Providers

String dispatch by prefix

Anything that doesn’t match one of these prefixes raises ConfigError rather than guessing — so mixtral-* or a bare provider name won’t resolve. For those, prefix with litellm/ (e.g. litellm/mistral/mixtral-8x7b) to route explicitly through LiteLLM.

from loomflow import Agent
 
Agent("...", model="claude-opus-4-7")
Agent("...", model="claude-haiku-4-5")
Agent("...", model="gpt-4o")
Agent("...", model="o3-mini")
Agent("...", model="deepseek-chat")
Agent("...", model="mistral-large")
Agent("...", model="command-r-plus")
Agent("...", model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0")
Agent("...", model="vertex_ai/gemini-pro")
Agent("...", model="ollama/llama3")
Agent("...", model="groq/llama-3.1-70b")
Agent("...", model="litellm/claude-3-haiku")     # explicit opt-in

Anthropic

from loomflow import Agent
 
agent = Agent("...", model="claude-opus-4-7")
# ANTHROPIC_API_KEY must be set.

from loomflow import Agent
from loomflow.model.anthropic import AnthropicModel
 
agent = Agent("...", model=AnthropicModel(
    "claude-opus-4-7",
    api_key="sk-ant-...",       # or via Secrets
    max_tokens=8192,
    temperature=0.2,
))

Install: pip install 'loomflow[anthropic]'.

The adapter wraps the official anthropic SDK’s AsyncAnthropic.messages.create(stream=True, ...). Tool calls serialize to Anthropic’s content-block format under the hood.

OpenAI

from loomflow import Agent
 
agent = Agent("...", model="gpt-4o")
# OPENAI_API_KEY must be set.

from loomflow import Agent
from loomflow.model.openai import OpenAIModel
 
agent = Agent("...", model=OpenAIModel(
    "gpt-4o",
    api_key="sk-...",
    temperature=0.2,
))

from openai import AsyncOpenAI
from loomflow import Agent
from loomflow.model.openai import OpenAIModel
 
client = AsyncOpenAI(
    timeout=30.0,
    max_retries=0,                     # let the framework retry
    base_url="https://api.together.ai/v1",  # OpenAI-compatible endpoint
)
agent = Agent("...", model=OpenAIModel("gpt-4o", client=client))

Install: pip install 'loomflow[openai]'.

OpenAI-compatible endpoints (Together, Fireworks, Anyscale, vLLM) work via base_url= on the underlying client.

DeepSeek

DeepSeek speaks the OpenAI wire format, so deepseek-* specs resolve to OpenAIModel pointed at https://api.deepseek.com — keyed by DEEPSEEK_API_KEY (Secrets backend or env var). A missing key raises ConfigError loudly rather than falling back to OPENAI_API_KEY.

Agent("...", model="deepseek-chat")       # DEEPSEEK_API_KEY must be set

Using DeepSeek through a reseller / gateway? Construct the adapter directly with the gateway’s endpoint, and pass its actual rates so cost_usd and budget caps stay honest — the third element is the cached-input rate:

from loomflow.model.openai import OpenAIModel
 
model = OpenAIModel(
    "deepseek-v4-flash",
    api_key=GATEWAY_KEY,
    base_url="https://gateway.example.com/v1",
    cost_per_mtoken=(0.75, 3.00, 0.008),  # (input, output, cached input) USD/MTok
)

On failover gateways, also set prompt_caching={"enabled": True, "cache_key": ...} — see Prompt caching for why the key is load-bearing there.

LiteLLM (~100 providers behind one adapter)

from loomflow import Agent
 
agent = Agent("...", model="mistral-large")
agent = Agent("...", model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0")
agent = Agent("...", model="ollama/llama3")

Install: pip install 'loomflow[litellm]'.

Provider auth is whatever litellm expects. Typically env vars matching the provider’s official client (AWS_PROFILE, GOOGLE_APPLICATION_CREDENTIALS, MISTRAL_API_KEY, COHERE_API_KEY, etc.). See LiteLLM’s docs for the full matrix.

Echo

agent = Agent("...", model="echo")
result = await agent.run("hi")
print(result.output)            # "Echo: hi"

EchoModel echoes the prompt as the assistant’s reply. No tool calls, no streaming meaningful tokens. For smoke-testing the agent loop without burning real tokens.

Scripted

from loomflow import Agent, ScriptedModel, ScriptedTurn
 
model = ScriptedModel(turns=[
    ScriptedTurn(text="The answer is 42."),
    ScriptedTurn(tool_calls=[...]),
    ScriptedTurn(text="Done."),
])
agent = Agent("...", model=model)

For tests with deterministic multi-turn scenarios. Each ScriptedTurn becomes one model response; the framework consumes them in order.

Secrets handling

API keys resolve in this order inside every adapter:

1. Explicit api_key= argument on the adapter.
2. secrets.lookup_sync(<ENV_VAR_NAME>) if a Secrets backend is wired on Agent(tuning=Tuning(secrets=...)).
3. os.environ[<ENV_VAR_NAME>] as the bare fallback.

For Vault / AWS Secrets Manager / 1Password, write a custom Secrets adapter. See Secrets resolution.