Life Forge
github.com/zariffromlatif/life-forge- Category
- Developer Tools
- Rank
- No. 1846Tools index
Previous survey · No. 1808 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Model & Agent Evaluation
- GitHub
- 6 stars
- Latest release
- v0.4.5
- Date
About
Life Forge is an open-source framework for adversarial red-teaming and stress-testing autonomous AI agents, such as Claude, GPT-4o, Llama, or custom agent frameworks, inside a simulated sandbox with an ERP database, vendor catalogs, and an email inbox. It uses artificial-life quality-diversity algorithms, specifically 3D MAP-Elites, to co-evolve increasingly challenging scenarios and perturbations against the agent under test, surfacing failure modes that fixed test suites would miss.
At a glance
Life Forge tests agents in simulated scenarios and grades what they do to the simulated world. It is worth evaluating if a bad tool call matters more to you than whether the agent’s final reply sounds safe.
Good fit
Teams comparing tool-using models on known failure scenarios.
Consider something else when
Skip it as proof that an agent is safe in production or if you cannot model the actions that matter.
Start here
Read one scenario and its expected outcome. Define a synthetic action your agent should refuse, then check whether the report identifies the action and its effect when it occurs.
Cost and requirements
MIT-licensed tool requiring Python 3.10 or newer. It documents both local model and hosted provider options, with compute or provider charges depending on the setup.
Limits to know
Results depend on the scenarios and simulated tools. A hosted model can receive evaluation inputs, so use synthetic data and check which real permissions the simulation leaves out.
Related work
- Seahaven (context)
Sources checked 2026-11-05. Source-based assessment.
Why it made the leaderboard
Gives agentic engineers an automated way to discover edge-case failures by evolving adversarial scenarios against an agent, rather than relying on fixed benchmark suites.
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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.