Examples

Runnable agents in examples/, in Go and Python. Each one sends a trace to a local Conduit and prints a link to it in the UI. CI runs every example on every push, so they stay working.

Run one in two commands

make quickstart                        # once: Conduit on localhost, needs only Docker
cd examples/go && go run ./tool-agent  # or: python examples/python/tool_agent.py

The examples find the certificates make quickstart generated and send as agent my-agent. To point them somewhere else, set CONDUIT_GATEWAY_ADDR, CONDUIT_CERT_DIR (a directory with ca.crt and <agent>.crt/.key), and CONDUIT_AGENT.

For Python, install the SDK first: pip install conduit-observability, or pip install "conduit-observability[anthropic,openai]" to include the Claude and OpenAI SDKs. To try SDK changes from a checkout, use pip install -e sdk/python.

The examples

Example	Go (`examples/go`)	Python (`examples/python`)	Needs
Hello trace. The smallest useful trace: a run with one LLM call. Start here.	`go run ./hello-trace`	`hello_trace.py`	nothing
Tool-calling agent. A model/tool loop with a simulated model.	`go run ./tool-agent`	`tool_agent.py`	nothing
Async agent. Concurrent tool calls with `asyncio.gather`; spans still nest correctly.		`async_agent.py`	nothing
Claude agent. The same loop with the official Anthropic SDK and real token usage.	`go run ./claude-agent`	`claude_agent.py`	`ANTHROPIC_API_KEY`
OpenAI agent. The same loop with the official OpenAI SDK (Chat Completions).	`go run ./openai-agent`	`openai_agent.py`	`OPENAI_API_KEY`

The Claude examples default to claude-sonnet-5 (override with ANTHROPIC_MODEL). The OpenAI examples default to gpt-5-mini (override with OPENAI_MODEL). Without a key, they say so and exit.

What a tool-calling trace looks like

Every tool-calling example produces the same span tree. It’s a good shape to copy into your own agent:

agent.run                 one per task; attributes: question, example
├── llm.call              one per model call: model, prompt, completion, tokens, cost
├── tool.get_weather      one per tool call: tool.input; error status if the tool failed
├── tool.get_weather
└── llm.call              the final answer

Tools run outside the llm.call span. Each llm.call span ends before its tools run, so its duration is the model’s latency alone.
Tool spans are children of the run, not of the model call. On the waterfall, concurrent tool calls then show up side by side (see the async example).
Record the provider’s real usage. Take the token counts and model name from the API response (usage.input_tokens for Anthropic, usage.prompt_tokens for OpenAI). EstimateCostUSD / estimate_cost_usd turns them into an approximate cost for models it knows.

How CI checks them without API keys

scripts/examples-check.sh runs every example against a running quickstart and confirms each trace appears in the UI with the expected spans. The Claude and OpenAI examples run against scripts/fake-llm-server.py, a stdlib fake of the Messages and Chat Completions APIs. Both official SDKs honor ANTHROPIC_BASE_URL and OPENAI_BASE_URL, so the examples’ real request building and response parsing are exercised. The fake rejects a second turn that lacks correct tool results.