headroom
★ 77.5k
vs
Zengram
★ 64
headroom vs Zengram
headroom: Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. It achieves the same answers with a fraction of the tokens.; Zengram: Zengram is a shared memory system for multi-agent AI systems, enabling agents to store and retrieve memories across machines. It features typed memory, dual storage (Qdrant + SQLite/Postgres), multi-path search, and cross-agent capabilities like briefings and corroboration. Self-hosted, open-source, with MCP server and SDKs.
01
TL;DR
headroomMemory & Context · ★ 77.5k
When to choose:
- Long tool output, logs, JSON responses or RAG chunks are driving up cost or hurting answers.
- You want compression without code changes: run it as a local proxy, an MCP server, or by wrapping Claude Code, Codex or Cursor.
- Compression must run locally, with originals cached so the model can retrieve the full text.
When to skip:
- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.
- You cannot add a component that must keep running and may add latency.
- Your tasks cannot tolerate lossy compression: verify answer quality on your own work first.
ZengramMemory & Context · ★ 64
Cross-agent memory sharing across different machines
02
Side-by-Side Comparison
Field
headroomZengramCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 64
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
git clone https://github.com/ZenSystemAI/zengram.git && cd zengram && cp .env.example .env && docker compose up -d
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
claude, claude-ai, claude-code
03
Features
headroom01In-app library for compression (Python/TypeScript)
02Zero-code-change proxy mode
03One-command agent wrapping for various AI agents
04Cross-agent shared memory and auto-deduplication
05Reversible compression (CCR) with original content retrieval
Zengram01Typed memory: events, facts, statuses, decisions
02Dual storage with semantic and structured search
03Multi-path search (vector, BM25, entity graph) with RRF fusion
04Cross-agent capabilities: briefings, corroboration, credential scrubbing
05Self-hosted, open-source, with MCP server and SDKs
04
Use Cases
headroom↳Reduce LLM token usage and API costs for AI agents.
↳Enable shared context and memory across multiple AI agents.
↳Optimize coding agents by compressing tool outputs, logs, and RAG chunks.
↳Maintain full data fidelity with reversible context compression.
Zengram↳Cross-agent memory sharing across different machines
↳Session reflection and context retrieval for agents
↳Logging memories from automation tools (e.g., n8n) for AI agents
05
Best For
headroomMost PopularEssential
Zengram—
FAQ
FAQ
What is the difference between headroom and Zengram?
Both headroom and Zengram are in the Memory & Context category. headroom has 77.5k stars, while Zengram has 64 stars.
Which is better, headroom or Zengram?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and Zengram if Cross-agent memory sharing across different machines.
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is Zengram free or open source?
Yes, Zengram is open source on GitHub (MIT).
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