Distill
Persistent memory, semantic dedup, and context compression for LLM agents. ~12ms, no LLM calls.
Distill is a well-engineered, deterministic alternative to ad-hoc context trimming. Its 12ms pipeline and write-time dedup tackle real production pain points without LLM overhead. For teams already using agent frameworks, this is a literal drop-in improvement.
- Developers building production LLM agent systems
- Teams needing persistent memory across agent sessions
- Projects experiencing context bloat and token waste
- Developers wanting deterministic, auditable context preprocessing
- Users without technical expertise (requires self-hosting or API setup)
- Projects needing real-time human-in-the-loop conflict resolution
- Teams that cannot run embeddings (requires OpenAI or Ollama key)
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In short
Distill — Persistent memory, semantic dedup, and context compression for LLM agents. ~12ms, no LLM calls. Best for Developers building production LLM agent systems, Teams needing persistent memory across agent sessions, Projects experiencing context bloat and token waste. Free to use.
Viability Score
How likely is Distill to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Persistent memory across agent sessions
- Semantic deduplication with cosine distance threshold (0.15 default)
- Conflict detection between 0.15 and 0.35 cosine distance
- Sensitivity tagging: PII, credentials, internal references
- Hierarchical decay: full text → summary → keywords → evicted
- Write-time dedup: merges similar entries, not duplicates
- Supersession: replace outdated memories with audit trail
- Task-relevance ranking with tag boosting and min relevance filter
- Six-stage pipeline: cache, cluster, select, compress, MMR, summarize
- Extractive compression removes noise, preserves signal
- Maximal Marginal Relevance (MMR) for diversity + relevance
- Session management with configurable token budget (default 128K)
- Auto-compression and importance-based eviction per session
- Deterministic processing: same input → same output
- OpenAPI 3.1 spec with interactive Swagger UI docs
About Distill
Distill is an open-source context intelligence layer that gives LLM agents persistent memory across sessions, semantic deduplication, and context compression. It sits between your agent's sources (RAG chunks, tool outputs, memories, docs, conversation) and the LLM, processing input through a deterministic six-stage pipeline: cache, cluster, select, compress, MMR, and summarize. The result is a cleaned, deduplicated, and compressed context that reduces token waste by 30-40% while improving reliability. Designed for developers building production-grade agentic systems, Distill addresses the problem of redundant context breaking reliability and causing non-deterministic outputs. Its onboard memory system supports write-time dedup (cosine distance <0.15), conflict detection (0.15-0.35), sensitivity tagging (PII, credentials), and hierarchical decay (full text → summary → keywords → evicted). No LLM calls are made during processing; it uses embeddings (OpenAI or Ollama) for similarity operations. Distill can be run as a standalone API server, integrated via MCP for Claude Desktop/Cursor, or embedded via its Go binary. The pipeline is fully auditable, with JSON output showing which chunks were clustered, selected, compressed, or summarized. With the 'sessions' feature, you can set token budgets (e.g., 128K tokens) and automatically compress/evict aging entries as the agent works. What makes Distill different: it treats deduplication as a correctness measure, not just optimization. It is deterministic (same input → same output), operates in ~12ms total overhead, and requires no vector database for basic use (though Pinecone and Qdrant are supported). The project is MIT-licensed and already in use in production agent workflows.
Behind the Verdict
Distill solves a real problem that most agent builders ignore until it bites them: context bloat and redundancy. We've seen teams spend weeks tuning prompts only to have their agent break because a duplicate chunk overwrote a critical constraint. Distill's semantic dedup and hierarchical memory prevent that deterministically, without an LLM call. That 12ms overhead is practically invisible. When to pick this: You're building a production agent that runs across multiple sessions or processes large volumes of RAG chunks. Distill shines when you need consistent behavior—same input, same output—and want to avoid the non-determinism of LLM-based summarization. It's also great for teams that want an auditable pipeline you can inspect and debug. When to pass: You're a solo developer experimenting with agents and don't yet have context bloat—the setup overhead (running a server, generating embeddings) isn't worth it. Also, if you need a fully managed cloud service with a GUI, Distill is self-hosted (though a hosted version may come later). Compared to alternatives: LangChain's memory is heavier and not deterministic. Mem0 is similar but closed-source. Distill's advantage is the open-source MIT license and the focus on deterministic dedup as a correctness measure. We'd reach for Distill when reproducibility matters more than ease of setup. Real-world usage caveats: You need an embedding provider (OpenAI or Ollama). The default 128K token budget works well, but you'll want to tune it for your specific context lengths. The tool is young (v0.9.1), so expect breaking changes. The Python SDK is in progress, so Go developers are better served today. Overall, if you're already using MCP, this integrates in minutes.
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Use Cases
- Save a JWT authentication approach as a memory and recall it in later sessions without re-explaining.
- Deduplicate overlapping RAG chunks before sending to an LLM to reduce token consumption by 30%.
- Create a session for a coding agent that automatically compresses old constraints when the token budget is exceeded.
- Flag sensitive credentials in memory with PII detection and prevent them from being surfaced in agent context.
- Supersede outdated project configuration memories while keeping an audit trail of previous versions.
- Use MCP tools to store agent preferences from Claude Desktop and recall them in Cursor without code changes.
Models Under the Hood
as of 2026-07-17
Limitations
- Requires an OpenAI API key for embeddings even for local operation.
- Pipeline's O(n²) distance matrix may become slower with very large chunk counts (though benchmark shows <2ms for 50 chunks).
- Self-hosted only; no cloud-hosted SaaS tier.
- Session memory default 128K token budget (configurable).
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
Official links
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Common stack mates teams adopt alongside Distill, with the specific reason each pairing earns its keep.
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