KI-Agenten
langmem
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
★ 1,7 Tsd. SterneMIT
Anleitung ansehen →agent-memory ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von tigerless-labs (tigerless-labs/agent-memory auf GitHub). Es hat 2.646 Sterne auf GitHub und 142 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 7. Oktober 2026 auf GitHub aktualisiert.
Long-term memory runtime for AI agents — plain Markdown as truth, local ranked retrieval, no API key
Decide which agents should share long-term memory and where the store should live (default ~/agent-memory-store).
Clone the repo and build the mem, mem-mcp and mem-hook binaries with uv sync --all-packages.
Run mem init, then mem setup --host claude-code / codex / muse-code to merge hooks, skills and MCP entries safely into each host.
Record a decision with mem record, recall it with mem recall, then delete .index and mem rebuild to prove nothing was lost.
One memory = one Markdown file with frontmatter (abstract, type, validity interval, links, provenance) — greppable, git-able and portable.
Recall returns an L0 list of paths/abstracts/scores (via BM25 FTS5, optionally fused with vectors), so the agent opens only the depth it needs.
Distillation triggers at conversation boundaries (SessionStart/Stop/SessionEnd hooks) without the agent having to remember to write.
An independent consolidation pass on its own clock can add and update; deletions only ever arrive as proposals you confirm, and supersede keeps history (recall --as-of).
Claude Code, Codex CLI and Muse Code share one store through the same pipeline; the CLI is the universal fallback for anything that can run a shell command.
There is no LLM client inside the library — judgement is borrowed from the host agent's own CLI, keeping writes visible in your transcript.
Clone agent-memory and build its binaries with uv (Python 3.12+, no PyPI release yet).
git clone https://github.com/tigerless-labs/agent-memory.git
cd agent-memoryCreate the default store at ~/agent-memory-store (or set AGENT_MEMORY_STORE).
mem initProbe and configure a host, then record and recall a first memory.
mem setup --host claude-code
mem record --type decision --field project=agent-memory --abstract "Markdown files are the single source of truth" --body "Indexes are rebuildable caches."tigerless-labs maintains it on GitHub.
Yes, MIT-licensed, and it runs entirely locally.
No API key at all — the README stresses none of it needs one; distillation borrows the host agent's existing login/model.
Those are service/graph or profile systems. agent-memory is a local runtime whose store is plain Markdown files with a rebuildable index, shared across agent hosts.
Clone, uv sync --all-packages, mem init, mem setup --host <your agent>, then mem record / mem recall.
LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.
Multi-agent platform where every agent gets its own computer and long-term memory.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.