Klea Agent¶

The Klea agent is Klea’s general-purpose agentic assistant, built on the shared BaseLangGraph orchestrator (see Klea Utils). It plans and carries out research tasks – literature review, hypothesis generation, planning, coding, pipeline execution and analysis – using the tools supplied by its configured MCP servers (see MCP servers).

Note

klea_agent is under active development and is not released yet. Some paths described here are specified but not fully wired; this page says so explicitly wherever that is the case.

Operating modes¶

The agent runs in one of two modes, decided at task entry (ADR-0030):

General: The default. Answers and tasks are handled with the configured models and tools.
Scientific: Intended for grounded, source-backed research answers over a curated knowledge source. Not yet runnable: the agent has no curated knowledge source wired in (retrieval is deferred to the ADR-0029 phase), so a Scientific request is declined with an explanation rather than silently answered without grounding.

A requested mode that cannot run is reported back to the user, and the effective mode is surfaced to the web UI through the graph’s context event (see Web interface).

How a task runs¶

The task path is a LangGraph state machine. In outline:

Route – a narrow entry router classifies each request as chat (answered inline) or task (handed to the planner). Trivial chat takes this short path; everything else is planned.
Plan – the Planner writes the goal and an ordered plan, and never answers the user directly.
Review – the plan can pause for human review. The review step currently auto-approves (a canned approval); real interactivity (LangGraph interrupt/resume) is pending.
Work loop – for each step the tools picker selects tools, the tool caller dispatches them (in parallel), and a deterministic triage router plus an operational evaluator decide whether to retry the step, replan, or move on. If the picker finds no suitable tool, the plan is revised rather than looping.
Answer – once the plan is done, the answer is composed from the step results and delivered.

Memory¶

Conversation history is summarised per session, so long-running chats stay within the model’s context window.

Tool access¶

The agent defaults to the full tool access level, and the level can be changed per chat in the web UI’s status pane. Under read_only only tools annotated read-only are offered. See MCP servers for the access model and its sandboxing caveats; environment and coding requests are answerable in full mode through the bundled run_command tool.

Status¶

The agent is unreleased. Notably still in progress:

the Scientific mode knowledge source (retrieval, ADR-0029);
grounding/assurance enforcement – answers are labelled unverified until this lands;
interactive human plan review (currently auto-approving).