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lean-ctx Comparisons
How does lean-ctx compare to other context and memory tools for AI agents?
We believe in transparent, fact-based comparisons. Every page below includes real feature data, honest assessments of competitor strengths, and guidance on when each tool is the right choice.
Quick Comparison Matrix
| lean-ctx | Repomix | codebase-memory | claude-context | Aider repo-map | Mem0 | |
|---|---|---|---|---|---|---|
| Stars | 2.9k+ | 25k+ | 3k+ | 11.5k+ | 43k+ | 55k+ |
| MCP Tools | 79 | 8 | 14 | 3 | 0 | 9 |
| Read Modes | 10 | 0 | 0 | 0 | 0 | 0 |
| Token Compression | 99% | ~70% | 99%+ | ~40% | N/A | N/A |
| Shell Compression | 95+ | — | — | — | — | — |
| PageRank Repo-Map | MCP | — | — | — | CLI only | — |
| Call Graph | Yes | — | Yes | — | — | — |
| Semantic Search | Hybrid | — | Yes | Yes | — | Yes |
| Session Memory | Yes | — | — | — | — | Yes |
| Knowledge Graph | Temporal | — | Yes | — | — | Yes |
| Multi-Agent | Yes | — | — | — | — | Yes |
| 100% Local | Yes | Yes | Yes | No | Yes | No |
| Single Binary | Rust | Node.js | C | Node.js | Python | Python |
| Agents Supported | 31 | Any MCP | 11 | 2-3 | 1 (Aider) | Any MCP |
| Stability Contract | 29 frozen/stable contracts, CI-enforced | — | — | — | — | — |
Which Tool Should I Use?
"I want to pack my repo and paste it into ChatGPT"
Use Repomix. It's the simplest, most popular tool for one-shot codebase packing. npx repomix and you're done.
"I need deep structural code intelligence (call paths, dead code, architecture)"
Use codebase-memory. It's the fastest code indexer with 155 language support and sub-millisecond graph queries. Consider lean-ctx if you also need compression and session memory.
"I need semantic code search for Claude Code"
Use lean-ctx if you want local-first operation. Use claude-context if you want cloud-scale embedding models. Both provide hybrid BM25 + vector search.
"I want PageRank-based repo maps"
Use Aider if you want a complete AI coding assistant. Use lean-ctx if you want repo-maps in Cursor, Claude Code, or other MCP-compatible agents.
"I need cross-session memory for my AI agents"
Use Mem0 for general-purpose AI memory (chatbots, assistants, customer support). Use lean-ctx for code-specific memory with compression and structural intelligence.
"I want to compress free-form prose / chat history / RAG context"
Use The Token Company for cloud ML prose compression. Use lean-ctx when the content is code or tool output, you need 100% local operation, or you need deterministic, prompt-cache-preserving output.
"I want a drop-in compress(messages) library like Headroom"
Use Headroom for ML prose compression and the widest set of framework wrappers. Use lean-ctx when you need deterministic, prompt-cache-safe output, 100% local operation in a single binary, or compression alongside cached reads, search and memory.
"I was going to build plain vector-DB RAG over my codebase"
Use lean-ctx for coding agents — it combines compress-into-window with a hybrid retriever (BM25 + dense + RRF + rerank) and is structure-aware (tree-sitter AST + code graph), where naive top-k vector search is not. Use a dedicated vector DB when the corpus is huge and unstructured (support tickets, web pages, PDFs) with no structure to exploit.
"I want all of the above in one tool"
Use lean-ctx. It's the only tool that combines compression, memory, code intelligence, semantic search, repo-maps, and observability in a single binary.
Detailed Comparisons
| Comparison | Key Distinction | Read More |
|---|---|---|
| lean-ctx vs Repomix | Live context layer vs snapshot packer — 99% vs 70% compression | Full comparison → |
| lean-ctx vs codebase-memory | Broad context layer vs deep code intelligence — 80 tools vs 14 | Full comparison → |
| lean-ctx vs claude-context | 100% local vs cloud-dependent — 80 tools vs 3 | Full comparison → |
| lean-ctx vs Aider repo-map | MCP-available vs CLI-locked — PageRank for 31 agents | Full comparison → |
| lean-ctx vs Mem0 | Code-specific vs general-purpose — local vs cloud | Full comparison → |
| lean-ctx vs The Token Company | Local deterministic code compression vs cloud ML prose compression | Full comparison → |
| lean-ctx vs Headroom | Deterministic, prompt-cache-safe compress() + full context layer vs ML compression library |
Full comparison → |
| lean-ctx vs naive RAG | Two-halves pipeline + structure-aware hybrid retrieval vs top-k vector search | Full comparison → |
What Makes lean-ctx Different
lean-ctx is the only tool that covers all three layers of AI agent context:
Layer 1: Compression
10 file read modes, 95+ shell compression patterns, cached re-reads (~13 tokens). Every interaction uses fewer tokens.
Layer 2: Memory
Temporal knowledge graph, session persistence, episodic memory. Context survives across chats and sessions.
Layer 3: Intelligence
PageRank repo-maps, call graphs, blast radius analysis, hybrid semantic search. The agent understands your code structurally.
No other tool in this space covers all three layers. Most focus on one: Repomix on compression, Mem0 on memory, Aider on intelligence.
And one guarantee none of them make: stability
Since v1.0, every lean-ctx surface is governed by a published stability policy
(CONTRACTS.md): 29 protocol contracts classified
frozen/stable/experimental, frozen surfaces SHA-256-locked in CI, and a public
/v1 API that can only grow. Integrations built on lean-ctx cannot silently
break — a claim no other tool in this matrix makes.
Where this is heading: a temporal axis
The three layers don't just stack — they're composing into a navigable history. lean-ctx already records why the model saw each item (Context Ledger), signs what it saw (Context Proof), and persists memory across runs, so that state can be git-anchored into a Context Snapshot you rewind, reproduce, resume, or share. This is a published direction, not yet a shipped feature — see the Context Time Machine concept.
Our Approach to Comparisons
- Factual and data-driven: real feature counts, real star counts, real capabilities
- Honest about competitor strengths: every comparison page includes a "where they lead" section
- Updated regularly: star counts and feature sets are verified against latest releases
- No FUD: we don't exaggerate weaknesses or minimize competitor accomplishments
- Try both: every page includes links to the competitor's GitHub and docs
Last updated: June 2026. Star counts and features reflect latest public releases; the lean-ctx tool count is generated from the registry (docs/reference/generated/mcp-tools.md).