| name | recallmax |
| description | FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens. |
| category | memory |
| risk | safe |
| source | community |
| date_added | 2026-03-13 |
| author | christopherlhammer11-ai |
| tags | ["memory","context","rag","summarization","compression","long-context","agent-infrastructure"] |
| tools | ["claude","cursor","codex","gemini","copilot","windsurf","antigravity","grok"] |
RecallMax — God-Tier Long-Context Memory
Overview
RecallMax enhances AI agent memory capabilities dramatically. Inject 500K to 1M clean tokens of external context without hallucination drift. Auto-summarize conversations while preserving tone, sarcasm, and intent. Compress multi-turn histories into high-density token sequences.
Free forever. Built by the Genesis Agent Marketplace.
Install
npx skills add christopherlhammer11-ai/recallmax
When to Use This Skill
- Use when your agent loses context in long conversations (50+ turns)
- Use when injecting large RAG/external documents into agent context
- Use when you need to compress conversation history without losing meaning
- Use when fact-checking claims across a long thread
- Use for any agent that needs to remember everything
How It Works
Step 1: Context Injection
RecallMax cleanly injects external context (documents, RAG results, prior conversations) into the agent's working memory. Unlike naive concatenation, it:
- Deduplicates overlapping content
- Preserves source attribution
- Prevents hallucination drift from context pollution
Step 2: Adaptive Summarization
As conversations grow, RecallMax automatically summarizes older turns while preserving:
- Tone — sarcasm, formality, urgency
- Intent — what the user actually wants vs. what they said
- Key facts — numbers, names, decisions, commitments
- Emotional register — frustration, excitement, confusion
Step 3: History Compression
Compress a 14-turn conversation history into ~800 high-density tokens that retain full semantic meaning. The compressed output can be re-expanded if needed.
Step 4: Fact Verification
Built-in cross-reference checks for controversial or ambiguous claims within the conversation context. Flags contradictions and unsupported assertions.
Best Practices
- ✅ Use RecallMax at the start of long-running agent sessions
- ✅ Enable auto-summarization for conversations beyond 20 turns
- ✅ Use compression before hitting context window limits
- ✅ Let the fact verifier run on high-stakes outputs
- ❌ Don't inject unvetted external content without dedup
- ❌ Don't skip summarization and rely on raw truncation
Related Skills
@tool-use-guardian - Tool-call reliability wrapper (also free from Genesis Marketplace)
