peekai 0.1.2

Lightweight, local-first observability and debugging for Python AI agents.

Lightweight, local-first observability and debugging for Python AI agents.

No cloud. No API keys. No dashboards to sign up for.
Drop it in, call peekai.init(), and see exactly what your agent is doing —
every LLM call, every tool use, every token spent.

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Why PeekAI?

Building AI agents is hard. Debugging them is harder. Tools like LangSmith or Weights & Biases require you to send your data to their cloud, create accounts, and wire up pipelines before you can see a single trace.

PeekAI is different:

🏠 Local-first	All traces stored in SQLite at ~/.peekai/peekai.db — nothing leaves your machine
⚡ Zero config	One line to instrument OpenAI, Anthropic, and LiteLLM
🧠 Multi-agent aware	Visualize agent-to-agent handoffs as a nested span tree
🔁 Trace replay	Re-run any past trace with a different model or modified tool response
🖥️ CLI + UI	Inspect traces in your terminal or a local Streamlit dashboard

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Install

pip install peekai

# With OpenAI support
pip install "peekai[openai]"

# With Anthropic support
pip install "peekai[anthropic]"

# With the web dashboard
pip install "peekai[ui]"

# With everything
pip install "peekai[all]"

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Quickstart

import peekai
from openai import OpenAI

# One line to instrument everything
peekai.init()

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is 2 + 2?"}],
)

print(response.choices[0].message.content)

Then inspect your traces:

peekai list                  # recent traces
peekai view <trace-id>       # full span waterfall
peekai stats                 # token + cost totals
peekai ui                    # launch the web dashboard

How it works — peekai.init() monkey-patches the SDK clients at startup. No changes to your existing API calls are needed.

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Multi-Agent Support

Decorate your agents and tools — PeekAI automatically builds the parent/child span tree:

import peekai
from openai import OpenAI

peekai.init()
client = OpenAI()


@peekai.agent("researcher")
def researcher_agent(topic: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Research: {topic}"}],
    )
    return response.choices[0].message.content


@peekai.agent("writer")
def writer_agent(research: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Summarise: {research}"}],
    )
    return response.choices[0].message.content


@peekai.tool("format_output")
def format_output(text: str) -> str:
    return f"📝 {text}"


@peekai.trace("multi_agent_pipeline")
def run():
    research = researcher_agent("the James Webb Space Telescope")
    summary = writer_agent(research)
    return format_output(summary)


run()

Visualize the agent flow in the terminal:

peekai map <trace-id>

  trace: multi_agent_pipeline  ✓ ok  3.6s  236 tokens  $0.000222

  └── 🧠 researcher  [agent]  ✓ ok  2.3s
      └── 🤖 openai/gpt-4o  [llm]  ✓ ok  2.3s  102 tok  $0.000115
  └── 🧠 writer  [agent]  ✓ ok  1.3s
      └── 🤖 openai/gpt-4o  [llm]  ✓ ok  1.3s  134 tok  $0.000107
  └── 🔧 format_output  [tool]  ✓ ok  0ms

---

Trace Replay

Re-run any past trace — swap the model, inject a different tool response, see what would have changed:

# Replay with the same model
peekai replay <trace-id>

# Swap to a different model
peekai replay <trace-id> --model gpt-4o

# Swap to Anthropic
peekai replay <trace-id> --model claude-3-5-sonnet-20241022

# Inject a modified tool response
peekai replay <trace-id> --tool search="different search result"

The replay is saved as a new trace and shown side by side in the UI with token/cost deltas.

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CLI Reference

Command	Description
peekai list	Show last 10 traces
peekai view <id>	Full span waterfall with I/O
peekai stats	Total runs, tokens, cost by model
peekai map <id>	ASCII agent flow tree
peekai replay <id>	Re-run a trace (supports --model, --tool)
peekai ui	Launch Streamlit dashboard
peekai clear	Wipe local storage

All commands accept short trace IDs — the first 8 characters are enough.

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Web Dashboard

peekai ui

Opens at http://localhost:8501 with four pages:

• Dashboard — KPIs, cost over time, per-model breakdown
• Traces — filterable list with status, tokens, cost
• Trace View — span waterfall with duration bars, input/output tabs, error highlighting
• Replay — run a replay with model swap, side-by-side comparison

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Decorators

Decorator	What it does
@peekai.trace("name")	Wraps a function as a top-level trace
@peekai.agent("name")	Wraps a sub-agent — its LLM calls become children in the tree
@peekai.tool("name")	Wraps a tool call as a TOOL span

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peekai.init() options

peekai.init(
    db_path="./my_traces.db",  # default: ~/.peekai/peekai.db
    openai=True,               # patch OpenAI SDK (default True)
    anthropic=True,            # patch Anthropic SDK (default True)
    litellm=True,              # patch LiteLLM (default True)
)

Traces are stored locally at ~/.peekai/peekai.db by default. You can open it directly with any SQLite viewer, back it up, or wipe it with peekai clear.

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Supported SDKs

SDK	Status	Notes
OpenAI	✅ Auto-patched	sync + async, streaming
Anthropic	✅ Auto-patched	sync + async, create(stream=True) + stream() context manager
LiteLLM	✅ Auto-patched	sync + async

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Development

# Clone and install
git clone https://github.com/oussamaKH63/peekai
cd peekai
uv sync --extra all  # includes openai, anthropic, litellm, ui

# Run tests
uv run pytest tests/ -v

# Run the demos
uv run python examples/demo_agent.py
uv run python examples/demo_multi_agent.py

# Launch the UI
uv run peekai ui

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Contributing

# Install dev dependencies
uv sync --extra dev

# Run linter
uv run ruff check src/

# Run type checker
uv run mypy src/

# Run tests
uv run pytest tests/ -v

PRs and issues are welcome. See CONTRIBUTING.md for more detail.

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License

MIT © Oussema Khorchani