Provider: Tera

Use Respan Gateway to call Tera models (tera/gpt-oss-20b) while keeping unified observability (logs, cost, latency, reliability) in Respan.

Quick setup

Send your first request

Pick the integration that matches your stack. The base URL is https://api.respan.ai/api and the only key needed is your RESPAN_API_KEY.

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from openai import OpenAI
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client = OpenAI(
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    api_key="YOUR_RESPAN_API_KEY",
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    base_url="https://api.respan.ai/api",
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)
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response = client.chat.completions.create(
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    model="tera/gpt-oss-20b",
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    messages=[{"role": "user", "content": "Hello, Tera!"}],
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)
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print(response.choices[0].message.content)

More integrations

Tera models work with every Respan gateway integration:

• OpenAI SDK
• OpenAI Agents SDK
• LangChain
• LangGraph
• LlamaIndex
• Pydantic AI
• Respan native (OTel)

Switch models

Change the model parameter to call any supported model through the same client. Use the tera/ prefix to disambiguate when routing across providers. Browse the full list on the Models page.

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client.chat.completions.create(model="tera/gpt-oss-20b", messages=messages)
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client.chat.completions.create(model="openai/gpt-5.5", messages=messages)
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client.chat.completions.create(model="anthropic/claude-sonnet-4-5", messages=messages)

Use your own Tera key (BYOK)

Credits are the default path. If you’d rather bill Tera directly, attach your own provider key.

Override credentials per model (Optional)

Use credential_override when one model on a request should use a different Tera key than the default.

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{
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  "customer_credentials": {
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    "tera": { "api_key": "YOUR_TERA_API_KEY" }
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  },
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  "credential_override": {
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    "tera/gpt-oss-20b": { "api_key": "ANOTHER_TERA_API_KEY" }
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  }
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}

Log without proxying (Optional)

Already calling Tera directly? Send logs to Respan asynchronously to track cost, latency, and performance for those external calls.

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import requests
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requests.post(
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    "https://api.respan.ai/api/request-logs/create/",
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    headers={
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        "Authorization": "Bearer YOUR_RESPAN_API_KEY",
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        "Content-Type": "application/json",
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    },
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    json={
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        "model": "tera/gpt-oss-20b",
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        "prompt_messages": [{"role": "user", "content": "Hello, how are you?"}],
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        "completion_message": {"role": "assistant", "content": "Hello from Tera through Respan."},
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        "cost": 0.001,
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        "generation_time": 1.2,
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        "customer_params": {"customer_identifier": "user_123"},
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    },
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)

See the logging guide for the full setup.