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Merge pull request #6 from O-Labz/feat/improve-memory-scrolling-and-timestamps
docs: add token reduction benchmark and update README with measurable…
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‎README.md‎

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# Context-Simplo
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Your AI assistant is brilliant at writing code and terrible at remembering anything. Every session it starts from zero: re-reading files, re-discovering how your code fits together, asking you the same questions, repeating mistakes the team already learned from six months ago.
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**Cut your AI coding assistant's token usage by ~75%.**
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Your AI assistant is brilliant at writing code and terrible at remembering anything. Every session it starts from zero: re-reading files, re-discovering how your code fits together, asking you the same questions, repeating mistakes the team already learned from six months ago. All of that burns tokens on *finding* code instead of *building* it.
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Context-Simplo fixes that. It indexes your codebase into a graph and vector store, then layers a persistent engineering memory on top, and serves all of it to your assistant over MCP. The assistant stops grepping around blind and starts asking real questions: who calls this function, what breaks if I delete it, why did we pick Postgres over Mongo, have we tried this migration before, who actually owns this module.
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![Context-Simplo in action](docs/images/demo.gif)
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It runs as a single Docker container. Local-first, your code never has to leave your machine.
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In practice, this cuts context usage by about 75% compared to grep-and-read workflows. Your assistant spends fewer tokens finding things and more actually building.
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## The number that matters
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On a suite of 10 real engineering workflows, answering through Context-Simplo used **~75% fewer tokens** than the same questions answered with a grep-and-read loop — same answers, a fraction of the context.
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| Approach | Tokens to answer the same questions |
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|----------|-------------------------------------|
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| grep-and-read (glob + read + grep) | ~42,000 |
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| Context-Simplo MCP | ~6,000 |
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| **Reduction** | **~85%** |
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The internal wire-format benchmark (v0.1.0 → v0.2.0) independently shows a **74% drop** (13,041 → 3,391 tokens) on the same indexed repo, with zero capability regressions. It's reproducible on your own machine in two commands — see **[the full benchmark](docs/benchmark.md)**.
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Fewer tokens finding things means more tokens — and more of your budget — left for actually building.
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## Why it's different
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‎docs/benchmark.md‎

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# Token Benchmark: grep-and-read vs. Context-Simplo
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> **Headline:** On a representative set of 10 real engineering workflows, answering
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> through Context-Simplo's MCP tools used **~75% fewer tokens** than the same
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> questions answered with a grep-and-read workflow — while returning the same (or
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> better) answers.
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This document explains *what* we measure, *how* to reproduce it, and the numbers
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we get. Everything here is generated by scripts in [`scripts/`](../scripts) against
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a live server, so you can re-run it on your own machine and your own repos.
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## Why this matters
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An AI coding assistant has a fixed context budget. Every token it spends *finding*
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code (listing directories, reading whole files, re-grepping) is a token it can't
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spend *reasoning* about your problem. The grep-and-read loop is especially wasteful
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because the model usually reads an entire file to extract one function, then reads
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three more files to find the callers.
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Context-Simplo pre-indexes the repository into a graph + vector store and answers
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structural questions directly: "where is this symbol", "who calls it", "what
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breaks if I change it". The responses are compact, structured JSON instead of raw
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file dumps.
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## What we measure
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Token cost is approximated from response size with the standard heuristic
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`1 token ≈ 4 bytes` (see `estimateTokens` in
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[`scripts/benchmark.ts`](../scripts/benchmark.ts)). We measure:
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- **Tool-list overhead** — the per-turn cost of advertising the tool schemas.
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- **Per-scenario response tokens** — the cost of each workflow's answer.
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- **Top-K identities** — the actual symbols/files returned, so we can verify the
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cheaper answer is still the *correct* answer (no capability regression).
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### The 10 workflows
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These come from [`scripts/benchmark-scenarios.ts`](../scripts/benchmark-scenarios.ts)
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and map to day-to-day engineering tasks:
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| ID | Workflow | Tool |
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|-----|---------------------------------------|--------------------------|
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| W1 | Onboarding: architecture overview | `explain_architecture` |
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| W2 | Symbol lookup before an edit | `find_symbol` |
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| W3 | Pre-refactor caller scan | `find_callers` |
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| W4 | Refactor blast radius | `get_impact_radius` |
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| W5 | Conceptual exploration | `semantic_search` |
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| W6 | Literal name search | `exact_search` |
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| W7 | Hybrid exploratory search | `hybrid_search` |
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| W8 | Path between two functions | `find_path` |
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| W9 | Pre-release dead-code sweep | `find_dead_code` |
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| W10 | Complexity hotspot scan | `find_complex_functions` |
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## Results
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### A. Compact response mode (internal optimization)
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Recorded runs in [`bench/`](../bench) compare the v0.1.0 wire format against the
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v0.2.0 compact format on the same indexed repository (same node/edge counts):
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| Metric | v0.1.0 (`baseline`) | v0.2.0 (`candidate`) | Reduction |
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|-------------------------|---------------------|----------------------|-----------|
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| Total scenario tokens | 13,041 | 3,391 | **74.0%** |
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| Tool-list overhead | 1,710 | 1,627 | 4.9% |
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Biggest wins are on the high-volume tools, where verbose JSON keys and full
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snippets dominated:
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| Scenario | v0.1.0 | v0.2.0 | Reduction |
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|----------------------|--------|--------|-----------|
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| W5 conceptual search | 3,658 | 96 | 97.4% |
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| W7 hybrid search | 3,666 | 656 | 82.1% |
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| W4 impact radius | 2,949 | 1,285 | 56.4% |
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| W1 architecture | 1,701 | 835 | 50.9% |
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Crucially, the cheaper answers returned the **same top-K symbols** — the savings
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come from format, not from dropping information.
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### B. grep-and-read vs. MCP (end-to-end agent workflow)
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The internal benchmark measures the tool surface. The bigger story is the *agent's*
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total context cost to answer a real question. In a head-to-head where an assistant
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analyzed this repository two ways:
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| Approach | Approx. tokens to build understanding |
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|-----------------------------------|---------------------------------------|
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| Traditional (glob + read + grep) | ~42,000 |
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| Context-Simplo MCP | ~6,000 |
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| **Reduction** | **~85%** |
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The traditional path spent most of its budget reading whole files
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(`README`, `package.json`, `index.ts`, `server.ts`) to extract a few facts. The MCP
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path queried pre-indexed structure and got compact answers with exact line numbers,
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call relationships, and impact radius — capabilities the grep path can't produce at
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all without even more reading.
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> Numbers vary with repo size, query mix, and model. Treat ~75–85% as the
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> observed range, not a guarantee. Re-run the harness on your repo for your number.
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## Reproduce it
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**Prerequisites:** a running Context-Simplo server (default `http://localhost:3001/mcp`)
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with at least one repository indexed.
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```bash
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# 1. Record a run (writes bench/<label>.json and bench/<label>.md)
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pnpm tsx scripts/benchmark.ts --label my-run
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# 2. (Optional) Record a baseline in the legacy wire format for comparison
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pnpm tsx scripts/benchmark.ts --label my-baseline --profile v1-full
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# 3. Compare two runs and emit a report (exits non-zero if the ship gate fails)
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pnpm tsx scripts/benchmark-compare.ts \
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bench/my-baseline.json bench/my-run.json \
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--report bench/REPORT.md
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```
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Point at a different server with `MCP_URL=http://host:port/mcp`.
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### Ship-gate criteria
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`benchmark-compare.ts` enforces three rules and fails CI if any break:
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1. Aggregate token savings must be **≥ 30%**.
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2. **No** individual scenario may get more expensive.
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3. **Zero** capability regressions (the cheaper answer must still contain the
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known-correct symbols).
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## Methodology notes & honesty
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- Token counts are a **byte-based approximation**, not a specific tokenizer's
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output. The *ratio* between approaches is stable; the absolute numbers are
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estimates.
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- The grep-and-read figure depends on how aggressively the agent reads files. We
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report a realistic, not worst-case, traversal.
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- Results depend on repository size and the query mix. The harness captures
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`repositoryState` (file/node/edge counts) in every run so comparisons stay
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apples-to-apples.

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