blueclaw 1.2.4

Terminal AI agent with built-in execution tracing and observability

BlueClaw treats AI agents like debuggable programs, not black boxes.
Built on Strands Agents SDK

Quickstart • Tracing • Features • Models • Configuration • Architecture

---

What is BlueClaw?

BlueClaw is a terminal-based AI agent with built-in execution tracing, enabling developers to inspect, replay, and debug agent behavior step by step.

Most AI agents are black boxes — when something goes wrong, you don't know if it was the model reasoning, the tool input, the tool output, or a bad retry. BlueClaw records every tool call with timing, inputs, and outputs, then gives you CLI tools to understand what happened.

blueclaw> research the MCP ecosystem, focus on Python SDKs
● web_search({"query": "MCP Model Context Protocol Python SDK"})
  ✓ 1.2s
● http_request({"url": "https://modelcontextprotocol.io/..."})
  ✓ 0.8s
Done · 2 steps · 1840 tokens · $0.0073 · 4.1s

Quickstart

# Install
pip install -e .

# Initialize workspace
blueclaw init

# Set your API key
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env

# Start an interactive session
blueclaw

# Or run a single prompt
blueclaw run "summarize the latest Python 3.13 release notes"

Tracing & Observability

Every agent run is recorded as a structured JSON trace with per-step timing, tool inputs, outputs, and errors. Eight CLI commands let you inspect runs after the fact — no dashboards, no external services, no setup.

See what happened: trace graph

Quick view of the tool call sequence for any run.

$ blueclaw trace graph 20260315-054426

search for Python 3.13 new features
├── web_search (1ms) ✓  query: Python 3.13 new features
├── web_search (1ms) ✓  query: Python 3.13 new features list 2024
└── http_request (366ms) ✓  url: https://docs.python.org/3.13/whatsnew/3.13.html

Find the bottleneck: trace timeline

See where time actually goes — tool execution vs. model reasoning overhead.

$ blueclaw trace timeline 20260315-054426

Goal: search for Python 3.13 new features
Model: claude-sonnet-4-6 · 3 steps · 1840 tokens · $0.0073

 #    Tool             Start     Duration  Cumulative  Bar
 1    web_search         +0ms       1ms         1ms    █
 2    web_search       +120ms       1ms         2ms    █
 3    http_request     +250ms     366ms       368ms    ████████████████████████████████████████

Tool time: 368ms · Wall time: 4100ms · Overhead: 3732ms (91%)

Understand why: trace explain

Feed a recorded trace to an LLM for post-hoc explanation. Useful when the agent took an unexpected path.

$ blueclaw trace explain 20260315-054426

The agent searched for Python 3.13 features, found the results too generic,
refined its query to include "list 2024", then fetched the official changelog
from docs.python.org. The two-step search pattern suggests the first results
didn't contain enough detail...

Post-hoc explanation · not the agent's actual reasoning

Compare two runs: trace diff

Did your prompt change make things better or worse?

$ blueclaw trace diff 20260315-054426 20260315-071830

Run A: 20260315-054426  Run B: 20260315-071830
Goal A: search for Python 3.13 new features
Goal B: search for Python 3.13 new features

Steps:  3 → 2 (-1)
Tokens: 1840 → 1200 (-640)
Cost:   $0.0073 → $0.0048
Time:   368ms → 420ms (+52ms)

Debug step by step: trace replay

Interactive step-through — see inputs and outputs for each tool call.

$ blueclaw trace replay 20260315-054426

Step 1: web_search (1ms) ✓
  input query: Python 3.13 new features
  output: Found 10 results...
[Enter] next · [q] quit >

Track performance over time: trace stats

Aggregate metrics across all your runs. Answer "how is my agent performing?" at a glance.

$ blueclaw trace stats --since 7

Trace Stats · 23 runs · last 7 days

Overview
  Total runs:     23
  Total steps:    87
  Avg steps/run:  3.8
  Avg tokens/run: 2,450
  Avg cost/run:   $0.0082
  Total cost:     $0.19

Timing
  Avg duration:    5.1s
  Median duration: 4.2s
  p95 duration:    12.3s
  Avg tool time:   2.1s (41% of wall)

Top Tools (by frequency)
  shell_command        34 calls (39%)
  web_search           28 calls (32%)
  http_request         18 calls (21%)
  file_read             7 calls (8%)

Failed Steps (3 across 2 runs · 3.4% step failure rate)
  timeout              2 (67%)
  network              1 (33%)

Filter by model to compare providers:

$ blueclaw trace stats --model ollama/llama3
$ blueclaw trace stats --model claude-sonnet-4-6 --since 30

All trace commands

Command	Use case
trace list	Find a run ID to inspect
trace show <id>	Detailed step table with timing
trace graph <id>	Quick tree view of tool sequence
trace timeline <id>	Find bottlenecks — where does time go?
trace explain <id>	LLM explains what happened and why
trace diff <id1> <id2>	Compare two runs (A/B test prompts)
trace replay <id>	Step-through debugger for tool calls
trace stats	Aggregate performance across all runs

Features

• Execution tracing — structured JSON traces with full observability tooling (see above)
• Model-agnostic — swap between Claude, Ollama, OpenAI, Gemini with one flag
• Web search — DuckDuckGo search via ddgs, returns top 5 results with titles, URLs, and snippets
• Persistent memory — CONTEXT.md updates in the background after each turn (instant exit), history.jsonl logs every run
• Interactive + scripted modes — blueclaw for chat, blueclaw run "..." for one-shot
• Shell execution — sandboxed shell_command tool with deny-list, 30s timeout, and interactive approval
• Workspace sandbox — path validation + destructive command deny-list
• Approval hooks — interactive confirmation for shell commands and new web domains
• Crash recovery — per-turn checkpoints in .blueclaw/last_turn.md
• Output truncation — 12k char limit prevents context blowout
• MCP support — bundled pdf-mcp server, custom stdio/SSE servers via config
• Skill system — progressive loading, index in prompt, full content on demand

Model Support

# Anthropic (default)
blueclaw

# Ollama (local, no data leaves your machine)
blueclaw --model ollama/llama3

# OpenAI
blueclaw --model openai/gpt-4.1-mini

# Gemini via LiteLLM
blueclaw --model litellm/gemini/gemini-2.0-flash

Set API keys in .env:

ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...

Commands

Command	Description
blueclaw	Start interactive session
blueclaw run "..."	Execute a single prompt and exit
blueclaw init	Initialize workspace directory
blueclaw history	View past run history
blueclaw trace list	List recent execution traces
blueclaw trace show <run_id>	Show detailed trace for a run
blueclaw trace explain <run_id>	LLM-powered explanation of a recorded trace
blueclaw trace graph <run_id>	Tree view of tool call sequence
blueclaw trace diff <id1> <id2>	Compare two traces side by side
blueclaw trace replay <run_id>	Step through a trace interactively
blueclaw trace timeline <run_id>	Waterfall timeline with timing and overhead
blueclaw trace stats	Aggregate metrics across all traces
blueclaw --version	Print version
blueclaw --model provider/model	Override model for this session

Configuration

blueclaw.yaml in your project root:

model:
  provider: anthropic
  model_id: claude-sonnet-4-6

workspace:
  path: ~/blueclaw/workspace/

tools:
  - web
  - shell                              # sandboxed shell execution (enables gh, git, etc.)
  - pdf
  - mcp:https://localhost:8080/sse     # custom MCP server

allowlist_domains:
  - github.com
  - docs.python.org

Architecture

Terminal input → cli.py → session.py → Strands Agent → Tools → workspace.py (sandbox) → observer.py (trace) → Response

Module	Purpose	Lines
cli.py	Typer entrypoints, welcome banner, trace tooling	~714
session.py	Config, model factory, agent, chat loop, background context updater	~537
workspace.py	Sandbox enforcement, context/history/trace I/O	~201
observer.py	Structured tool tracing + output truncation	~151
models.py	Pydantic models, trace schema, cost calculation, error classification	~124
tools/	Web, shell, MCP wiring (factory pattern)	~155
approval.py	Shell command + domain allowlist hooks	~51

Workspace Structure

~/blueclaw/workspace/
├── CONTEXT.md                    # Persistent agent knowledge (human-editable)
└── .blueclaw/
    ├── history.jsonl             # Append-only run log
    ├── last_turn.md              # Crash recovery checkpoint
    └── traces/                   # Structured execution traces
        └── 20260315-101201.json  # One JSON file per run

Development

# Install in dev mode
pip install -e ".[dev]"

# Run tests
pytest

# Lint
flake8 blueclaw/ tests/
black --check blueclaw/ tests/

License

MIT