lerim 0.1.81

Continual learning layer for coding agents and software projects.

Durable context for coding agents.

Turn coding-agent sessions into reusable context records. Capture decisions, constraints, preferences, and evidence so the next agent starts with precedent, not guesswork.

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Works with Claude Code, Codex CLI, Cursor, and OpenCode.

Lerim

Lerim is a local-first context runtime for coding agents.

It watches agent sessions, extracts the durable parts, and stores them in one shared context layer that every future agent can query.

Instead of losing the reasoning after each session, Lerim keeps:

• decisions
• constraints
• preferences
• reference facts
• evidence linked back to the source session

Why Lerim

Coding agents are fast, but they forget.

Without a durable context layer:

• decisions get re-debated
• constraints get rediscovered
• preferences get ignored
• every new session starts too close to zero

Lerim fixes that by turning raw traces into reusable context records and making them queryable from the CLI, Lerim Cloud, and agent tools.

Key Capabilities

• Local-first storage. Durable context lives in one global SQLite database at ~/.lerim/context.sqlite3.
• Shared across agents. What Claude Code learns can be reused by Codex, Cursor, or another supported agent later.
• Background maintenance. sync ingests sessions, maintain consolidates overlap and archives stale records, ask retrieves relevant precedent.
• Hybrid retrieval. Lerim combines local ONNX embeddings stored through sqlite-vec with SQLite FTS5 and RRF fusion.
• Clean agent tool surface. The runtime exposes semantic DB-era tools like list_context, search_context, get_context, save_context, revise_context, and count_context instead of file CRUD.

Quick Start

Install Lerim:

pip install lerim

Initialize and register the current repo:

lerim init
lerim connect auto
lerim project add .

Start the service:

lerim up

Check status:

lerim status
lerim status --live

Ask a question:

lerim ask "What do we already know about the auth flow?"

What the Commands Do

lerim up

Starts Lerim in the background so it can watch your workflow and process context jobs.

lerim status

Shows service health and current status.

lerim status --live

Shows live status updates. This is useful for demos and for watching background extraction happen.

lerim sync

Indexes sessions and extracts durable context from recent work. When Lerim is running in the background, sync work is scheduled from your configured intervals.

lerim maintain

Improves context quality over time by merging duplicates, archiving weak records, and refreshing useful context. Background maintenance is also driven by configured intervals.

lerim ask

Lets you ask questions against accumulated project context.

lerim ask "Why did we choose SQLite for local metadata?"

Configuration

lerim init creates the default local configuration. You can override settings in:

~/.lerim/config.toml

API keys are read from environment variables, stored by default in:

~/.lerim/.env

Example .env:

MINIMAX_API_KEY=your-key
OPENROUTER_API_KEY=your-key
OPENAI_API_KEY=your-key
ZAI_API_KEY=your-key

Example provider config:

[roles.agent]
provider = "minimax"
model = "MiniMax-M2.7"
fallback_models = ["zai:glm-4.7"]

How It Works

Lerim has three main flows:

1. sync Reads new traces/session metadata and extracts durable context records.

2. maintain Refines existing records by merging overlap and retiring low-value stale records.

3. ask Retrieves relevant records and answers a question using the current context layer.

In practice, this means Lerim becomes the shared precedent store behind your agent workflows.

Semantic retrieval is local:

• ONNX model: mixedbread-ai/mxbai-embed-xsmall-v1
• vector storage: sqlite-vec
• lexical retrieval: SQLite FTS5
• fusion: RRF

Storage Model

Global Lerim state lives under ~/.lerim/:

• context.sqlite3 — canonical durable context store
• index/sessions.sqlite3 — session catalog and queue
• workspace/ — sync and maintain run artifacts
• cache/traces/ — compacted agent trace cache
• models/embeddings/ — local embedding model cache
• models/huggingface/ — Hugging Face library cache
• config.toml — user config
• platforms.json — connected platform paths
• logs/YYYY/MM/DD/ — dated runtime logs (lerim.log, lerim.jsonl, and activity.log)

Project registration only stores host paths in config. Project separation happens inside the database by project_id.

There is no per-project durable store on disk.

Agent Tools

The agent-facing tool contract is intentionally small:

• read_trace
• list_context
• search_context
• get_context
• save_context
• revise_context
• archive_context
• supersede_context
• count_context
• note_trace_findings
• prune_trace_reads

These are the authoritative runtime tool names. Keeping the surface DB-era and semantic makes the runtime easier to reason about and gives smaller future models a cleaner action space for training.

Common Commands

lerim status
lerim status --live
lerim logs --follow
lerim queue
lerim queue --failed
lerim sync
lerim maintain
lerim ask "What decisions exist about caching?"

Setup and management:

lerim connect auto
lerim project list
lerim project remove <name>
lerim skill install

Alternative to the background service:

lerim serve

Development

uv venv && source .venv/bin/activate
uv pip install -e '.[test]'
tests/run_tests.sh unit
tests/run_tests.sh smoke
tests/run_tests.sh integration
tests/run_tests.sh e2e

Before release, verify the affected path with the relevant suites:

• tests/smoke/ — quick real-LLM extract sanity
• tests/integration/ — real extract, maintain, and semantic ask coverage
• tests/e2e/ — full runtime-cycle checks over sync, maintain, and ask

Start here if you want to read the codebase:

• src/lerim/README.md
• src/lerim/skills/cli-reference.md
• docs/concepts/how-it-works.md
• docs/concepts/context-model.md

Contributing

Contributions are welcome.

Good starting points include:

• session adapters and adding more agents
• extraction quality
• context maintenance quality
• docs and demo examples

Helpful links:

• Contributing Guide
• Open issues
• Agent adapter examples: src/lerim/adapters/