Cohere (tracing) | Respan Docs

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Use Respan Gateway

See Cohere gateway setup to route this integration through the Respan gateway.

Use AI

Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.

1 {
2   "mcpServers": {
3     "respan-docs": {
4       "url": "https://mcp.respan.ai/mcp/docs"
5     }
6   }
7 }

Example projects

What is Cohere?

The Cohere SDKs provide chat, streaming chat, embedding, and rerank APIs. respan-instrumentation-cohere and @respan/instrumentation-cohere normalize Cohere spans to the Respan span contract before export.

Setup

Install packages

$ pip install respan-ai respan-instrumentation-cohere cohere python-dotenv

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ export CO_API_KEY="YOUR_COHERE_API_KEY"
$ export COHERE_API_KEY="YOUR_COHERE_API_KEY"

Optional:

$ export RESPAN_BASE_URL="https://api.respan.ai/api"
$ export COHERE_CHAT_MODEL="command-a-03-2025"

CO_API_KEY is used by the Python SDK. COHERE_API_KEY is used by the TypeScript SDK. RESPAN_API_KEY is used by Respan for trace export.

Initialize and run

1 import os
2 
3 import cohere
4 from dotenv import load_dotenv
5 from respan import Respan, workflow
6 from respan_instrumentation_cohere import CohereInstrumentor
7 
8 load_dotenv()
9 
10 respan = Respan(
11     api_key=os.environ["RESPAN_API_KEY"],
12     base_url=os.getenv("RESPAN_BASE_URL", "https://api.respan.ai/api"),
13     instrumentations=[CohereInstrumentor()],
14 )
15 client = cohere.ClientV2(api_key=os.environ["CO_API_KEY"])
16 
17 
18 @workflow(name="cohere_chat.workflow")
19 def run_chat() -> str:
20     response = client.chat(
21         model=os.getenv("COHERE_CHAT_MODEL", "command-a-03-2025"),
22         messages=[
23             {
24                 "role": "user",
25                 "content": "In one sentence, explain what Cohere rerank is useful for.",
26             }
27         ],
28         temperature=0.1,
29         max_tokens=80,
30     )
31     return response.message.content[0].text
32 
33 
34 with respan.propagate_attributes(
35     trace_group_identifier="cohere_chat.workflow",
36     metadata={"workflow_name": "cohere_chat.workflow"},
37 ):
38     print(run_chat())

View your trace

Open the Traces page and search for the workflow name cohere_chat.workflow.

Configuration

Parameter	Type	Default	Description
`exception_logger`	`logging.Logger \| None`	`None`	Optional logger passed to the underlying Cohere OpenTelemetry instrumentor.
`sdkModule`	`object \| undefined`	`undefined`	TypeScript only. Pass the imported `cohere-ai` module so the instrumentor patches the same module instance used by your app.
`use_legacy_attributes`	`bool`	`True`	Python only. Emits span attributes compatible with the existing Respan span contract.
`**instrumentor_kwargs`	`dict`	`{}`	Python only. Extra keyword arguments forwarded to the upstream instrumentor.

Attributes

Attach customer identifiers, thread IDs, and metadata to Cohere calls with propagate_attributes or propagateAttributes.

1 with respan.propagate_attributes(
2     customer_identifier="user_123",
3     thread_identifier="conversation_456",
4     trace_group_identifier="cohere_support_chat.workflow",
5     metadata={"plan": "pro", "workflow_name": "cohere_support_chat.workflow"},
6 ):
7     response = client.chat(
8         model="command-a-03-2025",
9         messages=[{"role": "user", "content": "Summarize our support policy."}],
10     )

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`trace_group_identifier`	`str`	Groups spans by workflow name.
`metadata`	`dict`	Custom key-value pairs merged with default metadata.

Examples

Streaming chat

1 stream = client.chat_stream(
2     model="command-a-03-2025",
3     messages=[{"role": "user", "content": "Write a short haiku about observability."}],
4 )
5 
6 for event in stream:
7     if event.type == "content-delta":
8         print(event.delta.message.content.text, end="", flush=True)

Embeddings

1 response = client.embed(
2     model="embed-v4.0",
3     input_type="search_document",
4     texts=[
5         "Respan records traces and spans for AI systems.",
6         "Cohere embeddings convert text into vectors.",
7     ],
8     embedding_types=["float"],
9 )
10 print(len(response.embeddings.float_))

Rerank

1 response = client.rerank(
2     model="rerank-v3.5",
3     query="Which document explains search result ranking?",
4     documents=[
5         "Rerank models reorder candidate documents by query relevance.",
6         "Chat models generate conversational responses.",
7     ],
8     top_n=1,
9 )
10 print(response.results[0].index)