Instructions to use chromadb/context-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chromadb/context-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="chromadb/context-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chromadb/context-1")
model = AutoModelForCausalLM.from_pretrained("chromadb/context-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chromadb/context-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chromadb/context-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chromadb/context-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chromadb/context-1

SGLang

How to use chromadb/context-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "chromadb/context-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chromadb/context-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "chromadb/context-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chromadb/context-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chromadb/context-1 with Docker Model Runner:
```
docker model run hf.co/chromadb/context-1
```

hammadtime commited on Mar 26

Commit

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+---
+license: apache-2.0
+base_model:
+- openai/gpt-oss-20b
+---
+# Chroma Context-1
+Context-1 is a 20B parameter agentic search model trained
+to retrieve supporting documents for complex, multi-hop
+queries. It is designed to be used as a retrieval subagent
+alongside a frontier reasoning model: given a query,
+Context-1 decomposes it into subqueries, iteratively
+searches a corpus, and selectively edits its own context
+to free capacity for further exploration.
+Context-1 achieves retrieval performance comparable to
+frontier LLMs at a fraction of the cost and up to 10x
+faster inference speed.
+**Technical report:**
+[Chroma Context-1: Training a Self-Editing Search Agent](https://trychroma.com/research/context-1)
+## Model Details
+- **Base model:** gpt-oss-20b
+- **Parameters:** 20B (Mixture of Experts)
+- **Training:** SFT + RL (CISPO) with a staged curriculum
+- **Precision:** BF16 (MXFP4 quantized checkpoint coming soon)
+## Key Capabilities
+- **Query decomposition:** Breaks complex multi-constraint
+  questions into targeted subqueries.
+- **Parallel tool calling:** Averages 2.56 tool calls per
+  turn, reducing total turns and end-to-end latency.
+- **Self-editing context:** Selectively prunes irrelevant
+  documents mid-search to sustain retrieval quality over
+  long horizons within a bounded context window (0.94
+  prune accuracy).
+- **Cross-domain generalization:** Trained on web, legal,
+  and finance tasks; generalizes to held-out domains and
+  public benchmarks (BrowseComp-Plus, SealQA, FRAMES,
+  HLE).
+## Important: Agent Harness Required
+Context-1 is trained to operate within a specific agent
+harness that manages tool execution, token budgets, context
+pruning, and deduplication. **The harness is not yet
+public.** Running the model without it will not reproduce
+the results reported in the technical report.
+We plan to release the full agent harness and evaluation
+code soon. In the meantime, the technical report describes
+the harness design in detail.
+## Citation
+```bibtex
+@techreport{bashir2026context1,
+  title = {Chroma Context-1: Training a Self-Editing Search Agent},
+  author = {Bashir, Hammad and Hong, Kelly and Jiang, Patrick and Shi, Zhiyi},
+  year = {2026},
+  month = {March},
+  institution = {Chroma},
+  url = {https://trychroma.com/research/context-1},
+}
+```
+## License
+Apache 2.0