Dunetrace SDK

Runtime observability for AI agents. Detects tool loops, context bloat, prompt injection, and 20 other failure patterns in real-time — with a Slack alert while the run is still live.

Zero external dependencies.

Install

pip install dunetrace                    # core SDK
pip install 'dunetrace[langchain]'       # + LangChain / LangGraph
pip install 'dunetrace[otel]'            # + OpenTelemetry exporter

Quickstart

LangChain / LangGraph

from dunetrace import Dunetrace
from dunetrace.integrations.langchain import DunetraceCallbackHandler

dt = Dunetrace()
callback = DunetraceCallbackHandler(dt, agent_id="my-agent")

result = agent.invoke(input, config={"callbacks": [callback]})
dt.shutdown()

Pure Python / custom agent — decorator style

from dunetrace import Dunetrace

dt = Dunetrace()

@dt.tool                                  # auto-emits tool.called / tool.responded
def web_search(query: str) -> list: ...   # args are transmitted as-is

@dt.trace                                 # agent_id defaults to "my_agent"
def my_agent(question: str) -> str:
    return web_search(question)[0]        # zero SDK calls needed inside function bodies

@dt.trace supports bare usage (@dt.trace with no parens), explicit agent ID (@dt.trace("research-agent")), and keyword args (@dt.trace(model="gpt-4o")). @dt.tool works on both sync and async functions and is a no-op when called outside a run context.

Or with @dt.agent + auto-instrumentation:

dt.init(agent_id="my-agent")   # patches openai, anthropic, mistral, httpx, requests, langchain, crewai

@dt.agent(model="gpt-4o")      # agent_id inherited from init()
def run_agent(query: str) -> str:
    return openai_client.chat.completions.create(...).choices[0].message.content

LangChain/LangGraph and CrewAI agents need zero manual callback wiring — see docs/integrations/auto-instrumentation.md for how agent attribution is resolved.

FastAPI / Flask — one line each, see docs/integrate-custom-python-agent.md.

What it detects

28 detectors run on every completed run — no configuration, no LLM. A few of the main ones:

→ docs/detectors.md for the full list of 28 detectors

Output modes

Backend

git clone https://github.com/dunetrace/dunetrace
cd dunetrace && cp .env.example .env && docker compose up -d

Dashboard → http://localhost:3000 · Ingest → http://localhost:8001

Deploy markers

Annotate the detector timeline with release boundaries so you can correlate failure spikes with deploys:

# Call from your deploy script, CI/CD pipeline, or app startup
dt.mark_deploy("my-agent", version="v1.4.2", commit="abc1234", env="production")

The dashboard renders blue dashed vertical lines at each deploy timestamp on the 30-day detector rate chart. Fire-and-forget — runs on a background thread, never blocks the caller.

Additional keyword arguments are stored as meta and shown on hover.

Policies

Runtime guardrails that fire mid-run — before a failure propagates. Define conditions with any supported trigger and attach a stop, switch_model, inject_prompt, or log action.

from dunetrace import Dunetrace

dt = Dunetrace()

# Stop the run if tool call count exceeds 5
dt.add_policy(
    name="cap tool calls",
    condition={"trigger": "tool_call_count", "operator": "gt", "value": 5},
    action={"type": "stop"},
)

# Downgrade model when cost exceeds $0.50
dt.add_policy(
    name="cost cap",
    condition={"trigger": "cost_usd", "operator": "gt", "value": 0.50},
    action={"type": "switch_model", "params": {"model": "gpt-4o-mini"}},
)

# Inject a corrective prompt when a loop is detected
dt.add_policy(
    name="loop fix",
    condition={"trigger": "signal", "operator": "eq", "value": "TOOL_LOOP"},
    action={"type": "inject_prompt", "params": {"prompt": "Stop repeating tool calls. Summarise what you know and answer."}},
)

with dt.run("my-agent", user_input=query, tools=["search"]) as run:
    ...
    # After a stop policy fires, PolicyViolation is raised
    # After switch_model fires, check run.model_override
    # After inject_prompt fires, check run.pop_prompt_addition()

Policies can also be defined in the dashboard and fetched automatically at run start (60-second TTL cache per agent). See docs/policies.md for the full reference.

MCP server

Query agent signals directly from Claude Code, Cursor, or any MCP-compatible editor — no context switch to the dashboard required.

pip install dunetrace-mcp

Ten tools: list_agents, get_agent_signals, get_agent_health, get_run_detail, get_agent_runs, search_signals, get_signal_detail, get_agent_patterns, summarize_agent, get_instrumentation_guide.

Claude Code — add to ~/.claude.json:

{
  "mcpServers": {
    "dunetrace": {
      "command": "dunetrace-mcp",
      "env": {
        "DUNETRACE_API_URL": "http://localhost:8002",
        "DUNETRACE_API_KEY": "dt_dev_test"
      }
    }
  }
}

Cursor — add to .cursor/mcp.json in your project root (same shape as above).

Once connected, ask your editor things like:

• "Is my agent healthy?"
• "What failed in the last 24 hours?"
• "Show me signal #42 with its fix."
• "Is this failure systemic or a one-off?"

→ docs/mcp-server.md

Tests

python -m unittest discover -s tests -v          # SDK tests (no network required)
cd ../mcp-server && python -m pytest tests/ -v   # MCP server tests (no network required)

SDK: 620 tests · MCP server: 154 tests — both run fully offline.

Links

• LangChain / LangGraph
• Hermes Agent (Nous Research)
• CrewAI
• Pydantic AI
• AutoGen
• OpenAI Agents SDK
• Haystack 2.x
• LlamaIndex
• LiteLLM
• All integration guides
• GitHub
• Issues