Conventions

Timestep uses model name prefixes to determine which provider to use:

• No prefix (e.g., gpt-4): Defaults to OpenAI
• openai/ prefix (e.g., openai/gpt-4): Explicitly uses OpenAI
• ollama/ prefix (e.g., ollama/llama3): Uses Ollama

Features

• ✅ Durable Execution: Interrupt and resume agent workflows
• ✅ Cross-Language State Persistence: Seamless state transfer with TypeScript
• ✅ Multi-Model Support: Seamlessly switch between OpenAI and Ollama models
• ✅ Automatic Routing: Model names with prefixes automatically route to the correct provider
• ✅ Customizable: Add your own providers using MultiModelProviderMap
• ✅ OpenAI Compatible: Works with the OpenAI Agents SDK
• ✅ Ollama Integration: Full support for both local Ollama instances and Ollama Cloud
• ✅ RISC-Style API: Clean, simple abstractions over the OpenAI Agents SDK

Requirements

• Python >=3.11
• ollama >=0.6.0
• openai-agents >=0.4.2

Examples

See the tests directory for complete examples including:

• Basic agent execution
• Durable execution with state persistence
• Cross-language state transfer (Python → TypeScript)
• Same-language state persistence (Python → Python)

Future Plans

We're actively developing additional features:

• Enhanced DBOS Integration: Full integration with DBOS for distributed durable execution
• Additional Abstractions: More RISC-style components extracted from complex SDK patterns
• CLI Tool: Command-line interface with tracing support for debugging and monitoring

License

MIT