CrewAI Ollama Cloud Provider

A custom CrewAI LLM provider that speaks native Ollama protocol — POST /api/chat with NDJSON streaming. No OpenAI shim, no LiteLLM, no proxy needed. Works with local Ollama, self-hosted instances, and ollama.com Cloud API.

Why?

CrewAI's built-in Ollama support routes through the OpenAI-compatible shim (/v1/chat/completions). This provider talks the real Ollama protocol — /api/chat with native JSON, NDJSON streaming, and Ollama's native tool calling and thinking formats.

If you're running Ollama Cloud models (gpt-oss:120b-cloud, kimi-k2.6-cloud, etc.) or just want direct API access without translation layers, this is for you.

Features

Feature	Support
Native /api/chat	✅ real Ollama protocol, not OpenAI-compatible
NDJSON streaming	✅ token-by-token, thinking/reasoning tokens
Tool calling	✅ native Ollama tool calls (v0.3+)
Structured output	✅ JSON schema via format parameter
Thinking models	✅ think parameter for DeepSeek-R1, Kimi, etc.
Cloud auth	✅ Authorization: Bearer for ollama.com
Model discovery	✅ list_ollama_models()
Config overrides	✅ runtime temperature, max_tokens, etc.
Context windows	✅ auto-detection for popular models
Stop words	✅ options.stop
Keep alive	✅ keep_alive parameter
Multimodal	✅ image support for vision models
CrewAI events	✅ full observability integration

Installation

pip install crewai-ollama-cloud

Requires: Python ≥3.10, CrewAI ≥0.80.0, httpx ≥0.25.0

Environment Setup

# Optional: set your Ollama Cloud API key
export OLLAMA_API_KEY="sk-xxxx"

For local Ollama, no API key is needed.

Quick Start

from crewai import Agent, Task, Crew
from crewai_ollama_cloud import OllamaCloudProvider

# Ollama Cloud
llm = OllamaCloudProvider(
    model="deepseek-v4-flash",
    base_url="https://ollama.com",
    api_key="sk-xxxx",  # or set OLLAMA_API_KEY env var
    temperature=0.7,
    stream=True,
)

# Or local Ollama
# llm = OllamaCloudProvider(model="llama3.1:8b", base_url="http://localhost:11434")

agent = Agent(role="Analyst", goal="Analyze data", backstory="Expert", llm=llm)
task = Task(description="Summarize Q1 report", expected_output="Summary")
crew = Crew(agents=[agent], tasks=[task])

result = crew.kickoff()
print(result)

Configuration Reference

Constructor Parameters

Parameter	Type	Default	Description
model	str	(required)	Ollama model name (e.g. "llama3.1:8b", "deepseek-v4-flash")
base_url	str	"http://localhost:11434"	Ollama host URL (no trailing /v1)
api_key	str or None	env OLLAMA_API_KEY	API key for cloud instances
temperature	float or None	None	Sampling temperature (0–2)
max_tokens	int or None	None	Max tokens to generate
top_p	float or None	None	Nucleus sampling
top_k	int or None	None	Top-k sampling
stop	list[str]	[]	Stop sequences
stream	bool	False	Enable NDJSON streaming
timeout	float	120.0	HTTP timeout in seconds
keep_alive	str	"5m"	Model keep-alive duration
think	bool	False	Enable thinking/reasoning tokens
additional_params	dict	{}	Extra parameters merged into request body

Ollama Parameter Mapping

When calling the API, CrewAI parameters are mapped to Ollama's native format:

CrewAI field	Ollama request field
temperature	options.temperature
max_tokens	options.num_predict
top_p	options.top_p
top_k	options.top_k
stop	options.stop
think	think (top-level)
response_model	format (JSON schema)
keep_alive	keep_alive (top-level)

Runtime Overrides

All configuration fields can be changed at runtime between calls:

llm = OllamaCloudProvider(model="llama3.1:8b", temperature=0.3)

# Warm up: creative mode
llm.temperature = 0.9
result = llm.call("Write a poem")

# Switch to precise mode for next call
llm.temperature = 0.1
llm.top_p = 0.95
result = llm.call("Calculate 2+2")

Model Discovery

from crewai_ollama_cloud import list_ollama_models, OllamaModelInfo

# List models on a local GPU rig
models = list_ollama_models("http://localhost:11434")

# List cloud models
models = list_ollama_models("https://ollama.com", api_key="sk-xxxx")

for m in models:
    print(f"{m.name:35s} | {m.parameter_size:6s} | {m.family:10s} | {m.size_gb:5.1f} GB")
# Output:
# llama3.1:8b                         | 8b     | llama      |  4.7 GB
# mistral:7b                          | 7b     | mistral    |  4.1 GB
# deepseek-v4-flash                   | 70b    | deepseek   | 40.5 GB

The OllamaModelInfo object contains:

Attribute	Type	Description
name	str	Full model name
digest	str	SHA256 digest
size	int	Size in bytes
modified_at	str or None	Last modified timestamp
family	str	Inferred model family
parameter_size	str	Parameter count (e.g. "8b", "70b")
size_gb	float	Size in gigabytes

Environment Variables

Variable	Description
OLLAMA_API_KEY	API key for authenticated Ollama instances (e.g. cloud)

Stream Output

When stream=True, the provider uses Ollama's native NDJSON streaming. Tokens are emitted via CrewAI's LLMStreamChunkEvent:

llm = OllamaCloudProvider(model="llama3.1:8b", stream=True)

# Each token triggers a stream chunk event
result = llm.call("Tell me about black holes")
# Events:
#   chunk: "Black"
#   chunk: " holes"
#   chunk: " are"
#   ...

For thinking models (think=True, like deepseek-r1), reasoning tokens are separated from final output and emitted as thinking chunk events.

Tool Calling

Ollama v0.3+ supports native tool calling. The provider converts CrewAI BaseTool objects to Ollama's native tool format:

{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get weather for a city",
    "parameters": {
      "type": "object",
      "properties": {
        "city": {"type": "string", "description": "City name"}
      },
      "required": ["city"]
    }
  }
}

Tool execution results are returned directly.

Structured Output

To get JSON responses, use response_model:

from pydantic import BaseModel

class Summary(BaseModel):
    key_points: list[str]
    sentiment: str

llm = OllamaCloudProvider(model="llama3.1:8b", temperature=0)
result = llm.call("Analyze Q3 results", response_model=Summary)
# result.key_points = ["Revenue up 15%", ...]
# result.sentiment = "positive"

Context Windows

The provider auto-detects context window sizes for known models:

Model	Context Size
llama3:70b	8,192
llama3.1:8b	131,072
llama3.1:70b	131,072
llama3.1:405b	131,072
llama3.2:1b/3b	131,072
llama3.3:70b	131,072
mistral:7b	8,192
mixtral:8x7b	32,768
qwen2.5:7b/32b	32,768
deepseek-r1:7b/8b	131,072
Unknown models	4,096 (default)

Error Handling

Error	Provider Behavior
HTTP 4xx/5xx	HTTPStatusError → LLMCallFailedEvent
Context overflow	LLMContextLengthExceededError (CrewAI native)
Connection failure	Exception → LLMCallFailedEvent

Architecture

┌────────────────┐
│  CrewAI Agent  │
└───────┬────────┘
        │ Agent.llm.call(messages, tools, ...)
        ▼
┌─────────────────────────────┐
│  OllamaCloudProvider        │
│  (extends BaseLLM)          │
│                             │
│  call() / acall()           │
│   ├─ _format_messages()     │
│   ├─ _build_body()          │
│   ├─ BEFORE hooks           │
│   ├─ httpx POST /api/chat   │───────┐
│   ├─ _process_response()    │       │
│   ├─ AFTER hooks            │       │
│   └─ event emission         │       │
└─────────────────────────────┘       │
                                      ▼
                            ┌─────────────────┐
                            │  Ollama Instance │
                            │  (local/remote)  │
                            │                 │
                            │  POST /api/chat  │
                            │  ← JSON / NDJSON │
                            └─────────────────┘

Zero translation layers. httpx → /api/chat → Ollama. That's the whole call path.

Testing

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

39 tests cover: initialization, capabilities, request body building, non-streaming calls, streaming calls with thinking tokens, tool calls, stop words, context overflow handling, auth headers, async call delegation, model discovery.

License

MIT — see LICENSE file.