Metis

Amplify your experts’ judgement. Open-source tacit memory for AI agents, from Brightbeam Applied Research.

The illustrated version of this page is on GitHub, and the interactive walkthrough is at metis.brightbeam.works.

Website · Documentation · Paper · PyPI · CHAP

Metis is an open-source toolkit for capturing fragments of expert practice and making them available to AI agents as memory, with human review and agreed conditions for use.

Tacit fragments: a fourth layer of agent memory

A tacit fragment records what an expert noticed, how they responded, and the circumstances of that response. After human review, it sits alongside procedures, facts, and past events in the agent's memory.

Four layers of agent memory: procedural, what should happen; semantic, what is known; episodic, what happened; and tacit, what experience adds. The tacit layer comes from people.

The gap between procedure and practice

Procedures describe what should happen, and logs record what happened. The cue behind an expert's decision, and the reason for it, often go unrecorded.

Work as imagined, from SOP-17: reduce load only when the alarm threshold is crossed. Work as done, by an experienced operator on the night shift: ease back earlier, when high load meets a dull sound. Metis records what the expert noticed, the context, and the response, for people to review.

How a fragment reaches an agent

Three steps. Capture: the worker confirms the account in their own words. Human review: two named reviewers decide what the fragment may do. Use in context: the agent receives it when its conditions match. Three permission levels: Evidence, for reviewers only; Advisory, conditional support; Controlled, formal instruction.

Every capture, confirmation, review decision, and retrieval is recorded through the CHAP reference coordinator, chap-coordinator, on a hash-linked evidence chain.

The capture loop

When a recorded action differs from the procedure, a capture agent asks the expert one short question, a whisper, and the expert confirms the account in their own words.

The capture loop around the worker and the capture agent: observe, infer, whisper, confirm, store.

Seventeen kinds of know-how

Each fragment carries one of the paper's seventeen categories of tacit knowledge, K1 to K17. The atlas on the website gives an example of each and a way to capture it.

Seventeen kinds of know-how, K1 to K17, in six domains: procedural and embodied; material and equipment; perceptual; inferential; meta-cognitive; social and normative.

Quickstart: run the pump example

python -m pip install metis-memory
metis demo manufacturing-pump-vibration
metis fragment list
metis memory list
metis audit verify

The demo uses supplied observations, needs no model server, and keeps its records in ./.metis. To work from source:

git clone https://github.com/BrightbeamAI/metis && cd metis
pip install -e .

from metis import MetisEngine
from metis.conditions.context import TacitContext
from metis.consent.model import ConsentRecord, ConsentStatus

eng = MetisEngine()  # local and deterministic
eng.join_default_participants()

# Capture the operator's practice where it departs from the procedure.
frag = eng.capture_observation(
    {
        "observation_id": "OBS-1",
        "work_as_imagined": "Reduce load only when the alarm threshold is crossed.",
        "work_as_done": "Ease back earlier, when high load meets a dull sound.",
        "context": TacitContext(equipment_family="centrifugal_pump", operating_mode="high_load"),
    },
    consent=ConsentRecord(consent_status=ConsentStatus.granted),
    category="K7_sensory",
).fragment  # Evidence layer: reviewers only

# Two named reviewers promote it to Advisory.
eng.tier2_review(
    frag.fragment_id, "promoted_to_advisory", summary="advisory cue only",
    decided_by=["human:quality-lead@metis.local", "human:process-engineer@metis.local"],
)

# The gate returns it only where its conditions hold.
pump = TacitContext(equipment_family="centrifugal_pump", operating_mode="high_load", risk_class="moderate")
other = TacitContext(equipment_family="gear_pump", operating_mode="low_load", risk_class="moderate")
print(len(eng.retrieve(pump).eligible))       # 1
print(eng.retrieve(other).blocked[0].reason)  # conditions_do_not_match

Connect Metis to your application

metis mcp serves the same governed memory to MCP clients such as Claude Desktop and Claude Code. See the MCP server guide. To run Metis for a team, the server guide covers sign-in, workspace roles, the web app, and PostgreSQL; deploy/ runs it with Docker or Kubernetes; and the agent integrations guide connects agents through remote MCP, a Python client, or LangChain. Connectors capture from workplace systems and put whispers in Slack or Teams, and the operations guide covers running it in production.

Learn more

• Website: the interactive walkthrough, the atlas, and common questions.
• Documentation: architecture, governance, retrieval, and agent use.
• ABOUT.md: the repository map and how to develop.
• CHAP: the Collaborative Human-Agent Protocol.
• docs/demo.html and docs/explainer.html: an interactive demo and an illustrated explainer that open in any browser.

Ethical use

Metis captures fragments of human work with the worker's knowledge and consent. Do not use it for covert monitoring. It records no audio, video, biometrics, screenshots, or keystrokes. Production use needs worker consultation, legal review, and domain validation; read ETHICAL_USE.md first.

License

Apache-2.0. See LICENSE.

Citation

Metis is the reference implementation of Tacit Fragments: Operationalising Tacit Knowledge as a Governed Memory Layer for Agentic AI.

@article{shahid2026tacitfragments,
  title   = {Tacit Fragments: Operationalising Tacit Knowledge as a Governed Memory Layer for Agentic AI},
  author  = {Shahid, Arsalan and Suttie, Gordon and Black, Philip and Garz{\'o}n-Vico, Antonio},
  journal = {Preprints},
  year    = {2026},
  doi     = {10.20944/preprints202608.0927.v1},
  url     = {https://metis.brightbeam.works/resources/tacit-fragments-preprint.pdf}
}