Practical overview
When it fits
Use Distill when building an agent workflow that needs persistent context, deduplication or retrieval processing, or when preparing a reproducible context artifact from selected files.
Who it is for
- Developers building LLM agents that need memory to persist across sessions.
- Teams that want to reduce duplicate context or surface conflicting stored information.
- Operators seeking a self-hosted API or CLI for context processing and retrieval.
Documented uses
- Retain agent memories across sessions with write-time deduplication, expiry and sensitivity tags.
- Deduplicate chunks before they reach an LLM, including through the standalone API.
- Connect retrieval to Pinecone or Qdrant and apply deduplication to retrieved context.
- Use MCP mode with compatible assistants, with options for memory and sessions.
- Prepare a portable context artifact from a reviewed file inventory and verify it offline.
Requirements & limits
The repository is public and identifies Distill as MIT-licensed. Documented installation options include Go install, Docker and building from source.
- Text-to-embedding conversion requires an OpenAI API key; precomputed embeddings are documented as an alternative.
- The retrieval example uses Pinecone or Qdrant and requires the relevant backend configuration.
- The README says a self-hosted API is open when DISTILL_API_KEYS is unset and documents that variable for authentication.
- The repository describes core processing as deterministic, with about 12 ms overhead and no LLM calls.
- Distill Lock is a separate file-based workflow; its lock, build and verify stages are documented as not requiring a model, provider or network call.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run standalone chunk deduplication with the API
Start Distill’s API and submit text chunks for deduplication.
Before you start
- A Distill installation using one of the documented installation options.
- An OpenAI API key for converting text to embeddings, or precomputed embeddings supplied with the chunks.
Steps
Set OPENAI_API_KEY when using Distill to create embeddings from text.
Start the server with
distill api --port 8080.Send a JSON POST request containing chunks to
http://localhost:8080/v1/dedupe; include precomputed embeddings if no OpenAI key is used.
Expected outcome
The documented response returns retained chunks with cluster IDs and statistics including input count, output count, reduction percentage and latency.
Guide limitations
- The README’s OpenAI-key requirement applies to text-to-embedding conversion; it documents precomputed embeddings as an alternative.
- The example runs against localhost and does not establish deployment or security configuration for a public server.
Build and verify a Distill Lock context artifact
Create and check a portable context artifact from a reviewed file inventory.
Before you start
- Distill CLI.
- A configuration that explicitly lists selected and excluded UTF-8 text or code files.
Steps
Run
distill lock path/to/config.json --output path/to/context.lock.json.Build the artifact with
distill build path/to/context.lock.json --output path/to/context-output.Run
distill verify path/to/context-output.
Expected outcome
The documented workflow produces a portable context artifact and checks it offline; verification can also compare against a trusted lock digest when supplied.
Guide limitations
- The build refuses missing, changed, newly unexpected, unsafe or unsupported inputs rather than relocking automatically.
- Without an optional out-of-band lock digest, verification proves internal consistency, not trust in the original lock.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- Siddhant-K-code/distill · Official homepage title · retained in its source language