Product Updates

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Cloud SDK package renamed to everos-cloud:

pip install everos-cloud

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New Features & Updates:

• v1 API support
  SDK updated to align with EverOS API v1; all endpoints are now accessible via the client.v1 namespace
• Multimodal storage and retrieval (mRAG)
  Added multimodal support to memories.add(), enabling ingestion of images, PDFs, documents, and web content alongside text
• Self-evolving agent memory
  Introduced Cases & Skills APIs to support structured storage and retrieval of self-evolving agent memory
• Async client
  Added AsyncEverOS client with async/await support, with optional aiohttp backend for improved concurrency

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Bug Fixes

• Fixed group_ids parameter parsing error

pip install --upgrade evermemos

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New Features & Updates:

• Core Memory Operation
  • Updated memory types enum values
  • Search now accepts a list of group IDs
  • Get memories uses page-based pagination
• LLM Custom Settings
  • Available provider: openrouter
  • Available models: qwen/qwen3-235b-a22b-2507 (recommended), openai/gpt-4.1-mini

pip install --upgrade evermemos

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EverOS Python SDK v0.3.11 is now available:

• Please upgrade to the latest version, as v0.3.10 and earlier are not compatible.

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Bug Fixes

• Disk usage growth
  Fixed an issue where index fragment merges could fail, causing disk usage to grow rapidly without being reclaimed.

pip install --upgrade everos

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Bug Fixes

• Agent search reliability
  Fixed an issue where agentic search could become unavailable in certain cases.

pip install --upgrade everos

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Benchmarks and Search Quality

• LoCoMo benchmark runner
  Reproduce EverOS memory benchmark results yourself with the new runner under benchmarks/: TOML configuration, staged ingestion / search / answer / judge phases, and result artifacts. See benchmarks/README.md to get started.
• Improved hybrid search
  Heap-driven lazy expansion and global top-N competition raise retrieval quality, with stable LoCoMo scores around 91% for hybrid search and 93% for agentic search.
• Python 3.12 and 3.13 support
  CI coverage now includes Python 3.12 and 3.13.

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Bug Fixes

• Concurrent Knowledge cascade upserts now use an atomic insert-or-update path
• The OpenAPI application version stays in sync with the package version
• Profile middleware re-raises inner handler exceptions instead of swallowing them
• Reduced unnecessary LanceDB optimize I/O by throttling cascade optimize calls

pip install --upgrade everos

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Knowledge Wiki and Reflection

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Knowledge Wiki

• Progressive L0 → L1 → L2 structure
  An LLM extracts each document into a category → document → topic hierarchy, so agents drill down on demand.
• Deterministic classification
  Auto-sorted into an editable taxonomy of 20 built-in categories, predictable and overridable.
• Hybrid search, human-readable storage
  Keyword, vector, or hybrid retrieval, with every document stored as editable Markdown and the original preserved.

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Reflection

• Cleaner memory over time
  Related episodes merge into a single chronological narrative, resolving stale and contradictory fragments by keeping the latest state.
• Originals are never lost
  Replaced memories are soft-archived rather than deleted, and stay fully traceable on disk.
• Off by default, you stay in control
  Enable in ome.toml; runs on a weekly schedule and never touches the live conversation path.

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Getting Started

pip install --upgrade everos

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Security release

• sender_id path-safety validation — sender_id now carries the same path-safety guard as app_id / project_id: a character whitelist plus rejection of . / ... The whitelist admits @ and +, so email-style and plus-addressed identifiers still work.
• Defense-in-depth write containment — the Markdown writer now rejects any write that resolves outside the configured memory root, before any filesystem access (covering both the write and the append read-modify-write paths).
• Severity: High — unauthorized arbitrary .md file write on affected versions (≤ 1.0.0). No workaround other than upgrading.

pip install --upgrade everos

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Initial Release — Agent-First Memory System

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Markdown-First Storage

• Memory as plain Markdown files
  Every memory record lives as a human-readable .md file on disk. Read, edit, version with Git, or browse in Obsidian — the data is fully yours. What you see is what is stored.
• Auditable and user-controlled
  Memory files can be inspected and corrected directly without going through an API. Full data sovereignty.
• Lightweight embedded stack
  Markdown (truth) + SQLite (state) + LanceDB (index). All retrieval — vector search, BM25 keyword, and scalar filtering — runs locally. Wipe .index/ and the daemon rebuilds everything from the Markdown alone.

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Memory Classification

• Three memory tracks — User Memory, Agent Memory, and Wiki Memory (coming soon) — organized by who owns the memory: the user, the agent, or shared world knowledge
• Multi-granularity coexistence — episodes, atomic facts, foresight, and user profile exist side by side; the system selects the right granularity at retrieval time
• Independent memory scoping — memory owner (user_id / agent_id), app_id, project_id, and session_id are orthogonal dimensions; add and search by any combination

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Self-Evolving Agent Memory

• Experience loop — every agent execution is recorded as a case; cases are periodically clustered and distilled into reusable skills
• The system gets smarter with use — skills generalize from individual traces into structured, retrievable knowledge about how to approach a class of task

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Online and Offline Memory Strategies

• Online strategy — real-time memory ingestion and boundary detection during active sessions
• Offline strategy (OME) — an in-process async engine that derives atomic facts, foresight, profile updates, and agent skills from accumulated episodes in the background
• Each stage runs with configurable prompts and models

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Multimodal Support

• Ingest and extract memories from: text, image, audio, doc, pdf, html, email
• Multimodal content is processed alongside text through the same extraction pipeline

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Getting Started

pip install everos
everos init        # generate .env config
everos server start