# Getting started with context-mode and headroom: understand large codebases at 92% lower token cost In our live benchmark across an eight-file payment and webhook microservice (10 graded codebase comprehension queries per session, measured on october 20, 2026 against `https://api.regolo.ai/v1/chat/completions`), an unoptimized single-model agent consumed 197,644 total tokens (184,753 prompt tokens and 12,891 completion and thinking tokens) with a median latency of 15.16 seconds per query. Pairing local reciprocal-rank-fusion indexing ([context-mode](https://github.com/mksglu/context-mode), 31.2k github stars) and syntax-tree/log compression ([headroom](https://github.com/headroomlabs-ai/headroom), 76.4k github stars) with [regolo's `brick-complexity-pro` router](https://regolo.ai/pricing/) cut total consumption to 15,101 tokens per session (-92.4 percent) and dropped median query latency to 1.40 seconds (10.8x faster). Uncompressed (197.6K tok) → 1. context-mode (RRF index) ↓ **top-k module selection** bm25 + trigram fusion in sqlite → 2. headroom (AST + JSON crush) ↓ **-95.1% prompt tokens** 184,753 → 9,103 input tokens → 3. regolo brick-complexity-pro ↓ **11.1× faster (1.33s p50)** -53.5% thinking tokens · €0.97/1k runs (€0.29 promo) [See the measured 4-way benchmark results ↓](#cost-per-task) When you ask a coding agent in opencode, claude code, or cursor to trace an unfamiliar webhook handler or diagnose a race condition, thirty to fifty raw file reads rapidly fill the context window. Once the agent compacts the conversation to free space, it forgets which modules it already inspected and repeats the same searches, burning thousands of reasoning tokens on deterministic lookups. Combining local context compression with regolo's core subscription (€39.00/month for 600 million tokens, or €11.70/month during the first three months) reduces the cost of 1,000 codebase exploration sessions from $310.22 on z.ai's direct api ($486.70 on private workhorse apis and €233.97 on unoptimized Pay-As-You-Go) down to €0.97 (€0.29 during the three-month promotion, or €27.02 on Regolo Pay-As-You-Go). This tutorial teaches you how to build and benchmark a three-layer codebase comprehension pipeline: - rank and filter repository modules locally using bm25 and trigram reciprocal rank fusion with `context-mode`. - strip module-level boilerplate via python ast parsing and collapse repetitive json logs while preserving fatal error lines byte-for-byte with `headroom`. - route lookup queries to `brick-complexity-pro` with `reasoning_effort: "none"` while escalating multi-file bug diagnosis to `qwen3.5-122b` and `glm-5.3` at full reasoning depth. Before you start the implementation, you must have node.js 22+, python 3.10+, a local git repository to inspect, and an active regolo api key. SOVEREIGN EUROPEAN INFERENCE ### Run DeepSeek, Qwen, and GLM in Europe with Zero Data Retention Get 600 Million tokens on the Regolo Core plan (€39/mo flat, ~€0.065/1M). Switch endpoints in 1 line of code with full OpenAI SDK compatibility on 100% green datacenters. [ Start 30-day trial → ](https://regolo.ai/pricing/?utm_source=blog&utm_medium=bento_cta&utm_campaign=deepseek-flash-mid) Free credits included · Live in 60s ✓ 100% EU Green Datacenters ✓ Certified ZDR --- ## Set up context-mode and headroom with your coding agent ``` # 1. install context-mode globally and index your repository into sqlite fts5 npm install -g context-mode context-mode doctor cd /path/to/your/repo context-mode index . --source project:billing-service # 2. install headroom and point its proxy at regolo's european endpoint uv tool install --python 3.13 "headroom-ai[all]" export OPENAI_TARGET_API_URL="https://api.regolo.ai/v1" export OPENAI_API_KEY="$REGOLO_API_KEY" export HEADROOM_OUTPUT_SHAPER=1 export HEADROOM_BEACON=off # 3. wrap opencode (or start the proxy on port 8787 for claude code and cursor) headroom wrap opencode --openai-api-url https://api.regolo.ai/v1Code language: Python (python) ``` Running `context-mode index` chunks your codebase into a local sqlite fts5 table with porter stemming and trigram indexing so your agent queries only matching modules instead of reading the entire directory tree. Wrapping your agent with `headroom` activates local ast compression, json log deduplication, reversible caching (`headroom_retrieve`), and automatic thinking-effort shaping (`HEADROOM\_OUTPUT\_SHAPER=1`). --- ## Implement local rrf file ranking, ast compression, and json log crushing in python To measure the exact impact of each layer without external framework overhead, the following zero-dependency python module (`compressor.py`) implements the three core mechanisms used in our benchmark: reciprocal rank fusion (`bm25 + trigram` overlap), python ast docstring/banner stripping (`ast.parse` and `ast.unparse`), and json log crushing (collapsing repetitive `INFO` healthchecks while keeping `WARN`, `ERROR`, and `FATAL` entries intact): ``` import ast import json import math import re from collections import Counter STOPWORDS = { "in", "the", "what", "is", "are", "of", "and", "to", "for", "on", "with", "why", "does", "how", "when", "if", "by", "from", "src", "py", "json", "logs", "file", "value", "values", "exact", "name", } def tokenize(text: str) -> list[str]: return [t for t in re.findall(r"[a-z0-9_]+", text.lower()) if t not in STOPWORDS] def compress_python_ast(source: str) -> str: """strip repetitive module-level compliance headers while preserving executable code.""" try: tree = ast.parse(source) if ( tree.body and isinstance(tree.body[0], ast.Expr) and isinstance(tree.body[0].value, ast.Constant) and isinstance(tree.body[0].value.value, str) ): tree.body.pop(0) return ast.unparse(tree) except Exception: return source def crush_json_logs(raw_json: str) -> str: """collapse repetitive info events while preserving warn/error/fatal entries byte-for-byte.""" data = json.loads(raw_json) kept, info_counts = [], Counter() for ev in data.get("events", []): if ev.get("level", "INFO") in ("ERROR", "FATAL", "WARN"): kept.append(ev) else: info_counts[ev.get("event", "routine")] += 1 return json.dumps({ "service": data.get("service"), "collapsed_routine_events": dict(info_counts), "critical_events": kept, }, separators=(",", ":")) def select_route_and_effort(question_item: dict, use_regolo_routing: bool) -> tuple[str, str]: """route bounded lookups to brick-complexity-pro (thinking off) and diagnosis to flagship.""" if not use_regolo_routing: return ("qwen3.5-122b", "high") if question_item["category"] == "lookup": return ("brick-complexity-pro", "none") return ("qwen3.5-122b", "high")Code language: Python (python) ``` Separating lookup routing (`brick-complexity-pro` with `reasoning\_effort: "none"`) from multi-file bug diagnosis (`qwen3.5-122b` or `glm-5.3` with `reasoning\_effort: "high"`) is critical: in our ablation tests, turning thinking off on multi-step diagnosis questions caused accuracy on cross-file math and hmac verification (`q9` and `q10`) to drop to 60 percent, whereas keeping `reasoning\_effort: "high"` on diagnosis and `"none"` on lookups preserved 90 to 100 percent accuracy while cutting lookup latency from 9.75 seconds down to 0.37–0.87 seconds. When you execute `python3 benchmark.py`, the runner logs all three layers explicitly on every query so you can inspect which files were selected at layer 1, how many characters were stripped at layer 2, and which model, thinking budget, and cost applied at layer 3 (verified against [regolo.ai/pricing](https://regolo.ai/pricing/)): zsh — Codebase\_Comprehension\_Benchmark Layer 1 · RRF Filter Layer 2 · Headroom AST Layer 2 · Headroom JSON Crush Layer 3 · Regolo Router ``` $ python3 test_benchmark.py && python3 benchmark.py ✓ All local compression, RRF, and routing assertions passed. Running A. Baseline (Uncompressed + Pinned qwen3.5-122b)... -> Accuracy: 100.0% | Prompt: 184,753 | Completion: 11,719 | Total: 196,472 | p50: 14.81s -> Cost/1k sessions: Private API $486.70 | Z.ai Direct $310.22 | Regolo PAYG €233.97 | Regolo Core €12.77 (€3.83 promo) Running B. Local Compression Only (Context-Mode + Headroom AST/Log)... -> Accuracy: 100.0% | Prompt: 9,091 (-95.1%) | Completion: 12,731 | Total: 21,822 | p50: 15.04s -> Cost/1k sessions: Private API $145.49 | Z.ai Direct $68.74 | Regolo PAYG €62.56 | Regolo Core €1.42 (€0.43 promo) Running C. Regolo Brick Complexity Pro Only (Uncompressed)... -> Accuracy: 90.0% | Prompt: 184,765 | Completion: 5,072 (-56.7%) | Total: 189,837 | p50: 1.47s -> Cost/1k sessions: Private API $420.25 | Z.ai Direct $280.99 | Regolo PAYG €105.20 | Regolo Core €12.34 (€3.70 promo) Running D. Full Stack (Context-Mode + Headroom + Regolo Brick Pro)... -> Accuracy: 90.0% | Prompt: 9,103 (-95.1%) | Completion: 5,788 (-50.6%) | Total: 14,891 (-92.4%) | p50: 1.33s (11.1× faster) -> Cost/1k sessions: Private API $76.09 | Z.ai Direct $38.21 | Regolo PAYG €27.02 | Regolo Core €0.97 (€0.29 promo) === EXPLICIT 3-LAYER PIPELINE LOGS (Configuration D · Per-File Headroom Breakdown) === [q1 · lookup · Constant & header check in Python modules] [Layer 1 · context-mode RRF] 8 repo files (51,537 chars) -> top 4 selected (13,342 chars · -74.1%): ['src/config.py', 'src/auth.py', 'src/idempotency.py', 'src/retry_worker.py'] [Layer 2 · headroom compress] 13,342 chars -> 2,935 chars (-78.0% in Layer 2 | -94.3% vs raw repo): ├─ src/config.py [ast_strip_module_docstring] 2,969 -> 348 chars (-88.3%) · stripped 2,621 chars of compliance headers; kept executable AST & signatures ├─ src/auth.py [ast_strip_module_docstring] 3,407 -> 790 chars (-76.8%) · stripped 2,617 chars of compliance headers; kept executable AST & signatures ├─ src/idempotency.py [ast_strip_module_docstring] 3,732 -> 1,056 chars (-71.7%) · stripped 2,676 chars of compliance headers; kept executable AST & signatures └─ src/retry_worker.py [ast_strip_module_docstring] 3,234 -> 614 chars (-81.0%) · stripped 2,620 chars of compliance headers; kept executable AST & signatures [Layer 3 · regolo routing] model=brick-complexity-pro reasoning_effort=none -> in=866 tok, out=52 tok (0 thinking), latency=0.743s, pass=True [q6 · lookup · SRE incident log triage on noisy JSON dump] [Layer 1 · context-mode RRF] 8 repo files (51,537 chars) -> top 4 selected (38,709 chars · -24.9%): ['src/idempotency.py', 'src/webhooks.py', 'src/rate_limiter.py', 'logs/sre_incident_dump.json'] [Layer 2 · headroom compress] 38,709 chars -> 3,159 chars (-91.8% in Layer 2 | -93.9% vs raw repo): ├─ src/idempotency.py [ast_strip_module_docstring] 3,732 -> 1,056 chars (-71.7%) · stripped 2,676 chars of compliance headers; kept executable AST ├─ src/webhooks.py [ast_strip_module_docstring] 3,673 -> 946 chars (-74.2%) · stripped 2,727 chars of compliance headers; kept bug docstring & AST ├─ src/rate_limiter.py [ast_strip_module_docstring] 3,223 -> 579 chars (-82.0%) · stripped 2,644 chars of compliance headers; kept executable AST └─ logs/sre_incident_dump.json [json_log_crusher] 28,081 -> 433 chars (-98.5%) · collapsed 180 routine INFO healthchecks -> 1 counter; preserved 2 WARN/FATAL events [Layer 3 · regolo routing] model=brick-complexity-pro reasoning_effort=none -> in=944 tok, out=167 tok (0 thinking), latency=1.583s, pass=True [q8 · diagnosis · Cross-file race condition: Python config vs SRE JSON log] [Layer 1 · context-mode RRF] 8 repo files (51,537 chars) -> top 4 selected (38,455 chars · -25.4%): ['src/config.py', 'src/idempotency.py', 'src/webhooks.py', 'logs/sre_incident_dump.json'] [Layer 2 · headroom compress] 38,455 chars -> 2,922 chars (-92.4% in Layer 2 | -94.3% vs raw repo): ├─ src/config.py [ast_strip_module_docstring] 2,969 -> 348 chars (-88.3%) · kept REDIS_LOCK_TTL_MS = 1200; stripped 2,621 chars of boilerplate ├─ src/idempotency.py [ast_strip_module_docstring] 3,732 -> 1,056 chars (-71.7%) · kept acquire_event_lock() AST; stripped 2,676 chars of boilerplate ├─ src/webhooks.py [ast_strip_module_docstring] 3,673 -> 946 chars (-74.2%) · kept handle_invoice_paid_webhook() AST; stripped 2,727 chars of boilerplate └─ logs/sre_incident_dump.json [json_log_crusher] 28,081 -> 433 chars (-98.5%) · collapsed 180 INFO rows; preserved WARN (settlement_ms=1840) & FATAL [Layer 3 · regolo routing] model=qwen3.5-122b reasoning_effort=high -> in=896 tok, out=1,192 tok (deep diagnosis), latency=8.431s, pass=True ``` --- ## Measured benchmark: without regolo versus with regolo + local compression We evaluated four configurations on an eight-file payment and webhook service (`src/config.py`, `src/auth.py`, `src/idempotency.py`, `src/webhooks.py`, `src/ledger.py`, `src/retry\_worker.py`, `src/rate\_limiter.py`, and a 182-event `logs/sre\_incident\_dump.json` file) across 10 graded questions (6 constant/log lookups and 4 multi-file bug diagnosis tasks, including a race-condition double debit between `handle\_invoice\_paid\_webhook` and `REDIS\_LOCK\_TTL\_MS = 1200`): BENCHMARK · 8 REPO FILES, 182 SRE LOG EVENTS & 10 GRADED QUERIES ### 92.4% fewer total tokens, 10.8× faster p50 latency, and €0.97 per 1,000 sessions (€0.29 on 3-mo promo) on regolo core. Measured on october 20, 2026 against regolo's european zero-data-retention api (/v1/chat/completions), comparing an unoptimized flagship baseline against context-mode rrf + headroom ast compression + regolo brick-complexity-pro. With regolo (brick pro + context-mode + headroom) Single lever only (compression only / routing only) Without regolo (uncompressed + direct api baseline) KPI 1 · Prompt tokens per 10-query session Full stack (regolo)9,103 (-95.1%) Compression only9,091 (-95.1%) Brick routing only184,765 Uncompressed base184,753 KPI 2 · Completion & thinking tokens per session Brick routing only5,054 (-60.8%) Full stack (regolo)5,998 (-53.5%) Uncompressed base12,891 Compression only13,209 KPI 3 · Median query latency (p50 in seconds) Full stack (regolo)1.33 s (11.1×) Brick routing only1.95 s (7.8×) Compression only13.70 s Uncompressed base15.16 s KPI 4 · Cost per 1,000 sessions (10,000 queries) Regolo full (promo)€0.29 Regolo full (core)€0.97 Z.ai direct (base)$310.22 Private api (base)$486.70 **19.3×** fewer total tokens per session (15,101 vs 197,644 tokens, 92.4% reduction) **10.8×** faster median response time (1.40 s vs 15.16 s p50; lookups in 0.37 s–0.87 s) **100%** accuracy on multi-file bug diagnosis (90% overall across all 10 graded queries) Measured on october 20, 2026 across 8 microservice files and 182 sre log events (10 queries per session: 6 lookup, 4 multi-file bug diagnosis). Costs per 1,000 sessions compare regolo core (€39/mo for 600M tokens; €11.70/mo first 3 months) with brick-complexity-pro (all Core models included in the 600M pool at €0.065/1M; €0.0195/1M during the 3-month promo) against z.ai direct glm-5.3 api ($1.40 in / $4.40 out) and private workhorse apis ($2.00 in / $10.00 out). Looking across the four configurations reveals why neither local compression nor model routing is sufficient on its own. KPI FOCUS · MEDIAN QUERY LATENCY (P50)### Median response time per codebase query (p50) 1.40 seconds with context-mode + headroom + regolo brick-complexity-pro vs 15.16 seconds on the unoptimized baseline (10.8× faster). With regolo + compression1.40 s Regolo brick pro only1.95 s Local compression only13.70 s Without regolo (baseline)15.16 s Local compression alone shrinks the input prompt by 95.1%, but leaving reasoning\_effort="high" on lookup turns still stalls p50 latency at 13.70s. Routing lookups through brick-complexity-pro with thinking off drops lookup latency to 0.37s–0.87s. When we applied local compression by itself in configuration b, reciprocal rank fusion and ast/json crushing shrank the input prompt from 184,753 tokens down to 9,091 tokens (-95.1 percent), yet median query latency barely moved (13.70 seconds versus 15.16 seconds) because the flagship model still spent 13,209 completion tokens deliberating at high reasoning effort over simple constant lookups. KPI FOCUS · COST PER 1,000 CODEBASE SESSIONS (10,000 QUERIES)### Cost to run 1,000 codebase exploration sessions €0.97 on regolo core (€0.29 during the 3-month promo) vs $310.22 on z.ai's direct glm-5.3 api and $486.70 on private workhorse apis. Regolo core (3-mo promo)€0.29 Regolo core (€39/mo pool)€0.97 Z.ai direct (compressed)$68.74 Z.ai direct (uncompressed)$310.22 Private api (uncompressed)$486.70 Calculated from measured token consumption across 1,000 sessions (10,000 queries). Regolo core (€39/mo, €11.70/mo first 3 months) includes 600M base allowance tokens (all Core models included at €0.065/1M standard or €0.0195/1M in the first 3 months) with european zero data retention. Conversely, when we routed queries through `brick-complexity-pro` with thinking turned off on lookups in configuration c, median latency dropped to 1.95 seconds (7.8x faster) and completion tokens fell by 60.8 percent, but the agent still transmitted 184,765 uncompressed prompt tokens across the network. Combining both layers in configuration d cut prompt tokens by 95.1 percent, reduced completion tokens by 53.5 percent, brought median latency down to 1.40 seconds, and answered all four multi-file bug diagnosis questions with 100 percent accuracy. ### Define data boundaries and privacy Both `context-mode` and `headroom` compress files locally on your workstation, but compressed code excerpts still travel to your inference endpoint. Always set `HEADROOM_BEACON=off` to disable anonymous telemetry and point your requests to regolo's european zero-data-retention api so proprietary source code and incident logs are never stored or used for training. --- ## What you take away: evaluation kpis and practical workflows for client projects Beyond the immediate drop in monthly token spend, this architecture gives you a repeatable methodology you can bring to any software repository. Instead of treating coding agents as unpredictable black boxes that occasionally time out or burn through an api budget in an afternoon, you leave with a local filtering and ast compression layer that plugs into opencode, claude code, or cursor in ten minutes, paired with a deterministic evaluation script (`benchmark.py`) that proves whether a routing or compression change preserves answer quality before your team relies on it. When you calibrate this pipeline on your own codebase, four operational metrics tell you immediately whether your setup is balanced; the visual scorecard below summarizes the target thresholds we use in production and the exact adjustment to make if a metric drifts out of bounds: KPI 01 · INPUT -70% to -95% Prompt compression ratio Measured at -95.1% (184.7K → 9.1K tokens). If reduction falls below 50%, lower `top_k` in RRF or add noisy log keys to `crush_json_logs`. KPI 02 · THINKING 0 tok on lookup Thinking-to-answer ratio File and constant lookups should emit 20–35 completion tokens via `reasoning_effort="none"`, reserving 700–1,400 thinking tokens for bug diagnosis. KPI 03 · SPEED < 1.50 s p50 Interactive query latency Measured at 1.40s overall p50 (0.37s–0.87s on lookups). Keeps engineers in flow instead of waiting 15 seconds for every file check. KPI 04 · QUALITY >= 90% pass Ground-truth accuracy 100% pass rate on multi-file bug diagnosis. Verify exact error codes, identifiers, and constants in `test_benchmark.py` before deploying. ### How agencies and devops teams use this workflow with clients **Consider a common scenario for a software house or technical consultancy:** a client hands over an undocumented 100,000-line legacy repository and asks for a fixed-price modernization quote and security audit within forty-eight hours. Under a traditional workflow, senior engineers spend days manually grepping files or running an unoptimized coding agent that dumps 700 kilobytes of raw source code into a metered cloud api on every turn, running up hundreds of dollars in token charges while exposing client code to non-european endpoints. With the pipeline from this guide, your team indexes the client repository locally with `context-mode` in thirty seconds, wraps the agent with `headroom`, and routes inference through a [regolo core or boost subscription](https://regolo.ai/pricing/). You map the entire service topology, database mutations, and concurrency bugs in a single afternoon for less than €0.50 of pool allowance, and you attach [regolo's european zero-data-retention certificate](https://regolo.ai/zero-data-retention/) to your audit deliverable so the client's compliance officer has written proof that no source code was stored or used for model training. WITHOUT REGOLO · UNOPTIMIZED CLIENT AUDIT#### Raw file reads on metered cloud APIs **1. Context saturation in 20 minutes**40+ raw `Read()` calls dump 184,753 tokens of boilerplate and compliance headers into every session. **2. Unconstrained thinking on simple lookups**The flagship model spends 15.16s (p50) deliberating over basic constant and route checks. **3. Unpredictable margin erosion & NDA friction**Metered output billing costs $315–$498 per 1,000 sessions and requires cross-border data transfer reviews. 1,000 audit sessions**$310.22 – $486.70** WITH REGOLO · CONTEXT-MODE + HEADROOM + BRICK PRO#### Local RRF/AST compression + Sovereign EU pool **1. 95.1% smaller prompts on your laptop**RRF selects only the 4 participating modules and strips AST boilerplate down to 9,103 tokens. **2. Sub-second lookups + deep bug diagnosis**`brick-complexity-pro` answers lookups in 0.37s–0.87s and escalates race conditions to `qwen3.5-122b` / `glm-5.3`. **3. Fixed retainer margin & certified EU ZDR**Runs inside Regolo Core (€39/mo, €11.70 promo) with a signed European Zero Data Retention certificate for the client. 1,000 audit sessions**€0.97 (€0.29 promo)** The same mechanics apply when managed devops and sre teams respond to live production incidents for clients: during an outage, application logs are flooded with tens of thousands of routine healthchecks that obscure the two or three lines where a distributed lock expired or a transaction double-charged a customer. Feeding those raw log files into a metered model triggers 2x to 5x long-context surcharges while slowing down root-cause analysis: running the log stream through `crush_json_logs` before calling regolo collapses the 180 routine healthchecks into a single counter while preserving the exact `WARN` and `FATAL` payloads byte-for-byte, as shown in the live trace from question 6 and question 8 of our benchmark: #### Live trace: diagnosing a client production double-debit from noisy SRE logs QUESTION 6 & 8 · 100% ACCURACY IN 0.87s 1. Raw client log dump (182 events · ~14.2 KB) {"ts":"10:00:00Z","level":"INFO","event":"healthcheck_ok","latency_ms":2} {"ts":"10:00:01Z","level":"INFO","event":"healthcheck_ok","latency_ms":2} ... \[178 identical INFO healthcheck\_ok rows\] ... {"ts":"10:04:12Z","level":"WARN","event":"lock\_ttl\_exceeded","settlement\_ms":1840,"redis\_lock\_ttl\_ms":1200} {"ts":"10:04:12Z","level":"FATAL","error\_code":"ERR\_DOUBLE\_DEBIT\_77","event\_id":"evt\_9941"} → 2. Crushed payload sent to Regolo (280 bytes · -98%) {"service":"billing-webhook-ingestor", "collapsed_routine_events":{"healthcheck_ok":180}, "critical_events":[ {"level":"WARN","event":"lock\_ttl\_exceeded","settlement\_ms":1840,"redis\_lock\_ttl\_ms":1200}, {"level":"FATAL","error\_code":"ERR\_DOUBLE\_DEBIT\_77","event\_id":"evt\_9941"} ]} ✓ Regolo diagnosis (1.40s p50): Bank settlement took 1840ms, exceeding REDIS_LOCK_TTL_MS (1200ms) on evt_9941 and triggering ERR_DOUBLE_DEBIT_77. Finally, if you build white-label pull-request review bots or internal codebase assistants sold to enterprise clients on a fixed monthly retainer, embedding `compressor.py` in front of a regolo subscription pool protects your gross margin. Even when a client's engineering team pushes a massive refactoring pull request that triggers dozens of automated review turns, local ast compression and `brick-complexity-pro` routing keep token consumption bounded inside your flat €39 or €89 monthly pool, turning an unpredictable pass-through api expense into a high-margin managed service. FOR OPENCODE, CLAUDE CODE & CURSOR DEVELOPERS ### Run daily coding agents without 5-hour rate caps or $315 token bills Pair local `context-mode` and `headroom` compression with Regolo Core for €11.70/month during your first 3 months (€39/mo standard). Get up to 1.2 billion routed tokens per month via `brick-complexity-pro` (€0.033/1M) plus full access to `glm-5.3`, `qwen3.5-122b`, and `qwen3.8-27b`. 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