explainable-agent 0.1.3

A local-first, explainable AI agent framework with self-healing, detailed error diagnostics, and interactive tool-calling traces.

🔬 Explainable Agent Lab

A local-first, explainable agent framework designed to guide developers in building robust AI agents.

Building reliable agents is hard. LLMs hallucinate, get stuck in infinite loops, or fail to parse tools correctly. Explainable Agent Lab is built to solve this by focusing on explainability and guidance.

✨ Key Features:

• Show the Hidden Errors: Reveal exactly where and why an agent fails (e.g., low confidence, schema violations).
• Self-Healing: The agent automatically analyzes its own errors and proposes alternative tool-based solutions.
• Visual Terminal Tracking: Step-by-step interactive and colorful tracking using the rich library (--verbose).
• Detailed Diagnostic Reports: Actionable suggestions on hallucination risks, loop patterns, and prompt improvements.
• Chaos Engineering (Stress Testing): Inject simulated tool errors (e.g., timeouts, missing data) to test your agent's self-healing capabilities.
• Efficiency Diagnostics: Track token usage and step counts to identify context window exhaustion and prompt inefficiencies.
• Multi-Agent Orchestration (Team of Thoughts): Coordinate multiple specialized agents with transparent delegation plans, sub-agent traces, and orchestration diagnostics.

---

🚀 Quick Start

1. Install

Install directly from PyPI:

pip install explainable-agent

(Optional: for development, clone the repo and run pip install -e .[dev])

2. Connect Your Local LLM

You can use any OpenAI-compatible local server like Ollama or LM Studio.

• Ollama: http://localhost:11434/v1 (e.g., model: ministral-3:14b)
• LM Studio: http://localhost:1234/v1 (e.g., model: gpt-oss-20b)

Tip: You can create a .env file in your working directory to set your defaults (see .env.example).

3. Run the Agent

The package installs a global CLI command explainable-agent.

Example using Ollama:

explainable-agent \
  --base-url http://localhost:11434/v1 \
  --model ministral-3:14b \
  --task "calculate_math: (215*4)-12" \
  --verbose

---

💻 Using the Python API

Easily integrate the agent into your codebase or create custom tools using the @define_tool decorator.

Check out the examples/ directory:

• examples/showcase_all_features.py - A comprehensive test script demonstrating Built-in Tools, Custom Tools, Self-Healing, Chaos Mode, and Evaluation.
• examples/basic_usage.py - Initialize and run the agent programmatically.
• examples/custom_tool_usage.py - Learn how to build custom tools and watch the agent self-heal from errors.
• examples/multi_agent_demo.py - Multi-agent orchestration: researcher + db_expert working together.

Run the showcase:

python examples/showcase_all_features.py

---

📊 Evaluation & Custom Datasets

Evaluate your fine-tuned models or custom datasets easily. The pipeline parses messy outputs, repairs broken JSON, and generates actionable Markdown reports.

1. Create a .jsonl dataset (See examples/custom_eval_sample.jsonl)

2. Run the evaluation:

python scripts/eval_hf_tool_calls.py \
  --dataset examples/custom_eval_sample.jsonl \
  --model ministral-3:14b

We also support standard benchmarks out of the box:

• HF Tool Calls: data/evals/hf_xlam_fc_sample.jsonl
• BFCL SQL: data/evals/bfcl_sql/BFCL_v3_sql.json
• SWE-bench Lite: data/evals/swebench_lite_test.jsonl

---

🛠️ Built-in Tools

The agent comes with out-of-the-box tools ready to use: duckduckgo_search, calculate_math, read_text_file, list_workspace_files, now_utc, sqlite_init_demo, sqlite_list_tables, sqlite_describe_table, sqlite_query, sqlite_execute.

---

License: MIT | Current Release: v0.1.3