AI/ML●●Solid
AgentKeeper – Cross-model memory for AI agents
Recovers 95% critical facts when switching GPT-4 ↔ Claude with real benchmarks.
Solve My ProblemBig Brain
thinklanceai
103mo ago
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GitHub Repository
Own your AI memory — import ChatGPT, Claude and Gemini exports, see what each AI knows about you. Checkpoint/restore and cross-model continuity for agents.
119 starsPython
AgentKeeper – cognitive persistence layer for AI agents
by thinklanceai·Mar 2, 2026·3 points·0 comments
AI Analysis
●●SolidSolve My ProblemShip ItBig Brain
Cross-provider agent memory is clever, but LLM context windows keep growing and RAG is already standard.
Strengths
- •Cognitive Reconstruction Engine (CRE) with critical-fact prioritization under token constraints is thoughtful—95% recovery rate across model switches is measurable
- •Provider-agnostic abstraction (OpenAI, Anthropic, Gemini, Ollama) means agents can fail over without rewrite
- •SQLite persistence with hot-swap capability is pragmatic infrastructure choice
Weaknesses
- •RAG systems (Pinecone, Weaviate, Qdrant) and long-context models (Claude 200K+, GPT-4 Turbo 128K) already solve multi-turn memory; unclear differentiation vs. simpler vector embedding + retrieval
- •No benchmarks on latency overhead, token cost comparison to naive context window expansion, or real-world failure mode data
Category
Target Audience
AI agent builders managing multi-model workflows or handling provider outages
Similar To
LangChain memory modules · LlamaIndex context managers · Mem0 agent memory
Post Description
Hi HN,
I built AgentKeeper to solve a fundamental problem with AI agents: memory persistence.
Today, agents lose memory when:
• switching providers • restarting • crashing
AgentKeeper introduces a cognitive persistence layer that stores facts independently of any LLM provider and reconstructs context dynamically.
It works across:
• OpenAI • Anthropic • Gemini • Ollama
Memory survives provider switches and restarts.
GitHub: https://github.com/Thinklanceai/agentkeeper
I'm curious if others have faced the same problem and how you're handling memory persistence today.
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