elja

Elja (Icelandic): relentless drive — the quality of working at something without letting up.

A fully-customizable LLM agent harness built on Pydantic AI, designed for local-first models (LM Studio / OpenAI-compatible endpoints) with first-class support for tools, skills, sub-agents, MCP servers, and custom context management.

Status: under active development.

Installation

pip install elja

Usage

Start LM Studio serving a model (default expectation: qwen/qwen3.8-27b at http://localhost:1234/v1), then:

elja chat                      # interactive REPL (session persisted + resumed)
elja chat --once "list the files here and summarize them"
elja chat --once "what is in this screenshot?" --image shot.png
elja chat --config path/to/elja.toml --session mychat
# In the REPL, attach an image to a message with: /img <path> <prompt>

Or from Python:

from elja import EljaDeps, build_agent, build_usage_limits, load_settings

settings = load_settings()
agent = build_agent(settings)
result = agent.run_sync(
    "What's in this directory?",
    deps=EljaDeps.from_settings(settings),
    usage_limits=build_usage_limits(settings),
)
print(result.output)

Configuration lives in elja.toml (all keys optional; ELJA_* env vars override, e.g. ELJA_MODEL__BASE_URL):

[model]
provider = "openai"      # "openai" (any OpenAI-compatible endpoint — the default,
                         # aimed at LM Studio), "anthropic", or "google".
                         # Native providers need: pip install 'elja[anthropic]' / 'elja[google]'
name = "qwen/qwen3.8-27b"
base_url = "http://localhost:1234/v1"   # unset with provider="openai" = local LM Studio
temperature = 0.2
# api_key: set for cloud endpoints; native providers also honor
# ANTHROPIC_API_KEY / GOOGLE_API_KEY from the environment.

[limits]
request_limit = 25

[workspace]
root = "."

[tools]
run_shell = true
web_search = true  # keyless DuckDuckGo search via ddgs (network egress!)

[agent]
instructions = "Optional: replace the default system instructions."

[compaction]           # evidence-based tiered compaction (see elja/compaction.py)
                       # NB: 24000 is tuned for a local 27B; raise it for large-window
                       # cloud providers to avoid early masking + paid summarizer calls
enabled = true
target_tokens = 24000  # load the LM Studio model with at least this + headroom
keep_tool_pairs = 10   # recent tool results kept verbatim by the masking tier
keep_messages = 20     # verbatim tail if the summarization fallback fires

# Skills: markdown files in <workspace>/skills/ (or [skills] dir = "...") with
# YAML frontmatter (id, description) + an instructions body. The model loads
# them on demand, so a large skill library costs ~no context until used.

# Sub-agents: delegate tools with isolated context (results-only return).
[subagents.researcher]
description = "Researches a question and reports key facts."
instructions = "Answer tersely with sources."
tools = ["read_file", "web_search"]  # subset of ENABLED built-ins; omit for all enabled
request_limit = 8                     # per-delegation request budget (optional)

# Attach MCP servers; their tools become available to the agent.
[mcp.servers.mytools]
command = "python3"         # stdio: launched as a subprocess
args = ["my_mcp_server.py"]
env = { API_KEY = "..." }   # optional; NB the subprocess gets a minimal env
                            # (HOME/PATH/USER...) plus these — not your full shell env

[mcp.servers.remote]
transport = "http"          # streamable-HTTP endpoint
url = "http://localhost:9000/mcp"  # unauthenticated — localhost only

Development

poetry install --with dev
poetry run pre-commit install

Run checks:

poetry run ruff check src tests
poetry run mypy
poetry run pytest -m "not integration"

Integration tests (-m integration) require a running LM Studio server at http://localhost:1234/v1.

License

MIT