AI Coding Agents · teaql

teaql-agent-kit

TEAQL Agent Kit is an evaluation framework for testing how AI coding agents handle business software tasks while maintaining semantic integrity, audit trails, and framework boundaries. It provides controlled environments with explicit rules, generated API contracts, and focused guidance to measure both agent capability and token efficiency.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/teaql/teaql-agent-kit

2.8k

GitHub stars

958

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	teaql/teaql-agent-kit
Owner	teaql
Primary language	Python
License	MIT — OSI-approved
Stars	2.8k
Forks	958
Open issues	0
Latest release	1.0 (2020-08-16)
Last updated	2026-07-06
Source	https://github.com/teaql/teaql-agent-kit

What teaql-agent-kit is

Python-based evaluation environment that benchmarks coding agents across dimensions including functional completion, API adherence, hallucination rate, audit coverage, and error recoverability. Supports evaluation across TEAQL implementations (Java, Rust stacks) with machine-readable feedback, generated contract files, and reproducible test harnesses for deterministic execution of non-deterministic LLM behavior.

Quickstart

Get the teaql-agent-kit source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/teaql/teaql-agent-kit.git2cd teaql-agent-kit3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Evaluating AI Coding Agent Behavior

Use TEAQL Agent Kit to systematically test how different LLMs and coding agents perform on business logic tasks, measuring their adherence to generated APIs, semantic correctness, and recovery from errors. Provides comparative benchmarks across agent implementations.

Assessing Token Efficiency in AI Coding

Measure whether agents can complete tasks using minimal, well-structured context (generated guides, focused contracts, assist commands) versus broad repository exploration. Validates whether semantic guardrails reduce hallucination and redundant reasoning.

Testing Audit and Compliance Preservation

Evaluate whether AI-generated code maintains audit traces, respects framework boundaries, and preserves business rules for regulated domains. Useful for orgs evaluating safe automation of business-critical code generation tasks.

Implementation considerations

Requires agents/tasks to follow explicit TEAQL API contracts and semantic rules; agents unfamiliar with domain-driven design or TEAQL modeling will need training/adaptation.
Evaluation depends on well-structured 'AGENTS.md' files, generated contract documentation, and assist command playbooks; teams must invest in these materials upfront.
No out-of-box integration with popular LLM APIs or coding agent frameworks; custom harnesses needed to wire agents into the evaluation pipeline.
Token efficiency measurement requires detailed instrumentation of agent context reads and reasoning steps; not automatic.
Supports both controlled (human-gated) and autonomous (no-gate) evaluation modes; choose mode based on risk tolerance and audit needs.

When to avoid it — and what to weigh

Seeking Production-Ready Agent Automation — This is explicitly an evaluation and benchmarking tool, not an ungated production deployment system. The README distinguishes between 'controlled evaluation' (main branch) and experimental 'no-gate' (autonomous) modes and explicitly warns against blind automation.
Looking for General AI Code Generation — TEAQL Agent Kit is purpose-built for evaluating agents on business software with strict semantic and audit requirements. It is not a general-purpose code assistant or IDE plugin; it requires domain-driven design discipline and TEAQL API contracts.
Needing Multi-Language Agent Support Out-of-Box — While the kit evaluates across TEAQL stacks (Java, Rust), the evaluation framework itself is Python-based. Integration with non-Python agents requires adapter work not provided by the project.
Requiring Vendor Support or SLA Guarantees — This is a research/evaluation repository. Latest release is v1.0 from 2020; while recent commits exist (July 2026), there is no indication of commercial support, SLAs, or vendor backing.

License & commercial use

MIT License (OSI-approved, permissive). Allows commercial use, modification, and distribution with minimal restrictions (retain license and copyright notice).

MIT License permits commercial use. However, this is an evaluation/benchmarking tool, not production software. Any agents or generated code evaluated or created using TEAQL Agent Kit remain subject to their own licenses. Users must ensure compliance with licenses of LLMs and agents being evaluated. No warranty or support is implied.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Possible
Assessment confidence	Medium

Security considerations

TEAQL Agent Kit evaluates whether AI agents respect framework boundaries and preserve audit traces—important for secure code generation. However, the framework itself is not a security product. Users evaluating untrusted LLMs or agents should note: (1) agents may hallucinate or bypass guardrails (autonomous branch explicitly tests this), (2) audit traces are only as secure as the underlying TEAQL implementation, (3) no formal security audit of the evaluation harness is evident in provided data. Requires security review before using in regulated environments.

Alternatives to consider

Anthropic Prompt Caching / Extended Context

Reduces agent token usage via cached context, but does not provide semantic guardrails, audit trace validation, or multi-agent comparative benchmarking like TEAQL Agent Kit.

LangChain / LlamaIndex Agent Frameworks

General-purpose agent execution frameworks with tool use and retrieval. Lack domain-driven design discipline, business rule validation, and audit-specific evaluation metrics that TEAQL Agent Kit provides.

Internal Custom Evaluation Harnesses

Orgs can build proprietary benchmarks for their own business software. TEAQL Agent Kit offers a reference architecture and reusable evaluation dimensions, but may require significant adaptation to non-TEAQL codebases.

Software development agency

Build on teaql-agent-kit with DEV.co software developers

If your team is considering AI-assisted code generation for business-critical software, TEAQL Agent Kit provides a structured evaluation framework to measure semantic correctness, audit compliance, and token efficiency. Review the main branch for controlled evaluation patterns, and contact Devco for guidance on integrating agent evaluation into your CI/CD pipeline.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

langwatch RD-Agent adk-python

Related on DEV.co

Explore the category and the services that help you build with it.

AI Coding Agents AI Development Custom Software Enterprise Software Talk to DEV.co

teaql-agent-kit FAQ

Can I use TEAQL Agent Kit to automate code changes in production?

No. The README explicitly states the goal is evaluation, not 'ungated production automation.' The autonomous branch tests unsafe behavior and shortcuts; it is for stress-testing and benchmarking, not deployment. Use only for controlled evaluation with human gates.

What agents/LLMs does TEAQL Agent Kit support?

Unknown. The README references 'coding agents and language models' generically and mentions evaluation across different agents, but does not list supported frameworks or LLM providers. Custom integration required.

Do I need TEAQL domain-driven design expertise to use this?

Yes, effectively. The kit is designed for agents and teams working with TEAQL-based business software (Java or Rust stacks). Requires familiarity with semantic modeling, API contracts, and audit trace requirements. Not suitable for teams new to DDD or TEAQL.

How recent is TEAQL Agent Kit?

Latest tagged release is v1.0 from August 2020; last commit is July 2026. This suggests ongoing development, but the 6-year gap between release and recent commits raises questions about stability and breaking changes. Requires code review.

Software development & web development with DEV.co

From first prototype to production, DEV.co delivers software development services around tools like teaql-agent-kit. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai coding agents and beyond.

Evaluate Your AI Coding Agent Strategy

Talk to DEV.co Schedule a Consultation