Distill

Memory framework

First committed by Siddhant-K-code

Published in Siddhant-K-code/distill. Publisher page

198 GitHub starsUpdated 2026-09-30

What it is

Context intelligence layer for LLM agents: persistent memory with write-time dedup, sensitivity tagging, conflict detection, and hierarchical decay. ~12ms. No LLM calls. MIT.

— the description by Siddhant-K-code

AutoGineer overview, from the brain’s own files

Distill is a framework designed to serve as a context intelligence layer for LLM agents. It provides persistent memory capabilities, allowing agents to maintain context across sessions. The system includes features for deduplication, sensitivity tagging, conflict detection, and hierarchical decay of information. It is designed to integrate with various environments, including Claude Desktop and Cursor, via an MCP server, and provides REST API endpoints for broader agentic applications.

How it works

Memory model: Hybrid

AutoGineer overview, from the brain’s own files

Distill functions as a memory layer that manages agent context through a series of internal packages. It handles data ingestion, deduplication, and embedding to organize information. The system uses a memory model that supports persistent storage and retrieval, allowing agents to access relevant context. It includes tools for session management, token-budgeted context windows, and handoff capabilities for decision-to-documentation workflows.

Folders
Folder	Role	Files
.github	agent-config	5
docs	docs	17
examples	examples	4
pkg	source-code	115
scripts	scripts	5
tools	scripts	2
cmd	folder	34
grafana	folder	1
internal	folder	119
mcp	folder	4
research	folder	105
testdata	folder	27

Requirements

Apps: Docker

Runtimes: Go

Databases: Pinecone, Qdrant, Redis, SQLite, Weaviate

API keys (by name): COHERE_API_KEY, DISTILL_API_KEY, OPENAI_API_KEY, PINECONE_API_KEY

Works with: Claude apps

Set it up

This brain runs code automatically: 1 script. Review Makefile before opening the folder in Claude Code. No step below turns a hook on for you.
- Makefile: $(GO) install $(GOFLAGS) -ldflags "$(LDFLAGS)" .
Not scanned by AutoGineerThe author’s MCP configuration (README.md) also starts it in a way AutoGineer does not offer. Read that file: AutoGineer did not scan that path; what it runs is listed under “This brain runs”. https://github.com/Siddhant-K-code/distill/blob/2417e00dabe498eb8187f4544f001d5a49fbdb45/README.md
Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/Siddhant-K-code/distill/blob/2417e00dabe498eb8187f4544f001d5a49fbdb45/README.md#go-install
Not scanned by AutoGineerThe author’s README also describes installing it with go install. Read its install section: AutoGineer did not scan that path. https://github.com/Siddhant-K-code/distill/blob/2417e00dabe498eb8187f4544f001d5a49fbdb45/README.md#go-install
First committed by Siddhant-K-code. Published in Siddhant-K-code/distill. Licence: MIT License.

Usage

AutoGineer overview, from the brain’s own files

Integrating persistent memory into LLM agent workflows.
Managing context windows across multiple agent sessions.
Implementing deduplication and sensitivity tagging for agent data.
Using the MCP server to connect agent memory to desktop clients.
Automating decision-to-documentation bundles via handoff capabilities.

“Retrieve the context from the last session regarding the project architecture.”: The agent accesses the persistent memory layer to provide the relevant architectural details stored in the previous session.
“Summarize the recent decisions made in the current development sprint.”: The agent uses the handoff capability to bundle and present the documented decisions stored within the system.