{"adoption": {"forks": 302, "observed_at": "2026-08-28T04:07:00.802019+00:00", "stars": 2558}, "canonical_url": "https://ross.abutalabs.com/products/agentic-context-engine", "card": {"archived": false, "artifact_type": "library", "description": "🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai ", "domain": ["large-language-models", "machine-learning", "developer-tools"], "enriched": true, "function": ["agent-framework", "machine-learning", "llm-inference", "prompt-engineering"], "health_score": 94, "homepage": "https://www.kayba.ai", "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "active", "member_repos": ["kayba-ai/agentic-context-engine"], "name": "kayba-ai/agentic-context-engine", "platform": ["python", "cross-platform"], "pushed_at": "2026-07-08T23:36:10+00:00", "repo": "kayba-ai/agentic-context-engine", "stars": 2558, "tags": ["agent-memory", "context-engineering", "agent-learning", "llm-agents", "self-improving-agents", "ai-agents"], "topics": ["ai-agents", "ai-tools", "context-engineering", "llm", "python", "agent-memory", "agents", "ai", "memory", "agent-learning", "machine-learning"], "urls": [], "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"], "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.", "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"], "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"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/agentic-context-engine", "repo": "kayba-ai/agentic-context-engine", "role": "main", "score": 75}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:00.802019+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:23:38.260348+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "4ade9622ab578ef243912481d040315adaff11281bf5fa8efd69485f14f8217f", "fetched_at": "2026-08-28T04:07:00.802019+00:00", "kind": "readme", "missing": false, "url": "https://github.com/kayba-ai/agentic-context-engine"}, {"content_hash": "57403439089d17f43c31d1c28077ca879b07d1f8f205b4750ed4bcb18479bad0", "fetched_at": "2026-08-29T10:06:16.892400+00:00", "kind": "homepage", "missing": false, "url": "https://www.kayba.ai"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 91, "longevity": 23, "rhythm": 83}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 322, "days_push": 56, "days_rel": 118, "gap_med": 4.0, "n_releases_24m": 29}, "score": 75, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}