By ThinkfleetAI

Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it.

ThinkfleetAI/memmesh

Run MemMesh's competitive benchmark harness (LOCOMO / BEAM) to compare retrieval quality, tokens, latency, and cost against Mem0, Zep, full-context, and naive-RAG baselines.

ThinkfleetAI/memmesh

Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories)…

ThinkfleetAI/memmesh

Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation.

ThinkfleetAI/memmesh

MemMesh CLI + local MCP server — the zero-infra, no-API-key path to the same engine as the hosted SDK.

ThinkfleetAI/memmesh

MemMesh TypeScript SDK reference (@thinkfleet/memory-sdk) for the hosted platform at app.memmesh.ai.

ThinkfleetAI/memmesh

Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance.

ThinkfleetAI/memmesh

Explain a MemMesh prediction or recalled fact — surface its provenance (evidence memories), its calibrated confidence, and whether the model abstained and why.

ThinkfleetAI/memmesh

Consolidate MemMesh memories — find duplicates and contradictions, merge or supersede them, and retire stale entries — to keep search results clean.

ThinkfleetAI/memmesh

Export MemMesh memories for a project/user to a portable Markdown (or JSONL) file for backup, migration, or sharing.

ThinkfleetAI/memmesh

Delete or correct a MemMesh memory. An agent skill from ThinkfleetAI/memmesh.

ThinkfleetAI/memmesh

Diagnose MemMesh connectivity and correctness — is the MCP server reachable, is the key/config valid, do read and write actually work?

ThinkfleetAI/memmesh

Import memories into MemMesh from an exported file, a native MEMORY.md / CLAUDE.md, an ADR/decision log, or a mem0 export.

ThinkfleetAI/memmesh

Quick memory lookup — search MemMesh and show compact one-liner results, or fetch one memory by id.

ThinkfleetAI/memmesh

Protect a critical MemMesh memory from consolidation/pruning by raising its importance and marking it high-impact — or unpin to release it.

ThinkfleetAI/memmesh

Store a memory in MemMesh from the user's input. An agent skill from ThinkfleetAI/memmesh.

ThinkfleetAI/memmesh

Override the auto-detected project scope for MemMesh reads/writes, or widen to cross-project / user-level search.

ThinkfleetAI/memmesh

Browse all stored MemMesh memories for a project/user, grouped by type/scope with full content.

ThinkfleetAI/memmesh

Persistent hierarchical memory shared across every AI tool the user runs.

ThinkfleetAI/memmesh

Integrate MemMesh into an existing repository using a goal-driven, test-first (TDD) pipeline.

ThinkfleetAI/memmesh

Migrate an existing memory setup ONTO MemMesh — either from another vendor (Mem0, Zep, Letta/MemGPT, a raw vector store) or from MemMesh Local (SQLite) up to MemMesh Hosted.

ThinkfleetAI/memmesh

Verify a MemMesh integration produced by memmesh-integrate. An agent skill from ThinkfleetAI/memmesh.

ThinkfleetAI/memmesh

Show MemMesh usage stats for a project/user — total count and breakdowns by type, scope, and status, plus the oldest/newest timestamps.

ThinkfleetAI/memmesh

Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes.

ThinkfleetAI/memmesh