A Flask REST API backed by two storage layers: FalkorDB holds memories as graph nodes connected by eleven authorable typed relationships plus three system-added edge types, while Qdrant holds a 1024-dimension embedding per memory. Recall blends semantic similarity, graph traversal, temporal alignment, tag overlap, and importance into a nine-component score, with multi-hop bridge discovery that surfaces the memory connecting two seed results rather than just the seeds themselves. Background enrichment and biologically-inspired consolidation cycles (daily decay, weekly creative linking, monthly clustering, optional forgetting) run continuously.
- Storage
- FalkorDB as the canonical graph record and source of truth, plus Qdrant for vectors. Recall degrades gracefully if Qdrant is unavailable; if FalkorDB is down the service returns 503, making the graph the hard dependency.
- Retrieval
- Hybrid nine-component scored recall across semantic similarity, graph traversal, temporal alignment, tag overlap, and importance, plus multi-hop bridge discovery across the typed relationship edges. Tags act as a hard pre-filter.
- Self-host
- Self-host: moderate
- License
- MIT
- Pricing
- The software is free and MIT-licensed; the only cost is infrastructure you run it on, which the project's own docs put at roughly $0.50–1/month on Railway after trial credits. No paid AutoMem tier exists — the README explicitly points enterprise, SOC2, and multi-tenant needs at Mem0, Letta, or Zep instead. · Free / OSS
- GitHub stars
- 795
- Last release
- 2026-07-07
- Last commit
- 2026-07-18
- First catalogued
- 2026-07-25
Strengths
- True graph-plus-vector hybrid with eleven authorable relationship types and multi-hop bridge discovery — a genuinely differentiated retrieval mechanism, not flat similarity search
- One installer wires up eight-plus clients, plus a remote HTTPS bridge for web-based assistants
- Unusually self-skeptical benchmark posture: the README leads with the neutral third-party board and explicitly demotes its own self-judged numbers, naming the judge model and the roughly twelve-point swing risk
- Documented, tunable consolidation cycles (decay, creative linking, clustering, forgetting) with explicit graceful-degradation behaviour per storage layer
- Two clear deployment paths — one-click Railway or `make dev` Docker Compose
Watch out
- Self-hosting requires three orchestrated services (Flask, FalkorDB, Qdrant) — there is no embedded or single-binary mode
- The headline Agent Memory Benchmark figures (LoCoMo 85.1%, BEAM 10M 57.4%) were run and submitted by AutoMem's own team, and the submission PR to the neutral leaderboard remains open and unmerged as of 2026-07-25
- The README's own limitations section is candid about real gaps: no SOC2 or HIPAA, no ACLs, no per-agent isolation for multi-agent swarms, tags acting as a hard pre-filter that can hide correct matches, and unreliable temporal conflict resolution
- Explicitly pre-1.0
- The MCP client is versioned and released separately from the core service, so there are two moving parts to keep in sync
Best for
- Solo developers and small teams wanting one shared memory across several coding-agent tools via MCP
- Users who specifically want typed-relationship reasoning rather than similarity search alone
- Teams comfortable running Docker or Railway infrastructure who value an honestly-benchmarked self-hosted backend
How it integrates
Benchmark results
No sourced results yet.
Sources
- Repo metadata — 794 stars, MIT, release v0.16.1 on 2026-07-07, main last commit 2026-07-18 (vendor)
- README — graph+vector architecture, nine-component recall, consolidation cycles, and the candid limitations section (vendor)
- Separate MCP client repo — the installable bridge for Claude, Cursor, Codex and others (vendor)
- Vendor benchmark page — AMB LoCoMo 85.1% and BEAM 10M 57.4%, self-run against a neutral harness (vendor)
- Submission PR to the neutral Agent Memory Benchmark leaderboard — confirmed open and unmerged as of 2026-07-25 (third-party)
Last verified 2026-07-25 · updated by discover-frameworks