Acontext (memodb)
https://github.com/memodb-io/Acontext
'Agent Skills as a Memory Layer': an open-source skill-memory layer that stores what agents learn as agent skill files. Built by the Memobase team. The README now says Acontext is no longer maintained; the team has moved on to alook.ai.
Archived · Verified 2026-10-29 · fresh as of 2026-10-29 · 13 cited facts, 1 with a third-party source · 2 recorded changes · Suggest a correction
At a glance
- Tier
- T3
- Section
- Dedicated memory layers
- Created
- 2025-07-16 repo fix
- Latest release
- last commit 2026-04-21 repo fix
- License
- Apache-2.0 repo fix
- GitHub
- https://github.com/memodb-io/Acontext (3696★) repo fix
- Pricing
- searched not found fix
- Funding
- searched not found fix
- Freshness
- 2026-10-29 fix
- Layer
- agent-infra repo fix
- Licensing
- open-source repo fix
- Developer activity
- OpenRank 0.8 (12-mo avg 6.45) · 2 participants — 2026-09 third-party fix
Memory taxonomy
- storage
- file
- retrieval
- agentic
- persistence
- long-term
- update
- extraction
- unit
- skill
- governance
- editable
- conflict
- llm-arbitrate
When to use
Optimised for: searched not found
Anti-fit: searched not found
Pros & cons
Pros
searched not found
Cons
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Claims & capabilities
Acontext is an open-source skill memory layer for AI agents. It automatically captures learnings from agent runs and stores them as agent skill files — files you can read, edit, and share across agents, LLMs, and frameworks.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- searched not found
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- searched not found
- A2A
- searched not found
- OpenTelemetry
- searched not found
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Similar systems
Other dedicated memory layers in the catalog, ranked by inbound references.
- Mem0 T1
Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.
- Zep & Graphiti T1
Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.
- Cognee T1
"Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.
- Hindsight (Vectorize) T1
Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.
- Letta / MemGPT T1
Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.
- Memori T2
Structured, persistent memory layer for AI agents. Open source.
Sources (12 cited facts, 1 third-party)
Each fact links to the page it came from; the badge says who published it (how to read sources).
- What it is Framework repo
- What it does & what's distinct 'Agent Skills as a Memory Layer': an open-source skill-memory layer that stores what age… repo
- Claims / benefits Acontext is an open-source skill memory layer for AI agents. It automatically captures l… repo
- Created 2025-07-16 repo
- Latest release last commit 2026-04-21 repo
- License Apache-2.0 repo
- GitHub https://github.com/memodb-io/Acontext (3696★) repo
- Mindshare 3696 GitHub stars repo
- Project & sources https://github.com/memodb-io/Acontext repo
- licensing-bucket open-source repo
- taxonomy-three-layer-rollup agent-infra repo
- developer-activity-opendigger {"activity":2.22,"as_of":"2026-09","openrank_12mo":6.45,"openrank_current":0.8,"particip… third-party
What changed
Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)
Related systems
References (1)
- Memobase same team as — Same GitHub organisation `memodb` publishes both repos.