agentradar 1.0.4

Local-first visual debugger for multi-agent AI systems

agentradar

Local-first visual debugger for multi-agent AI systems.

Drop one import into any Python AI pipeline and get a live dashboard - graph, tokens, time travel, failures - no cloud, no setup.

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Demo

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Dashboard views

Live Graph	Tokens

Time Travel	Failures

Tab	What you see
Live Graph	DAG of agents as they run - nodes light up green/red in real time
State	Every step's input/output with diff vs previous step
Time Travel	Scrub back through the run like a video timeline
Tokens	Bar chart per agent + cumulative line + donut breakdown
Failures	Auto-saved error runs with full traceback, updated live

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Install

Via pip

pip install agentradar

Via git clone

git clone https://github.com/yossefaymanzedan/agentradar.git
cd agentradar
pip install -e .

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Quickstart

import agentradar   # server starts on :7111, browser opens automatically

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The @radar decorator

For custom pipelines (no framework), use @radar for explicit tracing:

from agentradar import radar, reset_run

@radar(tag="researcher")
def researcher(topic: str) -> str:
    ...

@radar(tag="writer", llm_input=["researcher"])
def writer(notes: str) -> str:
    ...

reset_run()
notes = researcher("quantum computing")
article = writer(notes)

llm_input=["tag"] draws an edge from researcher -> writer in the graph.

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Pipeline patterns

Sequential chain

Each agent declares its upstream with llm_input=["previous_tag"]:

@radar(tag="researcher")
def researcher(topic): ...

@radar(tag="writer", llm_input=["researcher"])
def writer(notes): ...

@radar(tag="editor", llm_input=["writer"])
def editor(draft): ...

Graph: researcher -> writer -> editor

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Fan-out (parallel agents)

Define a coordinator with no llm_input. Children declare llm_input=["coordinator_tag"]. The library auto-spawns all children in parallel - no asyncio.gather needed:

@radar(tag="coordinator")
async def coordinator(query: str):
    pass  # library spawns children, returns [result_a, result_b, result_c]

@radar(tag="fetch_a", llm_input=["coordinator"])
async def fetch_a(query: str) -> str: ...

@radar(tag="fetch_b", llm_input=["coordinator"])
async def fetch_b(query: str) -> str: ...

results = await coordinator(query)  # -> [fetch_a result, fetch_b result]

Graph: coordinator -> fetch_a, fetch_b (parallel band)

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Fan-in (merge point)

Declare multiple parents - edges are drawn from all of them:

@radar(tag="aggregator", llm_input=["fetch_a", "fetch_b", "fetch_c"])
async def aggregator(results: list) -> str: ...

Graph: fetch_a, fetch_b, fetch_c -> aggregator

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Full fan-out + fan-in

@radar(tag="coordinator")
async def coordinator(query): pass

@radar(tag="fetch_a", llm_input=["coordinator"])
async def fetch_a(query): ...

@radar(tag="fetch_b", llm_input=["coordinator"])
async def fetch_b(query): ...

@radar(tag="aggregator", llm_input=["fetch_a", "fetch_b"])
async def aggregator(results): ...

async def main():
    reset_run()
    results = await coordinator(query)   # auto-spawns fetch_a + fetch_b
    summary = await aggregator(results)  # merge point, draws edges from both

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@radar decorator parameters

Parameter	Type	Description
tag	str	Identifier for this agent. Used to draw edges and auto-spawn children.
llm_input	list[str]	Upstream tags - draws edges and marks parent agents. Single parent = auto-spawn candidate.
notes	str	Freeform annotation shown in the dashboard inspector panel.

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Token tracking

agentradar automatically captures token usage from LangChain (BaseChatModel.invoke / ainvoke) with no extra code. Prompt tokens, completion tokens, and total are shown per agent in the Tokens tab.

For other libraries, return a dict with a tokens key:

@radar(tag="my_agent")
def my_agent(prompt):
    response = my_llm.call(prompt)
    return {
        "content": response.text,
        "tokens": {"prompt": 120, "completion": 80, "total": 200}
    }

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Multi-run across a session

Call reset_run() before each pipeline execution to start a fresh run:

from agentradar import reset_run

reset_run()
result = await my_pipeline(input)

Without reset_run(), all agent calls accumulate into the same run.

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Configuration

import agentradar
agentradar.config(
    port=7111,           # dashboard port (auto-increments if taken)
    open_browser=True,   # open browser on start
)

Or suppress server start entirely (for CI/tests):

AGENT_TRACE_NO_SERVER=1 python your_script.py

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Examples

File	Pattern
examples/01_single_agent.py	Single agent baseline
examples/02_sequential_chain.py	4-step linear chain (research -> write -> edit -> publish)
examples/03_parallel_agents.py	Fan-out coordinator + fan-in aggregator
examples/04_error_handling.py	Error capture, fallback chains, failure history
examples/05_nested_agents.py	Deep nested agent calls
examples/06_rag_pipeline.py	RAG: 3 parallel retrievers -> reranker -> answer
examples/07_code_review_crew.py	3 specialist reviewers in parallel
examples/08_debate_agents.py	Multi-round debate with judge
examples/09_self_healing_pipeline.py	Retry loop with fallback

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How it works

1. import agentradar starts a FastAPI + WebSocket server on a background thread
2. The @radar decorator emits agent_start / agent_end / agent_error events into a thread-safe queue
3. The server drains the queue and broadcasts events over WebSocket to the browser
4. The React dashboard renders the live DAG, state diffs, token charts, and failure history in real time
5. All runs are stored as .agtrace files in ~/.agentradar/ for offline replay

No cloud. No API keys. No data leaves your machine.

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Contributing

Contributions are welcome. Please open an issue first to discuss what you'd like to change.

git clone https://github.com/yossefaymanzedan/agentradar.git
cd agentradar
pip install -e ".[dev]"

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License

MIT - Yossef Ayman Zedan