MemKernel

Site: memkernel.dev · Package: pypi.org/project/memkernel

Code-aware memory server for AI coding copilots.

MemKernel gives Claude Code (and any MCP-compatible copilot) persistent memory across sessions. When Claude compacts context or a session ends, project decisions, conventions, and code understanding are normally lost. MemKernel stores them in a local vector database and retrieves them on demand.

Key differentiator: MemKernel understands code as code. It uses tree-sitter AST parsing to chunk files at function and class boundaries, embeds chunks with scope context, and provides hybrid search (dense + BM25) for both semantic queries and exact identifier lookup.

Tiered memory (0.2): memories live in three tiers with different persistence rules — working (the current task, hours), short-term (episodes and observations, decays unless recalled) and long-term (decisions, conventions, facts, procedures, code; never decays, replaced by supersession). Recall ranks by relevance, recency and importance; consolidate and promote move what proved useful into long-term memory; an LLM-wiki bridge keeps a human-readable, git-versioned copy of long-term knowledge. Design: docs/architecture/memory-tiers.md.

Requirements

• Python 3.11 – 3.13
• Poetry (for development installs)

Install

pip install memkernel

Or with Poetry:

poetry add memkernel

Add to Claude Code

1. Register the MCP server

Add MemKernel to .claude/settings.json in your project:

{
  "mcpServers": {
    "memkernel": {
      "command": "memkernel",
      "args": [],
      "env": {
        "MEMKERNEL_PROJECT_ID": "my-project"
      }
    }
  }
}

Replace my-project with a short identifier for the project — memories are isolated per project.

2. Add memory instructions to CLAUDE.md

Copy the contents of templates/claude-memory-instructions.md into your project's CLAUDE.md. This tells Claude when and how to call the memory tools.

3. (Optional) Install lifecycle hooks

Copy the hook scripts from templates/hooks/ to .claude/hooks/ and register them in .claude/settings.json:

See the Prerequisites section inside each script for the Python environment requirement. Each script is a thin wrapper over MemoryLifecycle, so an in-process harness gets the same behaviour without the scripts.

MCP Tools

Memory types and tiers

Retention: ttl = base_ttl × (1 + access_boost × access_count), measured from the later of creation and last access. Long-term memories never expire; a newer memory that supersedes an older one hides it from recall.

Session protocol

1. Session start — recall(tier="long_term", query=<task>), plus the latest checkpoint (checkpoint_only=true). The session-start.py hook does this and prints promotion candidates.
2. While working — retain(type="task") when the plan changes; retain(type="observation") for findings; durable knowledge as decision / convention / fact / procedure.
3. Before compaction — retain(type="episode", checkpoint=true) (the pre-compact.py hook) and consolidate().
4. After compaction / end of phase — promote(ref_id, type, content) for the candidates worth keeping.
5. Large artefacts — retain(type="document", content=<body>, source=<name>) then ask(ref_id, question); never get a document to read it.
6. Curate — wiki_export → review the drafts → wiki_import.

Quick examples

retain(
  content="We chose PostgreSQL over SQLite because the team needs concurrent writes",
  type="decision",
  session_id="session-2024-06-01"
)

recall(query="database choice", top_k=5)

recall(query="session checkpoint", checkpoint_only=True)

retain(content="pytest hangs unless MEMKERNEL_PROJECT_ID is set", type="observation", tags=["ci"])

consolidate()                      # -> {"purged": 0, "candidates": [{"ref_id": ..., "access_count": 3, ...}]}

promote(ref_id="<candidate>", type="fact", content="CI must export MEMKERNEL_PROJECT_ID before pytest.")

retain(content="Use arq, not Celery", type="decision", supersedes="<old decision ref_id>")

get(ref_id="<ref_id from recall>")

forget(ref_id="<ref_id>")

retain(content=<the whole spec>, type="document", source="specs/auth/spec.md")

ask(ref_id="<document ref_id>", question="Which endpoints need rate limiting?", mode="auto")

Configuration

All settings are read from environment variables with the MEMKERNEL_ prefix, or from a .env file.

Transports

stdio (default)

Used by Claude Code and most MCP clients. The client spawns the server as a subprocess.

memkernel           # stdio, uses defaults

Streamable HTTP

For persistent server processes or multi-client setups:

MEMKERNEL_TRANSPORT=streamable-http memkernel

The server listens at http://127.0.0.1:8765/mcp by default.

Embedding MemKernel in your own harness

from memkernel.factory import build_service
from memkernel.core.models import MemoryType

svc = build_service()                      # settings from MEMKERNEL_* env vars
svc.retain("Use Postgres", MemoryType.DECISION)
for ref in svc.recall("database"):
    print(ref.tier, ref.score, ref.preview)
report = svc.consolidate()                 # {"purged": n, "candidates": [...]}

build_service() is the supported entry point; the MCP server, the admin API and the hook templates all use it. Integration notes for code-copilot-team and haa are in docs/integration/harness-integration.md.

The session protocol as a component

MemoryLifecycle packages what the hook scripts do, so a harness calls four methods instead of re-implementing the protocol. Every method returns markdown ready for injection and does nothing but talk to the service:

from memkernel.factory import build_service
from memkernel.lifecycle import MemoryLifecycle

memory = MemoryLifecycle(build_service())
system_context = memory.on_session_start()       # checkpoint, long-term context, promotion candidates, wiki pointer
...
memory.on_pre_compact(summary)                   # checkpoint episode + consolidation, before the context is compacted
recovered = memory.on_post_compact()             # what to re-inject after compaction
memory.on_session_end(summary)                   # final checkpoint + consolidation

Documents and ask

retain(type="document") keeps large artefacts out of the context window: the body goes to the document store (built-in: the project's own ChromaDB collection), its chunks are indexed for hybrid search, and the memkernel record is a summary. ask(ref_id, question, mode) answers through a ReasonerPort:

• MEMKERNEL_REASONER=local (default) — no LLM: the question is matched against the document's chunks with dense + BM25 + RRF and the best chunks come back as the answer, mode="rag".

• MEMKERNEL_REASONER=rlmstudio — delegates to rlm-studio's interact(content, question, mode=mode), so direct, rag, rlm and auto select how the document is read (ADR-009). rlm-studio is not a memkernel dependency and is not on PyPI yet; install it from git into the same environment:

  pip install "rlm-studio @ git+https://github.com/gosha70/rlm-studio"
  

  When it is not importable, build_service() logs a warning and uses the local reasoner. With an external reasoner, consolidate(synthesize=True) also drafts long-term statements from the session's short-term memories.

LLM wiki bridge

Long-term memory has a human-readable twin: a markdown wiki in the LLM Wiki pattern (index.md, log.md, typed pages with cited sources — the same schema as code-copilot-team/knowledge/wiki).

• wiki_export compiles every long-term memory that has no page yet into a draft page under decisions/, concepts/ or workflows/, with the required sections and a memkernel://<project>/<ref_id> citation. Existing pages are never overwritten.
• wiki_import indexes curated pages as long-term memories so recall finds the reviewed version; a changed page supersedes its earlier memory.

Set MEMKERNEL_WIKI_DIR (for example knowledge/wiki) or pass wiki_dir to the tools. Rationale and the proposed standalone ai-wiki repo: docs/adr/ADR-008-llm-wiki-as-semantic-layer.md.

Admin API

A FastAPI admin server provides health and stats endpoints (independent of MCP):

uvicorn memkernel.api.app:app --host 127.0.0.1 --port 8766

Development

git clone https://github.com/gosha70/memkernel
cd memkernel
poetry install
poetry run pytest --tb=short -q          # full test suite
poetry run pytest -m "not slow" -q       # skip slow tests
ruff check . && mypy src/                # lint + type check

CI runs on every push and PR: lint, type check, tests (≥ 80% coverage), security scan, and build verification.

Releasing

Releases are published to PyPI by .github/workflows/release.yml using trusted publishing (no token in the repo). To cut a release:

1. Set version in pyproject.toml and move the [Unreleased] entries in CHANGELOG.md under the new version.
2. Merge to main with CI green.
3. Tag and push: git tag -a v0.3.0 -m "memkernel 0.3.0" && git push origin v0.3.0.

The workflow refuses a tag that does not match the pyproject.toml version, builds the sdist and wheel, smoke-installs the wheel, and uploads through the pypi environment.

Architecture

src/memkernel/
├── mcp/           # MCP server + tool definitions (memory tools, wiki tools)
├── core/          # Domain models (tiers, types), MemoryService, ports (interfaces)
├── adapters/      # Optional external systems (RLM Studio reasoner), imported lazily
├── storage/       # ChromaDB adapter, local document store
├── retrieval/     # AST/token chunking, embeddings, hybrid search (dense + BM25 + RRF + rescoring), local reasoner
├── memory/        # Lifecycle: TTL/decay policies, retention, deduplication
├── wiki/          # LLM-wiki bridge: page model, export, import
├── api/           # FastAPI admin endpoints
├── factory.py     # build_service() — the supported embedding entry point
├── lifecycle.py   # MemoryLifecycle — the session protocol as a component
└── config.py      # Pydantic settings

Ports-and-adapters design: core/ depends only on protocols in core/ports.py, never on concrete backends. Storage and embedding backends are swappable.

Further reading: docs/architecture/memory-tiers.md (design), docs/research/memory-literature-review.md (HRM, JEPA, MemGPT, Mem0, Zep, HippoRAG, Generative Agents, Titans, …), docs/research/sibling-projects.md (what is shared with rlm-studio, document-assistant, ragkit, code-copilot-team, haa), docs/adr/, docs/roadmap.md.

Related projects

MemKernel is the memory component of a small family of projects by the same author; each is usable on its own.

The cross-repository sequence is in docs/roadmap.md.

License

Apache-2.0. See LICENSE and NOTICE. Changes are tracked in CHANGELOG.md.