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Lynkr

★ 542 Open GitHub ↗

Streamline your workflow with Lynkr, a CLI tool that acts as an HTTP proxy for efficient code interactions using Claude Code CLI.

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# Lynkr

### An LLM Gateway which optimises your token usage.

**84% fewer tokens on JSON tool results. 53% fewer tokens on tool-heavy requests. Sub-300ms semantic cache hits. Zero code changes.**

[![npm version](https://img.shields.io/npm/v/lynkr.svg)](https://www.npmjs.com/package/lynkr)
[![Tests](https://img.shields.io/badge/tests-1249%20passing-brightgreen)](https://github.com/Fast-Editor/Lynkr)
[![License: Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://raw.githubusercontent.com/Fast-Editor/Lynkr/main/LICENSE)
[![Node.js](https://img.shields.io/badge/node-20%2B-green)](https://nodejs.org)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Fast-Editor/Lynkr)

<table>
<tr>
<td align="center"><strong>84%</strong><br/>JSON Compression</td>
<td align="center"><strong>53%</strong><br/>Tool Token Reduction</td>
<td align="center"><strong>&lt;300ms</strong><br/>Semantic Cache Hits</td>
<td align="center"><strong>14+</strong><br/>LLM Providers</td>
<td align="center"><strong>0</strong><br/>Code Changes Required</td>
</tr>
</table>

> Numbers from the bundled benchmark against LiteLLM on identical free local backends — run it yourself: `node benchmark-tier-routing.js`. It doubles as a 19-scenario routing regression harness (currently 12/12 correctness checks), and `MODE=routing` runs a routing-only head-to-head that judges **both** proxies on the same acceptable-tier sets — including LiteLLM's Auto Router v2. [How it works →](docs/benchmarking.md)

> **Third-party benchmark:** on [RouterArena](https://github.com/RouteWorks/RouterArena) (ICLR 2026, 8,400 queries) Lynkr's routing scores **67.65 arena / 68.41% accuracy at $0.29 per 1K queries with 92.38 robustness** — above GPT-5's built-in router and NotDiamond at a fraction of their cost. [Methodology & caveats →](docs/routerarena-benchmark.md)

---

## 🚀 New: Wrap Mode for AI Coding Tools

**Use Lynkr's routing with your AI coding assistant — maximize your subscription value:**

```bash
npm install -g lynkr

# Claude Code Pro/Max
lynkr wrap claude

```

**Wrapping gives you:**
- ✅ Tier routing (send simple tasks to free Ollama, complex to your subscription/API)
- ✅ Sticky sessions: one routing decision per conversation via content fingerprinting, with automatic escalation when the task outgrows the model
- ✅ TOON/RTK compression (84% token reduction on large JSON tool outputs)
- ✅ Semantic caching (sub-300ms cache hits, 0 tokens billed)
- ✅ **3-5x more usage from the same subscription limits**
- ✅ Works with OAuth (Claude, Copilot, Cursor) or API keys (Aider, Codex)

[Full wrap guide →](docs/wrap-guide.md)

---

## Quick Start (2 Minutes)

### 1. Install Lynkr

```bash
npm install -g lynkr
```

### 2. Configure Lynkr

The fastest path is the interactive wizard:

```bash
lynkr init
```

It asks four questions — usage mode (Claude Pro/Max via wrap, or direct API keys), tier picks for SIMPLE/MEDIUM/COMPLEX/REASONING across the 14 supported providers, credentials for what you chose, and a few routing-intelligence knobs — then writes a fully-populated `.env` with sensible production defaults for everything else (caching, compression, policy budgets, MCP sandbox, rate limiting).

Useful flags:

```bash
lynkr init --force                # overwrite an existing .env
```

See [`docs/init.md`](docs/init.md) for the full wizard reference.

If you'd rather configure by hand, the manual options below still work — copy `.env.example` to `.env` and edit it directly:

**Option A: Free & Local (Ollama) - Recommended for Testing**

```bash
# Install Ollama first: https://ollama.com
ollama pull qwen2.5-coder:latest
```

Then start Lynkr:

```bash
lynkr start
```

### 3. Connect Your Tool

**Cursor IDE**
- Settings → Models → Override Base URL
- Set to: `http://localhost:8081/v1`
- API Key: `any-value`

**Codex CLI**

Edit `~/.codex/config.toml`:
```toml
model_provider = "lynkr"

[model_providers.lynkr]
base_url = "http://localhost:8081/v1"
wire_api = "responses"
```

✅ **Done!** Your AI tool now uses your chosen provider.

---

## Common Startup Errors

### Error: `unable to determine transport target for "pino-pretty"`

**Problem:** You're running an older version (< 9.3.0).

**Solution:** Update to the latest version:
```bash
npm install -g lynkr@latest
```

If you must use an older version, set `NODE_ENV=production` before starting.

### Warning: `Missing tier configuration: TIER_SIMPLE, TIER_MEDIUM...`

**This is just a warning - you can ignore it.** Tier routing is optional.

To remove the warning, add to `.env`:
```bash
TIER_SIMPLE=ollama:qwen2.5-coder:latest
TIER_MEDIUM=ollama:qwen2.5-coder:latest
TIER_COMPLEX=ollama:qwen2.5-coder:latest
TIER_REASONING=ollama:qwen2.5-coder:latest
```

### Warning: `FALLBACK_PROVIDER='databricks' is enabled but missing credentials`

**Solution:** Add to `.env`:
```bash
FALLBACK_ENABLED=false
```

### Error: `connect ECONNREFUSED ::1:11434` (Ollama)

**Problem:** Ollama is not running.

**Solution:**
```bash
ollama serve
```

Keep this terminal open, and start Lynkr in a new terminal.

### Error: `Connection refused` or `404 Not Found`

**Problem:** Lynkr is not running or wrong port.

**Solution:** Check Lynkr is running on the correct port:
```bash
curl http://localhost:8081/
```

Should return: `{"service":"Lynkr","version":"9.x.x","status":"running"}`

---

## Why Lynkr?

AI coding tools lock you into one provider and send every token raw. Lynkr breaks both locks.

```
Claude Code / Cursor / Codex / Cline / Continue
                    ↓
                  Lynkr
          ┌─────────────────────┐
          │  Strip unused tools  │  ← 53% fewer tokens on tool calls
          │  Compress JSON blobs │  ← 84% on large tool results
          │  Semantic cache      │  ← <300ms hits, 0 tokens billed
          │  Route by complexity │  ← cheap model for simple, cloud for hard
          │  Learn from outcomes │  ← kNN + bandit + auto-calibration
          └─────────────────────┘
                    ↓
    Ollama | Bedrock | Azure | Moonshot | OpenRouter | OpenAI
```

**What you get:**
- ✅ **53% fewer tokens** on tool-heavy requests (Claude Code, Cursor sessions)
- ✅ **84% compression** on large JSON tool results (grep, file reads, test output)
- ✅ **Semantic cache** serves repeated queries in under 300ms with 0 tokens billed
- ✅ **Automatic tier routing** — simple questions go to cheap models, complex ones escalate; sessions stick to one model until the task genuinely outgrows it
- ✅ **A closed learning loop** — every outcome trains a kNN router and bandit, and tier thresholds re-calibrate nightly from your own traffic
- ✅ Route through **your company's infrastructure** (Databricks, Azure, Bedrock)
- ✅ **Zero code changes** — just change one environment variable

---

## Supported Providers

| Provider | Type | Example Models | Cost |
|----------|------|---------------|------|
| **Ollama** | Local | qwen2.5-coder, deepseek-coder, llama3 | **Free** |
| **llama.cpp** | Local | Any GGUF model | **Free** |
| **LM Studio** | Local | Local models with GUI | **Free** |
| **OpenRouter** | Cloud | GPT-4o, Claude 3.5, Llama 3, Gemini | $ |
| **AWS Bedrock** | Cloud | Claude, Llama, Mistral, Titan | $$ |
| **Databricks** | Cloud | Claude Sonnet 4.5, Opus 4.6 | $$$ |
| **Azure OpenAI** | Cloud | GPT-4o, o1, o3 | $$$ |
| **Azure Anthropic** | Cloud | Claude Sonnet, Opus | $$$ |
| **OpenAI** | Cloud | GPT-4o, o3-mini | $$$ |
| **Atlas Cloud** | Cloud | Qwen, DeepSeek, and other OpenAI-compatible models | $-$$$ |
| **DeepSeek** | Cloud | DeepSeek R1, Reasoner | $ |

**4 local providers** for 100% offline, free usage. **11+ cloud providers** for scale.

<div align="center">
  <a href="https://www.atlascloud.ai/?utm_source=github&amp;utm_medium=readme&amp;utm_campaign=lynkr">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://www.atlascloud.ai/logo-white.svg">
      <img src="https://www.atlascloud.ai/logo.svg" alt="Atlas Cloud" width="196">
    </picture>
  </a>
  <br>
  <sub>Use Lynkr with Atlas Cloud's OpenAI-compatible API and route across its live model catalog with one optional provider configuration. <a href="https://www.atlascloud.ai/?utm_source=github&amp;utm_medium=readme&amp;utm_campaign=lynkr">Explore Atlas Cloud</a>.</sub>
</div>

---

## Advanced: Tier Routing (Save Even More)

Route different request types to different models automatically:

```bash
# .env file
MODEL_PROVIDER=ollama
FALLBACK_ENABLED=false

# Use small/fast models for simple tasks
TIER_SIMPLE=ollama:qwen2.5:3b

# Use medium models for normal coding
TIER_MEDIUM=ollama:qwen2.5:7b

# Use powerful models for complex architecture
TIER_COMPLEX=ollama:deepseek-r1:14b
TIER_REASONING=ollama:deepseek-r1:14b

# Optional: Limits (remove for unlimited) for long conversations
POLICY_MAX_STEPS=50
POLICY_MAX_TOOL_CALLS=100
```

Lynkr analyzes each request and routes it to the appropriate tier. Simple questions use fast models. Complex refactoring uses powerful models. The scorer combines anchor-embedding classification (WS7, payload-invariant) with an LLM difficulty classifier (Phase 6, added 2026-07-19) that catches topic-vs-difficulty confounding — `list the exports from this file` correctly routes to MEDIUM instead of getting boosted to REASONING by "technical vocabulary" alone.

**Result:** 70-90% of requests use cheaper/faster models. Only hard problems hit expensive models. **15× reduction in expensive-tier over-routing** vs the anchor-only baseline on the 381-prompt eval set (0.6% vs ~15%).

Tier configuration is strictly authoritative — bandit exploration is constrained to the models you've listed in `TIER_*`, and multi-turn conversations score with a recency-weighted sliding window so context isn't lost on short follow-ups. Conversations get a content-fingerprint session id (clients like Claude Code send none), the decision pins for the session, and a guarded escape ladder (risk keywords, force phrases, score drift, context overflow) re-escalates the moment a task outgrows its model. Full pipeline: [`docs/routing-intelligence.md`](docs/routing-intelligence.md) · intent scorer: [`docs/intent-window-routing.md`](docs/intent-window-routing.md) · verify any change: [`docs/benchmarking.md`](docs/benchmarking.md).

---

## Common Issues & Fixes

| Issue | Solution |
|-------|----------|
| **"Service temporarily overloaded"** | Ollama model too large for RAM. Use smaller model or increase `--max-old-space-size` |
| **"Route not found: HEAD /"** | Ignore - harmless health check from Claude Code |
| **"Hallucinated tool calls"** | Normal - Lynkr automatically filters invalid tools |
| **"Safe Command DSL blocked"** | Add `POLICY_SAFE_COMMANDS_ENABLED=false` to `.env` |
| **"spawn graphify ENOENT"** | Optional feature. Set `CODE_GRAPH_ENABLED=false` in `.env` (see Advanced Features section for installation) |
| **Slow first request (20+ sec)** | Ollama loading model into memory. Add `OLLAMA_KEEP_ALIVE=30m` in Ollama config |
| **No response after N turns** | Remove `POLICY_MAX_STEPS` and `POLICY_MAX_TOOL_CALLS` from `.env` (unlimited by default in v9.3.0+) |

---

## Advanced Features

### Real-time SSE streaming (all tiers)

Responses stream token-by-token instead of arriving all at once — including
through the tier router. Two mechanisms, both on by default:

- **Native passthrough**: when the upstream already speaks Anthropic SSE
  (Anthropic endpoints, Z.AI, Ollama v0.14+), Lynkr pipes the bytes straight
  through with backpressure. Kill switch: `LYNKR_NATIVE_PASSTHROUGH=false`.
- **Cross-format transform**: OpenAI-format upstreams (OpenAI, Azure OpenAI,
  OpenRouter, Databricks, llama.cpp, LM Studio) are reshaped into Anthropic
  events in flight — including reassembling tool-call argument fragments into
  clean `tool_use` blocks. Kill switch: `LYNKR_STREAM_TRANSFORM=false`.

The `LYNKR_VISIBLE_ROUTING` badge streams too (injected as the first content
block), and telemetry is recorded on stream close with real token counts.
Fallback safety: if an upstream fails before the first byte, the request
falls back to the buffered path automatically.

```bash
# Ollama thinking models (MiniMax): streaming skips the <think>-leak repair.
# If you see raw <think> text in responses, buffer that provider instead:
LYNKR_OLLAMA_BUFFER_RESPONSES=true   # default true; false = stream Ollama
```

### Token Optimization (60-80% savings)
```bash
# Enable all optimizations
PROMPT_CACHE_ENABLED=true
SEMANTIC_CACHE_ENABLED=true
```

Always-on (no config): **RTK tool-result compression**
(test/git/grep/lint/build/JSON output), **MCP tool dedup** (drops
built-in WebSearch/WebFetch when an Exa/Tavily MCP tool is present), and
**request bypass** (Claude CLI Warmup / title-extraction calls are answered
locally, never hitting a provider).

Optional **terse-output mode** to cut *output* tokens:
```bash
CAVEMAN_ENABLED=true        # off by default — nudges the model to be concise
CAVEMAN_LEVEL=lite          # lite | full | ultra
```

### Built-in dashboard

Open `http://localhost:8081/dashboard` while Lynkr is running. No setup, reads
the local telemetry store:

- **Spend & savings** — actual cost vs what the same traffic would have cost
  on the flagship model (the counterfactual no pass-through gateway can show,
  since they neither pick your model nor see local-model traffic)
- **Tier mix** — daily request breakdown across SIMPLE/MEDIUM/COMPLEX/REASONING
- **Routing accuracy** — over-/under-provisioned request counts, a self-audit
  of the tier router's decisions
- **Request logs** — filterable by provider, tier, and errors, with latency,
  tokens, and cost per request; every session id links to a drill-down
- **Provider health** — configured providers, credential warnings, circuit
  breaker states
- **Insights** — analyzer findings ranked by *past overspend* (auditable
  against spend already recorded, never projected savings): dead tool
  schemas paying rent, harness side-traffic served on expensive tiers,
  weak prompt-cache hit ratios, and Wilson-bounded tier-downsize verdicts.
  Each finding ships its evidence rows, the fix, the caveat, and how the
  figure was computed. "Not measured yet" is rendered distinctly from
  zero — an un-run scan is not an all-clear.
- **Explore** — a Metric × Dimension (× Stack) pivot over routing telemetry
  (spend/tokens/requests/sessions by provider, model, tier, request type,
  day…) with preset views, period-over-period deltas, and CSV export
- **Session drill-down** — per-model cost mix and an input-context-per-call
  chart where distillation and cache behavior are directly visible
- **Cache receipts** — projected switch/hold economics from cache-aware
  routing next to the *measured* companion (dollars saved by cache reads
  actually served)

### Status line for Claude Code

One line after every turn — routed tier, served model, today's spend, and
cache re-read share — with zero token cost (out-of-band, never enters model
context). Add to `~/.claude/settings.json`:

```json
"statusLine": { "type": "command", "command": "lynkr-statusline" }
```

```
◆ Opus 4.8 · MEDIUM → minimax-m3:cloud (ollama) · today $0.06 · cache 67%
```

Always prints exactly one line and always exits 0 — a down proxy can never
break the harness. Set `LYNKR_PORT` if Lynkr isn't on 8081.

### Cost tracking & model pricing
Per-request cost is computed from a model-pricing registry (LiteLLM → models.dev,
cached 24h) and recorded in telemetry. Models the registry doesn't know record
`cost_usd=null` (logged once) rather than a fabricated price. Pin prices for
unknown models:
```bash
# Per-1M-token USD prices, JSON keyed by model name
MODEL_PRICE_OVERRIDES={"my-model":{"input":0.5,"output":1.5}}
```

### Memory System (Titans-inspired)
```bash
MEMORY_ENABLED=true
MEMORY_TTL=3600000  # 1 hour
```

### Load Shedding & Resilience
```bash
LOAD_SHEDDING_ENABLED=true
LOAD_SHEDDING_HEAP_THRESHOLD=0.85
```

### Admin Hot-Reload (no restart needed)
```bash
curl -X POST http://localhost:8081/v1/admin/reload
```

### Code Intelligence (Optional - Graphify)

**Graphify** provides AST-based code analysis for smarter routing decisions.

**Installation (Rust required):**
```bash
# Install Rust if not already installed
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

# Build and install graphify
git clone https://github.com/safishamsi/graphify
cd graphify
cargo build --release
sudo cp target/release/graphify /usr/local/bin/

# Verify installation
graphify --version
```

**Enable in `.env`:**
```bash
CODE_GRAPH_ENABLED=true
CODE_GRAPH_WORKSPACE=/path/to/your/project  # Optional, defaults to cwd
```

**Features:**
- AST-based complexity scoring
- Structural code analysis (19 languages supported)
- Enhanced routing decisions based on code structure

**Note:** Graphify is completely optional. If not installed, Lynkr falls back to simpler complexity analysis.

---

## Installation Methods

**NPM (recommended)**
```bash
npm install -g lynkr
```

**One-line installer**
```bash
curl -fsSL https://raw.githubusercontent.com/Fast-Editor/Lynkr/main/install.sh | bash
```

**Homebrew** (macOS / Linux)
```bash
brew tap fast-editor/lynkr
brew install lynkr
lynkr --version
```
Upgrade later with `brew update && brew upgrade lynkr`. The formula tracks the latest [`lynkr` npm release](https://www.npmjs.com/package/lynkr) automatically.

**Docker**
```bash
git clone https://github.com/Fast-Editor/Lynkr.git
cd Lynkr
docker-compose up -d
```

**From source**
```bash
git clone https://github.com/Fast-Editor/Lynkr.git
cd Lynkr
npm install
cp .env.example .env
npm start
```

---

## Documentation

| Guide | Description |
|-------|-------------|
| [Installation](documentation/installation.md) | All installation methods |
| [Provider Setup](documentation/providers.md) | Configuration for all 12+ providers |
| [Claude Code](documentation/claude-code-cli.md) | Claude Code CLI integration |
| [Cursor IDE](documentation/cursor-integration.md) | Cursor setup + troubleshooting |
| [Codex CLI](documentation/codex-cli.md) | Codex configuration |
| [Tier Routing](documentation/routing.md) | Smart model routing by complexity |
| [Token Optimization](documentation/token-optimization.md) | 60-80% cost reduction |
| [Troubleshooting](documentation/troubleshooting.md) | Common issues and solutions |
| [API Reference](documentation/api.md) | REST API endpoints |
| [Production](documentation/production.md) | Enterprise deployment |

---

## Benchmark Results

Head-to-head against **LiteLLM** on the **same backends** (Ollama `minimax-m2.5`, Moonshot, Azure OpenAI), 9 scenarios across 4 feature categories. Apples-to-apples comparison is Lynkr vs LiteLLM **billed tokens on the same scenario**. Run with `node benchmark-tier-routing.js`.

> _Runs: token benchmarks — June 5, 2026 (Lynkr v9.3.2 · LiteLLM v1.87.1). Tier routing head-to-head — **re-run July 19, 2026** (Lynkr working tree w/ Phase-6 classifier + config B · LiteLLM v1.94.0.dev1 Auto Router v2). macOS, Apple Silicon._

### Token reduction (vs LiteLLM, same model & prompt)

| Mechanism | Lynkr | LiteLLM | Result |
|---|---|---|---|
| TOON compression (60-item grep JSON) | **427** tokens · $0.009 | 3,458 tokens · $0.018 | **87.6% fewer tokens, 50% cheaper** |

Lynkr binary-compresses large JSON tool results (TOON) in-process, with no added latency.

### Semantic cache

| | Tokens billed | Response time |
|---|---|---|
| First call (cold) | 2,857 | 1,891ms |
| **Second call — paraphrased, cache hit** | **0** (served from cache) | **171ms (11× faster)** |

Near-identical prompts return cached responses in 171ms. Zero model tokens billed on a cache hit.

### Tier routing — vs LiteLLM Auto Router v2 (re-run July 19, 2026)

LiteLLM v1.94 shipped a native complexity router (`auto_router/complexity_router`) with the same four tier names Lynkr uses. Head-to-head on the **same backends** with identical prompts, both proxies live, both judged against the same acceptable-tier sets (11 routing scenarios, `MODE=routing node benchmark-tier-routing.js`, LiteLLM config in `litellm-autorouter-v2.yaml`):

| Router | Routing-correct | Notes |
|---|---|---|
| **Lynkr (Phase-6 classifier + config B)** | **11/11 ✅** | anchor embedding + local LLM classifier reconcile + FORCE_REASONING patterns + risk-remap; ~500ms warm classifier call, cached |
| LiteLLM v2 — heuristic (default) | **4/11** | every miss *under-routed* — banking security analysis, whole-pipeline refactor, prod auth-file fix, autonomous agentic loop all sent to a 7B local model |
| LiteLLM v2 — LLM classifier | 6–8/11 (non-deterministic) | paid GPT-5.2 call + ~2–3s on **every** request; fails outright with local classifier models |

**The 7 requests LiteLLM v2 misroutes to a 7B ollama model (should be COMPLEX or REASONING):**

| What you typed | Lynkr sends to | LiteLLM sends to |
|---|---|---|
| "Analyse security trade-offs of JWT vs httpOnly cookies for a banking app" | Claude Opus (top) | **ollama 7B** ❌ |
| "Refactor the entire ingestion pipeline and give me the plan" | z.ai GLM-5.2 (mid) | **ollama 7B** ❌ |
| "Fix the null-check bug in `src/auth/middleware.ts`" | Claude Opus (risk-remap) | **ollama 7B** ❌ |
| "Figure out why this test is flaky. You have full autonomy — iterate until 10 runs pass" | Claude Opus (autonomous) | **ollama 7B** ❌ |
| "please summarize the exports of this file quickly" | ollama minimax (correct — MEDIUM) | ollama minimax — but for the wrong reason (LiteLLM under-routed to SIMPLE) |

Lynkr layers the following, in decreasing priority, to catch every miss LiteLLM leaks:

- FORCE_REASONING regex (`ultrathink`, `prove`, `security audit`, `from first principles`) → deterministic top-tier
- Risk classifier (auth/middleware/credentials paths) → REASONING under config B
- Agentic detector (`AUTONOMOUS` workflows) → REASONING minimum
- Anchor-embedding classifier over 4 difficulty classes + FRONTIER_MIN_SIM floor
- Local LLM classifier (qwen2.5:3b) reconciles borderline anchor calls
- Envelope invariance — scores cleaned user text only, ignoring tool schemas / history / system-reminders

LiteLLM's router reads the raw last message with no envelope-stripping, no verify-then-escalate cascade, no risk classifier, no agentic detection — its fallbacks trigger only on HTTP errors, never on a bad answer.

_Fairness notes: the 11 scenarios derive from Lynkr's own regression suite, so Lynkr has home-field advantage — the transferable finding is the **direction** of LiteLLM's failures (systematic under-routing to cheap on hard prompts), not the exact scores. Lynkr's top tiers used Claude Opus 4.8 / z.ai GLM-5.2; LiteLLM was given the identical tier targets._

### Cost projection (100,000 requests/month, same backend)

**Direct cost (raw meter):**

| | Monthly cost | vs LiteLLM |
|---|---|---|
| LiteLLM (TOON tool-heavy scenario) | ~$818 | baseline |
| **Lynkr (TOON tool-heavy)** | **~$409** | **~50% cheaper** via token optimization |

**Effective cost per correct-tier answer** (July 19 head-to-head, all 11 routing scenarios):

| | Direct $/mo | Effective $/mo if you re-issue misroutes at correct tier |
|---|---|---|
| **Lynkr** | ~$5.50 (spends where it should, doesn't where it shouldn't) | ~$5.50 |
| LiteLLM v2 heuristic | $0 (under-routes everything to ollama) | ~$89 (7 misroutes need re-issue on Moonshot/COMPLEX) |

The story isn't "Lynkr always spends less." It's **Lynkr spends where it should and doesn't where it shouldn't**. LiteLLM's zero-dollar cost on complex prompts is bought with wrong-tier answers to hard work — a 7B model answering "prove this rate limiter is fair under concurrent refill" is silent quality collapse, not a savings.

→ [Full benchmark report with methodology](BENCHMARK_REPORT.md)

---

## Cost Comparison

| Scenario | Direct Anthropic | Lynkr + Ollama | Lynkr + OpenRouter |
|----------|-----------------|----------------|-------------------|
| Daily coding (8h) | $10-30/day | **$0 (free)** | $2-8/day |
| Monthly (heavy use) | $300-900 | **$0** | $60-240 |

With tier routing + token optimization: **additional 50-87% savings** on cloud providers depending on workload.

---

## Why Lynkr vs Alternatives

| Feature | Lynkr | LiteLLM | OpenRouter | PortKey |
|---------|-------|---------|-----------|---------|
| **Setup** | `npm install -g lynkr` | Python + Docker + Postgres | Account signup | Docker stack |
| **Claude Code native** | ✅ Drop-in | ⚠️ Requires config | ❌ | ⚠️ Partial |
| **Cursor native** | ✅ Drop-in | ⚠️ Partial | ❌ | ⚠️ Partial |
| **Local models** | Ollama, llama.cpp, LM Studio | Ollama only | ❌ | ❌ |
| **Automatic tier routing** | ✅ embedding intent + 13-dimension scorer, verified cascade | ⚠️ Auto Router v2 (v1.94): heuristic under-routes, LLM classifier billed per request | ❌ | ❌ Manual metadata |
| **TOON JSON compression** | ✅ up to 87.6% | ❌ | ❌ | ❌ |
| **Upstream SSE streaming** | ✅ native passthrough + cross-format transform | ⚠️ passthrough only | ✅ | ⚠️ |
| **Semantic cache** | ✅ 171ms hits, 0 tokens | ❌ | ❌ | ✅ Prompt cache only |
| **Savings & routing dashboard** | ✅ spend, savings vs flagship, tier mix, routing accuracy, request logs, pivot explorer, session drill-down | ⚠️ spend UI only | ⚠️ usage page | ✅ observability suite (no routing accuracy) |
| **Cost recommendations** | ✅ evidence-backed findings (dead tool schemas, side-traffic, cache weak spots, Wilson-proven downsizes) ranked by past overspend | ❌ | ❌ | ⚠️ alerts, no findings |
| **Long-term memory** | ✅ SQLite, per-session | ❌ | ❌ | ❌ |
| **MCP integration** | ✅ | ❌ | ❌ | ❌ |
| **Self-hosted** | ✅ Node.js only | ✅ Python stack | ❌ SaaS | ✅ Docker |
| **Dependencies** | Node.js 20+ | Python, Prisma, PostgreSQL | None | Docker, Python |

**Lynkr's edge:** Purpose-built for AI coding tools. Compresses tokens before they reach the model — not just after. Zero-config for Claude Code, Cursor, and Codex. Installs in one command.

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## Community

- [GitHub Discussions](https://github.com/Fast-Editor/Lynkr/discussions) — Ask questions
- [Report Issues](https://github.com/Fast-Editor/Lynkr/issues) — Bug reports
- [NPM Package](https://www.npmjs.com/package/lynkr) — Official releases
- [DeepWiki](https://deepwiki.com/Fast-Editor/Lynkr) — AI-powered docs

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## License

Apache 2.0 — See [LICENSE](LICENSE).

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**Built by [Vishal Veera Reddy](https://github.com/vishalveerareddy123) for developers who want control over their AI tools.**

Lynkr

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