# kayba-ai/agentic-context-engine

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

Repository: https://github.com/kayba-ai/agentic-context-engine
Canonical: https://ross.abutalabs.com/products/agentic-context-engine
Homepage: https://www.kayba.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai-agents, ai-tools, context-engineering, llm, python, agent-memory, agents, ai, memory, agent-learning, machine-learning
Last push: 2026-07-08T23:36:10+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 83, longevity 23
- inputs: {"age_days": 322, "days_push": 56, "days_rel": 118, "gap_med": 4.0, "n_releases_24m": 29}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2558, forks 302 (observed 2026-08-28T04:07:00.802019+00:00)

## What it is
Agentic Context Engine (ACE) is a Python library that lets LLM-based agents learn from experience by maintaining and evolving an evolving context/memory of strategies and lessons. It is also offered as a hosted solution that automatically investigates agent errors, proposes fixes as PRs, and tracks their performance.

## Use cases
- make my ai agent learn from past mistakes
- add persistent memory to an llm agent
- improve agent prompts automatically from experience
- reduce repeated errors in my agent runs
- context engineering for llm agents
- auto-fix failing agent tool calls

## When to choose
- you build LLM agents in Python and want them to accumulate learned strategies over time
- you want an open-source, self-hostable agent memory/context engine
- you want automated investigation and fix proposals for recurring agent errors

## When to avoid
- you need a general-purpose agent orchestration framework rather than memory/context learning
- your stack is not Python and you cannot use an API-based integration
- you require fully deterministic, non-learning agent behavior

## Facets
- artifact type: library
- maturity: active
- function: agent-framework, machine-learning, llm-inference, prompt-engineering
- domain: large-language-models, machine-learning, developer-tools
- platform: python, cross-platform
- tags: agent-memory, context-engineering, agent-learning, llm-agents, self-improving-agents, ai-agents

## Member repositories
- kayba-ai/agentic-context-engine (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:00.802019+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:38.260348+00:00, confidence not recorded.
  - readme: https://github.com/kayba-ai/agentic-context-engine (fetched 2026-08-28T04:07:00.802019+00:00, sha 4ade9622ab57)
  - homepage: https://www.kayba.ai (fetched 2026-08-29T10:06:16.892400+00:00, sha 57403439089d)
- Data as of 2026-08-30T08:39:29.467469+00:00.