ACE Context Learning Engine

What does this agent do, and when should you use it?

Agentic Context Engine (ACE) is an open-source Python engine for adding a persistent learning loop to AI agents. It stores strategies in a persistent Skillbook and divides work among an Agent, Reflector, and SkillManager for execution, trace analysis, and strategy curation. Its Recursive Reflector writes and runs Python in a sandboxed environment to search traces for patterns, isolate errors, and derive actionable insights. The repository offers ACELiteLLM, ACE, TraceAnalyser, BrowserUse, LangChain, and ClaudeCode runners, plus a composable pipeline system. It can be used as a library in a Python project and exposes ace, kayba, and ace-mcp commands; Kayba is the managed service powered by the engine.

With ACELiteLLM, an application calls .ask() to invoke a model, then passes a correction to .learn_from_feedback(); ACE extracts a strategy, updates the Skillbook, and makes the strategy available to later .ask() calls. For existing logs, agent.learn_from_traces(your_existing_traces) provides a path to learn from recorded traces without rerunning tasks. A full learning flow can combine AgentStep, EvaluateStep, ReflectStep, UpdateStep, and DeduplicateStep, or use learning_tail() for the standard tail sequence. The environment sends a trace to the Reflector; the Recursive Reflector executes Python analysis in a sandbox, and the SkillManager adds, refines, or removes Skillbook strategies. The CLI supports ace setup for model and key configuration, ace models for model and pricing search, ace validate for connection testing, and kayba for trace upload, insight retrieval, and prompt management.

1. A Python developer whose question-answering or tool-using agent receives human corrections and needs those corrections to influence later requests.
2. A team with historical agent logs that wants to extract reusable strategies through learn_from_traces() instead of rerunning the original tasks.
3. A developer using browser-use for browser automation who wants repeated runs to improve through accumulated strategies.
4. A team already operating a LangChain chain or agent that wants to add a learning loop through the LangChain runner.
5. An engineering team running Claude Code CLI tasks that wants to apply the learning loop through the ClaudeCode runner.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

You need Python, uv, and an API key for a supported model provider. Install with:

uv add ace-framework

For guided configuration:

ace setup

Or set credentials manually; for OpenAI:

export OPENAI_API_KEY="your-key"

Install integration extras as needed:

uv add 'ace-framework[browser-use]'
uv add 'ace-framework[langchain]'
uv add 'ace-framework[mcp]'

How do you use this agent?

After running ace setup or setting an API key, a minimal learning flow is:

from ace import ACELiteLLM

agent = ACELiteLLM(model="gpt-4o-mini")
answer = agent.ask("Is there a seahorse emoji?")
agent.learn_from_feedback("There is no seahorse emoji in Unicode.")
answer = agent.ask("Is there a seahorse emoji?")
print(agent.get_strategies())

Use ace validate <model> to test a model connection, or ace models <query> to search available models and pricing.

FAQ