V0.2.0

Release Notes for v0.2.0

This release adds a pluggable on-prem backend (talk to a local Moorcheh server instead of the cloud), a new memanto analyze command suite for comparing Memanto against Mem0 / Letta / Supermemory, an official langgraph-memanto integration package, a separate detect-conflicts job entrypoint, plus large UI upgrades (Connect tab, memory-history timeline, daily summary view, file pagination).

New Features

• On-prem Moorcheh backend (memanto/app/clients/{backend,onprem,moorcheh}.py,
  memanto/app/config.py, memanto/app/routes/{health,auth_deps}.py,
  memanto/cli/commands/{core,config_cmd}.py)

  • New MEMANTO_BACKEND setting (cloud | on-prem) routes every call through
    a backend-aware dispatcher that exposes the same
    namespaces / documents / similarity_search / answer / files / vectors
    shape regardless of target — service code never branches.
  • First-run wizard now asks Cloud vs On-Prem; on-prem path installs
    moorcheh-client>=0.1.3, prompts for embedding + LLM provider
    (ollama / openai / cohere), persists choices to
    ~/.memanto/on-prem/state.json, writes the full LLM block to
    ~/.moorcheh/config.json before moorcheh up, then pulls Ollama
    models into the container.
  • On-prem data lives under ~/.memanto/on-prem/ (sessions, agents, summaries)
    so cloud and on-prem never share local state; switching backends clears the
    active session.
  • New memanto config backend [cloud|on-prem] CLI command for runtime
    switching, plus Backend, MOORCHEH_ONPREM_URL (default
    http://localhost:8080) and MOORCHEH_ONPREM_TIMEOUT (default 300) rows
    in memanto config show.
  • Health check, startup validation, and the agent delete flow are all
    backend-aware.

• Official LangGraph integration package (integrations/langgraph/)

  • New langgraph-memanto PyPI package: create_memanto_tools returns native
    LangChain @tool wrappers (memanto_remember, memanto_recall,
    memanto_answer); create_recall_node / create_remember_node are
    pre-built graph nodes; MemantoStore is a drop-in BaseStore
    implementation for the official LangGraph Store API.
  • Lazy, exception-driven, thread-safe setup so agents are created only on
    first tool call; auto-detects type via the parser; uses recall_recent
    when no query is provided.

• memanto detect-conflicts + scheduled job split (memanto/cli/commands/memory.py,
  memanto/cli/commands/schedule.py, memanto/app/services/daily_analysis_service.py,
  memanto/app/routes/memory.py)

  • Conflict detection split out of daily-summary into its own command,
    POST /{agent_id}/conflicts/generate REST endpoint, and
    DirectClient.generate_conflict_report() method.
  • New hidden memanto schedule _run entrypoint executes daily-summary +
    detect-conflicts back-to-back; OS scheduler now points at it. On-prem
    backend short-circuits with a clear error (scheduled job depends on
    cloud-only LLM Answer).
  • daily_summary_service.py renamed → daily_analysis_service.py.

• UI: Connect tab, memory timeline, daily summary, file pagination
  (memanto/app/ui/static/index.html, memanto/app/ui/routes/ui_router.py)

  • Connect tab installs/removes Memanto skills into any registered agent
    (Claude Code, Cursor, etc.) via the underlying install_agent /
    remove_agent engine, with a connections.json registry tracking
    project-local vs global installs.
  • Memory History page with a vertical timeline of every change (created,
    updated, conflict resolved) per memory.
  • Daily summary + Unreviewed conflicts widget surfaced on the
    dashboard (Daily Summary tab renders the generated MD and shows days with
    pending conflict review).
  • Answer panel is backend-aware: on-prem shows
    provider/model/api-key only (no cloud-only knobs) and writes to
    ~/.moorcheh/config.json without polluting the shared cloud yaml.
  • Memory Explorer + file uploads now use cursor pagination through
    documents.fetch_text_data (next_token / has_more) instead of being
    capped at 100 items per namespace.

Improvements

• Backend-aware recall_* REST endpoints (memanto/app/routes/memory.py)

  • recall_as_of, recall_changed_since, recall_recent, and the
    underlying MemoryReadService methods now treat limit=None as
    "fetch all" — the CostGuard.validate_k_limit cap is only applied when a
    limit is explicitly set.
  • answer.generate calls route through get_active_llm_model() so the LLM
    identifier comes from cloud settings on cloud, on-prem state.json on
    on-prem, with the field omitted entirely when on-prem has no LLM
    configured (server picks its own default).

• Stale active-session handling (memanto/app/services/session_service.py,
  memanto/app/models/session.py)

  • get_active_session() now clears the stale active marker and returns
    None when the session has expired, instead of returning an expired
    Session.
  • All datetimes flow through a single utc_now() helper; Pydantic v1
    Config.json_encoders blocks removed from session models.

• Connect engine ↔ registry sync (memanto/cli/connect/engine.py,
  memanto/cli/config/manager.py)

  • install_agent / remove_agent now sync their results into
    ~/.memanto/connections.json so the UI's Connections page reflects what
    the CLI did and vice versa.

• CrewAI integration (integrations/crewai/...)

  • Sessions are validated and refreshed on tool init so long-running CrewAI
    runs don't fail on a silently-expired session.

Tests

• New tests/test_backend.py covering cloud/on-prem dispatcher behavior and
  get_active_llm_model fallbacks.
• New tests/test_analyze.py covering the Mem0/Letta/Supermemory export +
  compare + report flow end-to-end with mocked provider responses.
• tests/test_cli.py and tests/test_unit.py expanded to cover the new
  detect-conflicts / schedule _run paths.

Full Changelog

Full Changelog: v0.1.3...v0.2.0