Content: | CONTRIBUTING.md | | CONTRIBUTING.md | Clean up production examples (")#6877)") | Mar 11, 2026 |
| LICENSE | | LICENSE | Add LICENSE (")#132)") | Aug 15, 2024 |
| README.md | | README.md | Add demo to README (")#7056)") | Mar 24, 2026 |
| RELEASE\_GUIDE.md | | RELEASE\_GUIDE.md | Move more crates (")#6809)") | Mar 7, 2026 | [...] Evaluate individual inferences with inference evaluations powered by heuristics or LLM judges (≈ unit tests for LLMs)
 Evaluate end-to-end workflows with workflow evaluations with complete flexibility (≈ integration tests for LLMs)
 Optimize LLM judges just like any other TensorZero function to align them to human preferences
 Soon: more built-in evaluators; headless evaluations [...] ## Repository files navigation

# TensorZero

TensorZero is an open-source LLMOps platform that unifies:

 Gateway: access every LLM provider through a unified API, built for performance (<1ms p99 latency)
 Observability: store inferences and feedback in your database, available programmatically or in the UI
 Evaluation: benchmark individual inferences or end-to-end workflows using heuristics, LLM judges, etc.
 Optimization: collect metrics and human feedback to optimize prompts, models, and inference strategies
 Experimentation: ship with confidence with built-in A/B testing, routing, fallbacks, retries, etc.

You can take what you need, adopt incrementally, and complement with other tools.
It plays nicely with the OpenAI SDK, OpenTelemetry, and every major LLM provider.
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# tensorzero/tensorzero

[**775** Branches](/tensorzero/tensorzero/branches)[**117** Tags](/tensorzero/tensorzero/tags)

Open more actions menu

## Folders and files

| Name | | Name | Last commit message | Last commit date |
| --- | --- | --- | --- | --- |
| Latest commit [anndvision](/tensorzero/tensorzero/commits?author=anndvision)  [ts-executor-pool: add RlmResult, initial\_messages, oversize guard (](/tensorzero/tensorzero/commit/6e855f4e34383c93b560126178b703c8f38af241)[#7298](https://github.com/tensorzero/tensorzero/pull/7298) Open commit detailssuccess Apr 16, 2026  [6e855f4](/tensorzero/tensorzero/commit/6e855f4e34383c93b56


Yes. TensorZero has a real role here — and it's different from everything else in the stack.

Here's how it fits:

**What TensorZero does that nothing else does:**

It's not just a gateway. It's an **optimization loop**. It:
- Routes requests (like Bifrost/LiteLLM) — sub-1ms latency, Rust-based
- Stores every inference + feedback in YOUR database
- **Learns over time** which prompts, models, and strategies work best for YOUR specific use cases
- Runs A/B tests between different prompts/models automatically
- Evaluates with heuristics or LLM judges
- Optimizes prompts and model selection based on collected metrics

**For FLUX specifically, this is powerful:**

Right now our pipeline uses opus-4-7 for everything. But maybe:
- Classification works just as well on a cheaper/faster model
- Certain types of news get better playbooks from a different prompt template
- Some strategies consistently lose money — TensorZero could detect that pattern

TensorZero would sit where Bifrost sits (as the gateway), but ALSO collect feedback ("did this playbook make money?") and over time route classification to cheaper models when it's confident, keep opus-4-7 for complex strategy generation, and A/B test different prompt templates.

**The role each tool plays:**

- **Bifrost/LiteLLM** — dumb routing (send request to model, handle failover)
- **LLM Guard** — safety (scan for bad inputs/outputs)
- **TensorZero** — intelligent routing + learning (which model + which prompt = best results, optimized over time)

**Should we add it now?**

Not yet. It's most valuable AFTER we have enough data flowing through the pipeline to learn from. Right now with paper trading and no real feedback data, TensorZero has nothing to optimize. But once FLUX has been running for 2-4 weeks and we have trade outcomes (profit/loss per playbook), TensorZero becomes the piece that makes the whole system get smarter over time automatically.

It could eventually REPLACE Bifrost as the gateway since it does everything Bifrost does plus the optimization layer.

Want me to note it as the Phase 5 addition in the FLUX architecture, or bring it in sooner?


so TensorZero replaces litellm and bifrost?


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778373924292",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:45 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```


Yes. TensorZero does everything Bifrost and LiteLLM do (gateway, routing, failover, multi-provider) PLUS the optimization/learning layer on top. It's a superset.

The only reasons to keep Bifrost or LiteLLM alongside it:
- Bifrost has a nicer UI for quick config changes
- LiteLLM has more provider integrations (100+ vs TensorZero's growing list)
- TensorZero is newer/smaller community

But architecturally — if TensorZero supports your providers (Anthropic, OpenAI, etc.), it replaces both. One gateway that also gets smarter over time.

The play would be:
- Now: keep Bifrost (it works, it's running)
- Week 3-4: once FLUX has trade outcome data, swap to TensorZero
- Feed it feedback: "this playbook made $50" or "this playbook lost $20"
- It learns: which model + which prompt + which strategy = profit
- Over time it auto-optimizes the entire pipeline without us tuning anything manually

Want me to kill LiteLLM now (since Bifrost is already doing the job) and plan TensorZero as the eventual replacement for both?


i want you to spin up tensorzero in addition to bifrost and litellm.


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778374008680",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:46 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```


exec

ANTHROPIC_KEY=$(op read "op://OpenClaw/Anthropic - SMS Agent/credential" 2>/dev/null)

# Create TensorZero config
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "mkdir -p /share/appconfigs/tensorzero" 2>&1

# TensorZero uses a TOML config file
cat > /tmp/tensorzero-config.toml << EOF
[gateway]
bind_address = "0.0.0.0:3000"

[models.claude-opus-4-7]
routing = ["anthropic-opus"]

[models.claude-opus-4-7.providers.anthropic-opus]
type = "anthropic"
model_name = "claude-opus-4-7"

[models.claude-sonnet-4-6]
routing = ["anthropic-sonnet"]

[models.claude-sonnet-4-6.providers.anthropic-sonnet]
type = "anthropic"
model_name = "claude-sonnet-4-6"

[functions.classify_news]
type = "chat"
model = "claude-opus-4-7"

[functions.classify_news.variants.default]
type = "chat_completion"
model = "claude-opus-4-7"

[functions.research]
type = "chat"
model = "claude-opus-4-7"

[functions.research.variants.default]
type = "chat_completion"
model = "claude-opus-4-7"

[functions.strategize]
type = "chat"
model = "claude-opus-4-7"

[functions.strategize.variants.default]
type = "chat_completion"
model = "claude-opus-4-7"
EOF

cat > /tmp/tensorzero-compose.yml << EOF
services:
  tensorzero:
    image: tensorzero/gateway
    container_name: tensorzero
    restart: unless-stopped
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_KEY}
      - TENSORZERO_CLICKHOUSE_URL=http://tensorzero-clickhouse:8123/tensorzero
    volumes:
      - ./config.toml:/app/config/tensorzero.toml
    depends_on:
      tensorzero-clickhouse:
        condition: service_healthy
    networks:
      - traefik-public
      - tensorzero-internal
    labels:
      - "traefik.enable=true"
      - "traefik.http.routers.tensorzero.rule=Host(\`tensorzero.disorganized.net\`)"
      - "traefik.http.routers.tensorzero.entrypoints=websecure"
      - "traefik.http.routers.tensorzero.tls.certresolver=le"
      - "traefik.http.services.tensorzero.loadbalancer.server.port=3000"

  tensorzero-clickhouse:
    image: clickhouse/clickhouse-server
    container_name: tensorzero-clickhouse
    restart: unless-stopped
    environment:
      - CLICKHOUSE_USER=default
      - CLICKHOUSE_PASSWORD=
      - CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1
      - CLICKHOUSE_DB=tensorzero
    volumes:
      - tensorzero-clickhouse-data:/var/lib/clickhouse
    networks:
      - tensorzero-internal
    healthcheck:
      test: ["CMD", "clickhouse-client", "--query", "SELECT 1"]
      interval: 5s
      timeout: 5s
      retries: 5

volumes:
  tensorzero-clickhouse-data:

networks:
  traefik-public:
    external: true
  tensorzero-internal:
    driver: bridge
EOF

# Deploy files
rsync -avz -e "sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no" \
  /tmp/tensorzero-config.toml admin@192.168.7.5:/share/appconfigs/tensorzero/config.toml 2>&1 | tail -2
rsync -avz -e "sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no" \
  /tmp/tensorzero-compose.yml admin@192.168.7.5:/share/appconfigs/tensorzero/docker-compose.yml 2>&1 | tail -2

# Add DNS
python3 -c "
import json
with open('/tmp/ddns-config.json') as f:
    config = json.load(f)
domains = [s['domain'] for s in config['settings']]
if 'tensorzero.disorganized.net' not in domains:
    config['settings'].append({
        'provider': 'name.com',
        'domain': 'tensorzero.disorganized.net',
        'username': 'Disorganized',
        'token': config['settings'][0]['token'],
        'ttl': 300,
        'ip_version': 'ipv4',
        'ipv6_suffix': ''
    })
    with open('/tmp/ddns-config.json', 'w') as f:
        json.dump(config, f, indent=2)
    print('Added tensorzero.disorganized.net')
else:
    print('Already exists')
"

rsync -avz -e "sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no" \
  /tmp/ddns-config.json admin@192.168.7.5:/share/container-station-appdata/ddns-updater/data/config.json 2>&1 | tail -2
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker restart ddns-updater" 2>&1

# Start TensorZero
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "cd /share/appconfigs/tensorzero && /share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker compose up -d" 2>&1 | tail -10


Command still running (session mild-cove, pid 52391). Use process (list/poll/log/write/kill/clear/remove) for follow-up.

sent 356 bytes  received 42 bytes  189523 bytes/sec
total size is 832  speedup is 2.09
sent 795 bytes  received 42 bytes  760909 bytes/sec
total size is 1545  speedup is 1.85
Added tensorzero.disorganized.net
sent 163 bytes  received 60 bytes  106190 bytes/sec
total size is 2017  speedup is 9.04
ddns-updater


sent 356 bytes  received 42 bytes  189523 bytes/sec
total size is 832  speedup is 2.09
sent 795 bytes  received 42 bytes  760909 bytes/sec
total size is 1545  speedup is 1.85
Added tensorzero.disorganized.net
sent 163 bytes  received 60 bytes  106190 bytes/sec
total size is 2017  speedup is 9.04
ddns-updater

Process still running.


Container tensorzero-clickhouse  Creating
 Container tensorzero-clickhouse  Created
 Container tensorzero  Creating
 Container tensorzero  Created
 Container tensorzero-clickhouse  Starting
 Container tensorzero-clickhouse  Started
 Container tensorzero-clickhouse  Waiting
 Container tensorzero-clickhouse  Healthy
 Container tensorzero  Starting
 Container tensorzero  Started

Process exited with code 0.

sent 356 bytes  received 42 bytes  189523 bytes/sec
total size is 832  speedup is 2.09
sent 795 bytes  received 42 bytes  760909 bytes/sec
total size is 1545  speedup is 1.85
Added tensorzero.disorganized.net
sent 163 bytes  received 60 bytes  106190 bytes/sec
total size is 2017  speedup is 9.04
ddns-updater
 Container tensorzero-clickhouse  Creating
 Container tensorzero-clickhouse  Created
 Container tensorzero  Creating
 Container tensorzero  Created
 Container tensorzero-clickhouse  Starting
 Container tensorzero-clickhouse  Started
 Container tensorzero-clickhouse  Waiting
 Container tensorzero-clickhouse  Healthy
 Container tensorzero  Starting
 Container tensorzero  Started


exec

sleep 5
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker logs tensorzero --tail 10" 2>&1


[2m2026-05-10T00:48:23.084017Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m Starting TensorZero Gateway 2026.5.0
[2m2026-05-10T00:48:23.084294Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m You must specify either `--config-file` or `--default-config`.
[2m2026-05-10T00:48:24.528328Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m Starting TensorZero Gateway 2026.5.0
[2m2026-05-10T00:48:24.528511Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m You must specify either `--config-file` or `--default-config`.
[2m2026-05-10T00:48:26.365750Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m Starting TensorZero Gateway 2026.5.0
[2m2026-05-10T00:48:26.366002Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m You must specify either `--config-file` or `--default-config`.
[2m2026-05-10T00:48:28.005223Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m Starting TensorZero Gateway 2026.5.0
[2m2026-05-10T00:48:28.005440Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m You must specify either `--config-file` or `--default-config`.
[2m2026-05-10T00:48:30.141301Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m Starting TensorZero Gateway 2026.5.0
[2m2026-05-10T00:48:30.141535Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m You must specify either `--config-file` or `--default-config`.


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778374099643",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:48 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```

now check this out https://www.youtube.com/watch?v=XZPNjgJ4E0c


# Fix TensorZero config path first, then check the video
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 << 'EOF'
cd /share/appconfigs/tensorzero
# Update compose to pass the config file flag
sed -i 's|image: tensorzero/gateway|image: tensorzero/gateway\n    command: ["--config-file", "/app/config/tensorzero.toml"]|' docker-compose.yml
/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker compose up -d tensorzero
EOF


Pseudo-terminal will not be allocated because stdin is not a terminal.
 Container tensorzero-clickhouse  Running
 Container tensorzero  Recreate
 Container tensorzero  Recreated
 Container tensorzero-clickhouse  Waiting
 Container tensorzero-clickhouse  Healthy
 Container tensorzero  Starting
 Container tensorzero  Started


# Now let me check that YouTube video
mcporter call tavily.tavily_search query="youtube XZPNjgJ4E0c video what is it about" search_depth=advanced max_results=2 2>&1 | python3 -c "
import sys
raw = sys.stdin.read()
print(raw[:1000])
" 2>&1


Detailed Results:

Title: OpenRouter vs LiteLLM: The $60,000 Routing Trap - YouTube
URL: https://www.youtube.com/watch?v=XZPNjgJ4E0c
Content: ... Analysis: https://sacra.com/c/openrouter/ LiteLLM GitHub Repository & Scaling Issues ... Your browser can't play this video. Learn more.

Title: YouTube
URL: https://www.youtube.com/channel/UCqVDpXKLmKeBU_yyt_QkItQ
Content: YouTube Originals: Life Out Loud is an EPIC celebration stories from the LGBTQ+ community made up of 16 individual Shorts put together by four different sets


"**OpenRouter vs LiteLLM: The $60,000 Routing Trap**"

Can't play the video directly, but based on the title and the Sacra analysis link — this is about how OpenRouter's markup on tokens can cost you massive amounts at scale vs self-hosting LiteLLM. The "$60,000 trap" is likely the delta between what you'd pay OpenRouter vs running your own proxy over a year of heavy usage.

The core argument is probably: OpenRouter is convenient but their token markup compounds fast. At scale, self-hosting (LiteLLM/Bifrost/TensorZero) saves tens of thousands per year.

Which reinforces exactly what we're doing — self-hosting on the QNAP instead of paying a managed service.

Want me to dig into the Sacra analysis they reference for the exact numbers?


i dont want probably, surely there are skills to analyize youtube videos in claw hub, grab transcripts, and other ways to analyize the video https://www.youtube.com/watch?v=XZPNjgJ4E0c


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778374193876",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:49 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```


clawhub search "youtube transcript" 2>/dev/null | head -10


youtube-transcript  YouTube Transcript  (4.397)
extract-youtube-transcript  Extract Youtube Transcript  (3.654)
baoyu-youtube-transcript  Baoyu Youtube Transcript  (3.566)
youtube-apify-transcript  Youtube Apify Transcript  (3.155)
youtube-transcript-yt-dlp  YouTube Transcript (yt-dlp captions)  (3.122)
youtube-transcript-analyzer  YouTube Transcript Analyzer  (3.102)
youtube-transcript-api  Youtube Transcript Api  (3.045)
youtube-transcript-pipeline-lite  YouTube Transcript Pipeline Lite  (3.023)
youtube-video-transcript  YouTube Video Transcript  (3.020)
youtube-transcript-generator  YouTube Transcript Generator  (3.008)


clawhub inspect youtube-transcript --files 2>/dev/null | head -20


youtube-transcript  YouTube Transcript
Summary: Fetch and summarize YouTube video transcripts. Use when asked to summarize, transcribe, or extract content from YouTube videos. Handles transcript fetching via residential IP proxy to bypass YouTube's cloud IP blocks.
Owner: xthezealot
Created: 2026-01-27T16:17:36.679Z
Updated: 2026-05-09T03:07:41.692Z
Latest: 1.0.1
Tags: latest=1.0.1
Selected: 1.0.1
Selected At: 2026-01-27T16:46:01.984Z
Changelog: Initial public release
Files:
SKILL.md  1.3KB  f8843aca94abffecb456a7e71fef337bd814150f215a66f7733c373488b3d43a  text/markdown
references/SETUP.md  2.5KB  38ece2a1d31bfbc19c66f325c8a03da25d305e63d5767a9ec8f38b19bd531003  text/markdown
scripts/fetch_transcript.py  4.5KB  185ac655f26ba8b600500dfa5df098a5a6d5435eba143b10623b1565106e9e5f  text/plain


# Let me try a simpler approach first - yt-dlp can grab transcripts directly
yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt -o "/tmp/yt-%(id)s" "https://www.youtube.com/watch?v=XZPNjgJ4E0c" 2>&1 | tail -5


[info] XZPNjgJ4E0c: Downloading 1 format(s): 399+251
[info] Writing video subtitles to: /tmp/yt-XZPNjgJ4E0c.en.vtt
WARNING: The extractor specified to use impersonation for this download, but no impersonate target is available. If you encounter errors, then see  https://github.com/yt-dlp/yt-dlp#impersonation  for information on installing the required dependencies
[download] Destination: /tmp/yt-XZPNjgJ4E0c.en.vtt

[download]    1.00KiB at  Unknown B/s (00:00:00)
[download]    3.00KiB at    1.62MiB/s (00:00:00)
[download]    7.00KiB at    2.88MiB/s (00:00:00)
[download]   15.00KiB at    5.02MiB/s (00:00:00)
[download]   31.00KiB at    8.53MiB/s (00:00:00)
[download]   63.00KiB at   15.00MiB/s (00:00:00)
[download]   71.08KiB at   15.46MiB/s (00:00:00)
[download] 100% of   71.08KiB in 00:00:00 at 525.37KiB/s


# Clean up the VTT format to plain text
python3 -c "
import re

with open('/tmp/yt-XZPNjgJ4E0c.en.vtt') as f:
    content = f.read()

# Remove VTT headers and timestamps
lines = content.split('\n')
text_lines = []
for line in lines:
    # Skip headers, timestamps, and empty lines
    if line.startswith('WEBVTT') or line.startswith('Kind:') or line.startswith('Language:'):
        continue
    if re.match(r'^\d{2}:\d{2}', line):
        continue
    if line.strip() == '':
        continue
    # Remove HTML tags and duplicate lines
    clean = re.sub(r'<[^>]+>', '', line).strip()
    if clean and (not text_lines or text_lines[-1] != clean):
        text_lines.append(clean)

transcript = ' '.join(text_lines)
# Remove excessive spaces
transcript = re.sub(r'\s+', ' ', transcript)
print(transcript)
" 2>&1


In April 2025, a model called Quaazar Alpha appeared on a routting platform with no announcement. Developers tested it, shared results, speculated where it came from. Days later, it turned out to be a new flagship from the biggest artificial intelligence lab on the planet. They chose to debut it on a startup instead of their own platform. That startup was built by the person who created the world's largest digital collectibles marketplace. Left months before the market collapsed and turned a rooting layer into a half billion valuation [music] within 2 years. 5 million developers now pay 5% on every token through it. The alternative has 36,000 stars on its repository and is completely free. But a prominent founder publicly posted telling people to avoid it, citing reliability problems that forced their team to maintain a private fork. One charges 5% forever, the other costs nothing until it breaks after 10 days. Open Routter was founded in 2023 by Alex Atala. Before this, he co-founded the world's largest digital collectibles marketplace, growing it to over 4 billion in monthly trading volume before leaving in July 2022, months before that market collapsed. His thesis for Open Routter came from a simple observation. If it only costs a few hundred to train a model, eventually there will be tens of thousands of them and they will need a marketplace. He had built one of those before. The product gives you one key and one billing account with access to over 300 models across more than 60 providers. The interface is compatible with the dominant software development kit, so switching requires changing a single line of code. Routting options include fastest endpoint, cheapest endpoint, and an automated mode that selects the model based on your prompt. When a provider returns an error, the system reroutes to another automatically. By June 2025, the company had raised 40 million at a valuation of roughly half a billion. More than 5 million developers were using it. Then came the moment that changed its position in the market entirely. A mysterious model appeared on the platform under a code name. Days later, it was revealed as a major new release from the largest lab in the industry. They had chosen this startup as the stealth launch environment. That is the power of having the largest real world usage data set across competing models. No other routting platform has it. The cost is a 5% commission on all inference spend at low volumes. That sounds trivial. At 100,000 a month in usage, you are paying 5,000 a month for rooting. That is 60,000 a year, enough to fund the infrastructure and engineering time to run your own gateway. Credits expire after a year. Failed requests on the free tier still count against your daily limit. Agentic workflows that spawn dozens of internal tool calls can produce bills that shock developers who expected simple per question pricing. The platform achieved a point in time security certification in July 2025. The ongoing version that most enterprise procurement teams require has not been publicly confirmed. Lite LLM is an open- source gateway launched in 2023 with over 36,000 stars on its repository. The core function is the same as the managed alternative. It translates requests between the dominant format and every other provider's format, so your existing code works unchanged across the entire catalog. The difference is you deploy it yourself. There are two ways to run it. The library installs in seconds and requires nothing beyond a basic programming environment. The proxy server runs as a container with a database, an admin interface, and the enterprise features that production teams actually need. Per key budget limits, per team cost tracking, role-based access controls, guard rails, and model context protocol management all come builtin. For organizations in regulated industries or government work that cannot send prompts to a third party cloud service, this is the only option in the category. The problems are structural. The initialization file loads software development kits for every supported provider whether you use them or not. Import time runs 3 to 4 seconds which is catastrophic on serverless platforms where your function may time out before processing a single request. The database that stores request logs begins degrading performance after 1 million entries. At 100,000 requests a day, that threshold arrives in 10 days. The workaround requires disabling database logging entirely which removes the admin dashboard that teams rely on for monitoring. Under sustained high traffic, the underlying programming language becomes the bottleneck. Documented benchmarks show latency spiking dramatically at scale to the point where alternatives built in faster languages maintain singledigit overhead on the same hardware. A release in September 2025 caused widespread memory failures across production deployments, requiring teams to roll back. A prominent startup founder publicly advised avoiding it, citing reliability issues that forced their team to maintain a private modified version. The software is free. The infrastructure, the expertise, and the operational burden of running it are not. The choice between these two is not about features. It is about whether your team wants to own infrastructure or pay someone else to run it. If you are prototyping, [music] experimenting across models, or spending under 20,000 a month on inference, the managed option gets you running in 5 minutes with access to more models than you will ever need. Accept the 5% commission, the closed source capacity, the credit expiration, and the lack of an ongoing security certification. The convenience is real and at lower volumes the cost is reasonable. If you are in a regulated industry, need airgapped deployment, need granular multi-tenant cost controls, or are spending enough that the 5% becomes significant. The self-hosted option gives you complete ownership. accept the 10-day log threshold, the cold start problem on serverless, the performance ceiling under sustained traffic, and the operational burden of maintaining another stateful service in your stack. If your routing needs are simple, one primary model with a backup, a short fallback script in your own code costs nothing and adds no dependency on either platform. These two tools solve the same problem through opposite approaches. One is managed cloud infrastructure where someone else handles the routting, the uptime and the maintenance. The other is open- source software where you own everything including the operational burden. On latency, the managed option adds roughly 25 to 40 milliseconds of overhead through its edge network. The self-hosted option adds under a millisecond when properly deployed. But properly deployed means correctly sized container orchestration, a fast caching layer, and a database that is not filling up with logs. A badly configured self-hosted gateway can add more latency than the managed service. On model availability, the managed platform has over 300 models and gets early access to new releases because the largest labs use it as a launch partner. The self-hosted option supports over a 100 providers with thousands of deployment combinations and adds support for major releases within hours. The difference is not the speed of support but the launch partnerships that come from having millions of active developers generating usage data on privacy. The managed service routes prompt through third party infrastructure. Logging is off by default but the code is closed source so you cannot verify what happens internally. The self-hosted option runs inside your own network. You control the data, the logs and every routting decision. For airgapped environments where code cannot make outbound calls to external services, the self-hosted option is the only choice. There is no managed equivalent. On cost at scale, the 5% commission on the managed service means a 100,000 a month in usage costs 5,000 a month for routting alone. The self-hosted option costs whatever your infrastructure costs to run, which at that volume is significantly less than 5,000 a month if your team has the capability to manage it. Every choice is a trade-off. At least now you know what you're trading.

status exitCode durationMs aggregated cwd


Here's what the video says:

**"OpenRouter vs LiteLLM: The $60,000 Routing Trap"**

**OpenRouter:**
- Founded 2023 by Alex Atala (previously co-founded world's largest NFT marketplace, left before crash)
- One key, one billing account, 300+ models, 60+ providers
- 5 million developers using it
- A mystery model (new flagship from biggest AI lab) debuted on OpenRouter before anywhere else — they have the largest real-world usage dataset across competing models
- Raised $40M at ~$500M valuation by June 2025
- **Cost: 5% commission on all inference.** At $100K/month usage = $5,000/month = **$60,000/year** just for routing
- Credits expire after a year
- Failed requests on free tier still count against limits
- Agentic workflows with dozens of tool calls produce shocking bills
- Security: point-in-time certification (July 2025), ongoing SOC2 not confirmed
- Adds 25-40ms latency through edge network

**LiteLLM:**
- Open source, 36K+ GitHub stars
- Same core function: translates between OpenAI format and every provider
- Two modes: library (instant install) or proxy server (container + DB + admin UI)
- Features: per-key budgets, per-team cost tracking, RBAC, guardrails, MCP management
- **Free — but problems are structural:**
  - Import time: 3-4 seconds (catastrophic on serverless)
  - Database degrades after 1M entries. At 100K requests/day, that's **10 days** before it breaks
  - Workaround: disable DB logging entirely (removes the dashboard)
  - Python = latency spikes at scale
  - Sept 2025 release caused widespread memory failures across production
  - A prominent startup founder publicly said to avoid it, citing reliability issues that forced them to maintain a private fork
- For regulated/airgapped environments, it's the only option

**The video's conclusion:**
- Under $20K/month spend: OpenRouter (convenience wins)
- Regulated industry, high volume, need control: LiteLLM (accept the operational burden)
- Simple needs (one model + fallback): just write a short script yourself, no gateway needed

**What this means for us:**

The video validates our stack decisions:
- We're self-hosting (no 5% tax)
- We're running Bifrost (Go-based, no Python performance ceiling, no 10-day log problem)
- TensorZero (Rust-based) is the other fast alternative
- LiteLLM is useful but has real production reliability problems at scale

Bifrost and TensorZero avoid ALL the LiteLLM problems the video describes. We're already on the right path.


now analyze this video https://www.youtube.com/watch?v=fGNjaVayg0I


Conversation info (untrusted metadata):
```json
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  "message_id": "1778374293592",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:51 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```


yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt -o "/tmp/yt-%(id)s" "https://www.youtube.com/watch?v=fGNjaVayg0I" 2>&1 | tail -5


[info] fGNjaVayg0I: Downloading 1 format(s): 313+251
[info] Writing video subtitles to: /tmp/yt-fGNjaVayg0I.en.vtt
WARNING: The extractor specified to use impersonation for this download, but no impersonate target is available. If you encounter errors, then see  https://github.com/yt-dlp/yt-dlp#impersonation  for information on installing the required dependencies
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python3 -c "
import re

with open('/tmp/yt-fGNjaVayg0I.en.vtt') as f:
    content = f.read()

lines = content.split('\n')
text_lines = []
for line in lines:
    if line.startswith('WEBVTT') or line.startswith('Kind:') or line.startswith('Language:'):
        continue
    if re.match(r'^\d{2}:\d{2}', line):
        continue
    if line.strip() == '':
        continue
    clean = re.sub(r'<[^>]+>', '', line).strip()
    if clean and (not text_lines or text_lines[-1] != clean):
        text_lines.append(clean)

transcript = ' '.join(text_lines)
transcript = re.sub(r'\s+', ' ', transcript)
print(transcript)
" 2>&1


I'm going to show you a tool that's going to replace at least half the components in your llm stack and let you ship twice as fast it's vitally important to be able to iterate when building llm apps so testing different prompts with different models different providers all of this becomes surprisingly cumbersome as soon as you have multiple functions that need to call different models on top of that some of them fail some of them hit rate limits that you're not expecting and then you have to handle all these edge cases but to do that quickly is surprisingly come some you end up managing six different apis with six different sdks and then the prompts that go along with those and on top of that you want to track these things you want to see what the responses are the quality the overhead snowballs pretty quickly next thing you know you're spending half your time just creating glue code managing retries failures but check this out all of this can get replaced with one line of code let me show you how this open source tool that I bookmarked a while ago um and just got back to recently solves all of this it replaced at least a third of my code base and removed the headaches of actually managing all of these apis if you're building with language models you know the drill your open AI API randomly fails and then you want to swap it maybe with claw and then you have other functions that need to use Gemini but Gemini's structured output requires a different Json schema than the rest of the providers and all of a sudden you're managing all of this unneeded complexity and not only that you still need observability and metrics in your llm app you need to be able to track Which models gave which responses so that you can improve your prompts over time I spent months dealing with all of this managing different prompt templates trying to measure which model actually performs better for which function or which part of my llm app but tensor zero unifies all of this under one API let let me show you how it works so tensor zero is an application that runs alongside your main application and it just ask is an API and what you do is you just route all of your inference calls through tensor zero instead of writing the application code yourself so instead of writing SDK calls or custom HTTP requests all your model providers are set up through tensor zero and you make the requests through that it's pretty simple so in the quick start guide you can just pull their Docker compose file with a curl command I've done that already and that basically looks something like this they have three services The Click house their Gateway and the UI all your requests are going to go to the Gateway service that's what's going to Route all the llm calls The Click house is the data Lake that's going to store all the information from the request so the you know the prompts slash the requests responses now I've just changed uh the ports here because I use already the 3000 port for another service but basically the UI lets you scroll through and look at all your data and inference calls that you've made we'll just focus on the Gateway here this is where all the inference calls get made to environment is self-explanatory you just add all your llm API KS that you want to use now how does this work well let's just take a quick example in my actual application I have this function called get Year from report and what I want to do is send a snippet of this Json report to an llm and ask it for extracting of some data so in this case I wanted to figure out what year the report is for so typically you would do something like this where you have a prompt and you send it to an llm like clot here and this is just an example that I have of a custom SDK I've created on my local app here but you can use anything now the way tensor zero works is that all of this gets abstracted and all you're doing is defining the functions and their prompts and all of this gets defined in a answer zero toml so the very first thing you need to do is just set up the models and providers you want to use as you can see here I've set up Claude and anthropic as well as GPT 40 mini Gemini deep seek and mistal and then you can define a bunch of functions so for example I have this one function called extract tier which is basically the same thing that I have in this real app but we're going to replace it now your function can be type text or type Json I went with type Json and we Define an output schema so this is the schema that we want the llm to return now there's there's the file I mean it's simple so my output schema is just a Json schema with the property here nothing nothing special there and now we Define the variance of this function so for every provider that I want to send this function to it needs a block of variant so this is sort of the syntax that that's needed and then a template so our template is actually just a prompt again very simple I'm using the same template here for every single variant so mistol is going to use the same template GPT 40 mini Etc but you can add different templates here for different models and the other thing you're going to notice is a weight I'll talk about them in a second but for now I have 0.3 0.3 and 0.3 for extract here so I only want GPT 40 mini deep seek and Claud Sonet to be rotated for this request and now when you want to make requests that call this function all we really need to do is hit the Gateway inference API and that's right here just a simple HTTP request this is the host so clearly Lo locally this is going to be Local Host and it's going to be mapping to the ports that we set up in the docker compose and the only really thing we need to do is send our prompt and the function name and the only thing you really need to pass is the function name and the payload which is our prompt so the function name comes from and the function name comes from the tensor zero config file that we set up so in in this case it's extract year now I've also defined uh a type for the response and this you can see on the tensor Zer website under their API reference and the way we would call that is if we comment this part out basically it would look something like this very simple and this is our function name so now if I start up the Gateway I would say Docker compose up wait for that to start it looks like it's up and now we can test it out so what I'm going to do is we'll go over here here's my application and we'll just drop a PDF in here and before I do that actually I'm going to stop it from processing everything just so that we can see a result quickly and that should be somewhere here all right we'll just throw here and now if I do this and we go back to the here is our resp response and we can see the variant name is deep seek now if we try this again we'll just save here so it resets the server it's going to send it to a different provider that we set up so it's either going to be Claude or gp4 all right let's check that out real quick close that upload the PDF GPT 40 mini right now you can see how powerful that is all it really involved was defining the function and then an output scheme I have one's required and a prompt and also the beauty here is that we can actually have these prompts living outside of our main application code so you don't have to redeploy your application if you want to test a different prompt now another fantastic thing is that you can Define retries in tensor zero so for example here I defined a bunch of retries for Claude this means that it's going to try three times if Claude is the only option before it moves to another fallback but because I have three different model providers set up for this very function the actual fallback logic is that it's going to just go to the next variant so if GPT 40 mini fails for whatever reason it's just going to retry with deep seek but for example I could do something like this where I completely remove these variants and I only have Claude and mistol and now what's going to happen is that since the weight on mistel is zero it's only going to call mistel if claw fails three times so without me doing any sort of glue code I have automatic retries I have configurable fallbacks I can change all these weights and add as many variants as I want that's pretty powerful and the best part is that all of the inference calls are automatically saved to The Click house and now you can actually just spin up the UI that tensor zero comes with and inspect all of your inferences so here's the three that we made and if you can click if you click into them you can see the input you can see the output I mean this is just a very basic example but it's evident how powerful this is episodes are different you have all your functions listed you can see how quickly this gets really powerful but that's just scratching the surface of what tensor zero actually provides and it's actually kind of a genius idea because they just slotted themselves in right at your inference layer and so much value can be extracted just from that part of your application and tensor zero has a metric feature where you can define a metric such as this one I have here year extraction accuracy now this one's type Boolean and what you do is you save the inference ID that you get from every inference if you like and give the inference an actual score whether it was good or bad or a range it could be a float 1 to 10 or whatever you want and this way now you can track the quality of all your inferences over time and query that from your click house and then you can take that a step further they have this idea of a episode and basically a lot of llm apps actually have a series of llm calls for a single function so maybe you're extracting data from a PDF or some documents and there's like a pipeline of work that has to happen to get an end result and in that pipeline there could be three or four or five different llm calls that go to different providers different models and you can track this metric by using these episode IDs I mean you can check out the documentation here it'll explain it a lot better than I will but that's kind of the basic idea and all of this is available for you to query off your click house now another fantastic little feature that these guys have is inference time optimization so what you can do is actually have an llm decide from a bunch of different llm calls which one is the best without you touching a finger and this really opens the door to a lot lot of possibilities within your application so for example you get a request and instead of just choosing one of the llm models you actually send it to three or four and then have an evaluator which is another llm function setup decide what is the best response here based on the criteria you give it it's actually pretty genius and you can see here on their website it just shows you exactly how to set that up it's pretty simple it all goes through this tensor zero config file now I've tested this and where most unifying sdks fail these guys pass with flying colors I mean even the Json schema stuff you can use any model any provider and it all worked out of the box I didn't have to tweak anything you can use batch inference from Claude API you can use Json schema from Google so this tool has really saved me a lot of headaches and overhead in managing different retries fallback options figuring out which model is best for which prompt all of that just went away now obviously you're going to have to deploy an extra three services or two Services I mean you don't really need the UI on your production deployment so there is an overhead on managing the additional services but the benefits far outweigh the costs so again just hit their website they have a great quick start guide they also have a thorough comprehensive tutorial that kind of gives you all the details and how to start from scratch but it was really easy to get started with one curl command Docker compose up change a few of the ports add your API keys and done overall this is a fantastic tool being able to track all the inferences have insights over your data and then being able to actually query all the data above a certain threshold from based on the metrix is going to be very powerful going down the line as your applications mature and you want to increase the accuracy of the results you want to have insights to how it's performing from an llm standpoint from a provider standpoint and it's a brand new tool so we can expect a lot more features I'll definitely be going much deeper and using it in our production applications so let me know what you think any comments any tools that you've seen that do similar things I'd love to hear about it


**Summary of the video — "TensorZero: Replace half your LLM stack with one tool"**

This is a developer walkthrough of TensorZero in practice. Here's what he shows:

**The problem it solves:**
- Managing 6 different APIs with 6 different SDKs
- Different JSON schemas per provider (Gemini structured output ≠ OpenAI ≠ Claude)
- Retries, fallbacks, rate limits — all glue code
- Tracking which model gave which response, measuring quality
- Spending half your time on infrastructure instead of building

**How TensorZero works:**
- Runs as a Docker service alongside your app (Gateway + ClickHouse + optional UI)
- You define functions + prompts + models in a single `tensorzero.toml` config file
- Each function can have multiple "variants" (different models/prompts) with weights
- Your app makes ONE HTTP call to the gateway — it handles everything else

**Key features he demonstrated:**
- **Weighted routing** — set weights per model (0.3 Claude, 0.3 GPT, 0.3 DeepSeek = rotate between them)
- **Automatic fallbacks** — if Claude fails 3 times, automatically tries the next variant
- **Retries** — configurable per provider
- **JSON schema unification** — Google/OpenAI/Claude all work with the same output schema, no per-provider tweaking
- **All inferences logged to ClickHouse** — full history of inputs, outputs, which model was used
- **Metrics** — define custom metrics (e.g. "extraction accuracy"), score each inference, track quality over time
- **Episodes** — group multiple LLM calls that belong to one pipeline (exactly like our classify → research → strategize flow)
- **Inference-time optimization** — send the same prompt to multiple models, then have ANOTHER LLM evaluate which response is best. Automatic quality selection without human intervention.
- **Prompts live outside your app code** — change prompts without redeploying

**His verdict:** "Replaced at least a third of my codebase. Removed the headaches of managing APIs. Fantastic tool."

**What this means for FLUX:**

TensorZero is exactly what the FLUX pipeline needs. Our pipeline currently:
- Calls Claude directly for classify/research/strategize
- No fallbacks if Anthropic is down
- No tracking of which calls worked well
- No way to compare model performance
- No metrics on playbook quality

With TensorZero we'd get:
- Define `classify_news`, `research`, `strategize` as TensorZero functions
- Weight opus-4-7 at 1.0 for now, add cheaper models later with lower weights to A/B test
- Automatic fallback if Anthropic fails
- Every pipeline call logged with full context
- Score playbooks after they execute ("did this trade make money?") → feed back into TensorZero metrics
- Over time: data shows which model + prompt combo produces the best playbooks
- Episodes link all three pipeline stages together per news event

This IS the missing piece. It replaces the direct Anthropic calls in the pipeline AND gives us the learning loop.


now this one https://www.youtube.com/watch?v=E5-QK3CDVQM


Conversation info (untrusted metadata):
```json
{
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  "message_id": "1778374352256",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:52 MDT"
}
```

Sender (untrusted metadata):
```json
{
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yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt -o "/tmp/yt-%(id)s" "https://www.youtube.com/watch?v=E5-QK3CDVQMs" 2>&1 | tail -5


[info] E5-QK3CDVQM: Downloading 1 format(s): 399+251
[info] Writing video subtitles to: /tmp/yt-E5-QK3CDVQM.en.vtt
WARNING: The extractor specified to use impersonation for this download, but no impersonate target is available. If you encounter errors, then see  https://github.com/yt-dlp/yt-dlp#impersonation  for information on installing the required dependencies
[download] Destination: /tmp/yt-E5-QK3CDVQM.en.vtt

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python3 -c "
import re

with open('/tmp/yt-E5-QK3CDVQM.en.vtt') as f:
    content = f.read()

lines = content.split('\n')
text_lines = []
for line in lines:
    if line.startswith('WEBVTT') or line.startswith('Kind:') or line.startswith('Language:'):
        continue
    if re.match(r'^\d{2}:\d{2}', line):
        continue
    if line.strip() == '':
        continue
    clean = re.sub(r'<[^>]+>', '', line).strip()
    if clean and (not text_lines or text_lines[-1] != clean):
        text_lines.append(clean)

transcript = ' '.join(text_lines)
transcript = re.sub(r'\s+', ' ', transcript)
print(transcript)
" 2>&1


One of my goals for the last 6 months have been trying to get my agents, my coding agents, to run totally AFK. These AFK agents have been picking up backlog tasks, have been implementing features for me, have been doing QA, and crucially they have been running in parallel. So, I've had lots of them running at the same time. However, in order to get them to run properly, you need to handle the permissions requests that they make. And a question that you probably have right now is how do I get my agent to run without it constantly battering me with requests for permissions. course, you could just go into Yolo mode and have it totally bypass any permissions requests. But, if you do that, Claude will do mad things on your system like delete your home directory. Or, if you're in an enterprise setup, then it might be concerned about, you know, it exfiltrating data or sending your code off to a random third party. So, in order to get agents to run properly AFK, you need them to be sandboxed. And there are a bunch of solutions for this. However, I was not particularly happy with any of them. The one I really tried to use and try to make work was Docker sandboxes. However, there were just so many problems with running it AFK that I won't bore you with now. What I wanted was a simple TypeScript function that I could run and just say, "Run this prompt inside this sandbox using this agent." And all the tools that I found were trying to sell me some third-party service. So, I realized I needed to build something here. And that thing is Sandcastle, a TypeScript library for orchestrating AI coding agents in isolated sandboxes. You can use this to build TypeScript scripts here where you simply say run, passing in the agent, passing in the sandbox, and passing in the prompt. If you look in any one of my open-source repos, you'll see this little Sandcastle or dot Sandcastle directory here, which has a main.ts file. And this is full of these Sandcastle.run little functions here. With this simple function, you can build really, really complex systems. You can build systems that parallelize agents running side by side. You can have systems that review their own code and then merge it in. I've been really, really enjoying using this and now I think it's time to make a video on it. Let me show you how to get this set up inside a repo. We first run npm install AI hero Sandcastle. Once that's done, we can run npx Sandcastle in knit and you'll first be asked to select an agent. Let's select claw code, why not? You can then select between one of the first class sandbox providers that we provide. My plan in the future is to add many, many more of these but and you can also implement your own if you like. For now, let's just choose Docker. Sandcastle also uses a backlog manager because AFK agents need some way of picking up tickets and knowing what to do next. My preferred way of doing this is GitHub issues. We also ship with five templates here currently. I mean, there may be many more by the time you run this. Let's actually max out here. Let's go for a parallel planner with a review step. And since we've chosen GitHub issues, we're going to create a Sandcastle GitHub label. The issues will be filtered by this label and it means that only things with the Sandcastle label on our GitHub issue list will be picked up by the agents. We can see at this stage that a bunch of stuff has been thrown into a dot Sandcastle directory just up here. The thing to know about now is this Dockerfile here, which is essentially the Docker container or the instructions for setting up the Docker container that we're going to be using. Sandcastle runs inside this Docker container and it means that you can just install anything you like inside here. We're installing some important system dependencies, we're installing the GitHub CLI. We're doing a little bit of setup to rename the um home directory to agent. We're installing claw code and then we're just ready to go. So, let's go ahead and build this default Docker image now. That was really fast and it has now completed. Our next steps are we need to set the required environment variables in dot Sandcastle {forward slash} dot env. If we have a look in dot Sandcastle, where it is dot Sandcastle {forward slash} dot env.example, we can see that we have an Anthropic API key and a GitHub token required. If you want to use your Claude subscription instead of an API key, then you can head to this issue here that will tell you more about it. If you don't know, Anthropic is a little bit funny about people using their subscription for these kind of things, and so there's some up-to-date advice there. For me, I'm going to copy over some environment variables that I've had already. Once that's done, I'm going to go into my source control, I'm going to commit this code, and I'm going to push it up because I'm going to show you how we can use GitHub issues to schedule some work for this agent that we've created. So, let's go to our repo and create a new issue. Let's say, "Scaffold me a basic TypeScript template in the repo. Give me a basic TypeScript application that uses Vitest, that uses type checking, that has a very very simple CLI that I can call. Use Commander for the CLI. Add a CI script that does type checking and runs the tests." So, now I'm going to create that issue, and we can now run our agent to see what happens. So, after that, it should be ready to be picked up. First, I'm going to add this little piece of code to my package.json here, which is just going to allow me to run a script here. So, let's say scripts, and then add this sandcastle script here. This is just going to run NPX TSX, and TSX is just a way that you can run TypeScript as a script, and it's going to run this file, .sandcastle/main.mts. So, let's actually go ahead and run this and see what happens. We can see immediately that it's kicked off a planner agent here, and we can control-click these logs to see what it's up to. We can see that it's successfully set up the sandbox. It's the planner agent running on Docker, and it's looking at the open issues here, and it sees that there's only one open issue. It then spits out this plan here, which is a set of issues which are going to be worked on. Finally, at the bottom here, it shows the amount of context window that it used. If we zoom back to our terminal here, we can see that an implementer agent was kicked off, too. Let's control-click these logs and take a look at them, and we can see that it called GitHub issue view one. It has a clear picture and it asked for a basic TypeScript script out V test for testing, type checking, simple CLI using Commander. Great. We can see that it's running bash commands inside here. It's uh doing Okay, good dependencies installed. And I've even got it prompted so it's doing a little bit of red green refactor here where it's writing the test first, V test run, etc. We can see it all happening. It's now moved on a little bit further and we can sit and watch this if we want to or, you know, we can go and have a cup of tea, we can relax and uh this will just do its work without us. So, while this is running, why don't we go and have a look at the main.mts file here? We can see the planner that we saw earlier is just down here where we have a sandcastle.run command that takes in a name of planner. It takes in an agent here so we can just change this if we want to. If we want to do planning with Codex, let's say, instead of Claude code, we totally can. And it's also using this prompt file here. So, plan prompt in here. This is scaffolded by the template and you can totally edit this as much as you want to to run anything inside a sandbox. This one is taking all of the open issues from the repo that have the label sandcastle. It's grabbing all of the labels, all the comments, grabbing all of the comments body as well. And then it's working out which ones can be done right now. So, it's only looking for unblocked issues here. And finally, we tell it to output its plan in a JSON object wrapped in plan tags. If we go back to main.mts, we can see that this then gets picked up here. We then grab the JSON out of the plan here and figure out the issues. And then for each of the issues, we run a a separate sandbox here. We run an implementer. And this one has an implement prompt that's just inside here. So, implement prompt. This one takes in some prompt arguments here. So, it takes in an issue title. It takes in the task ID, which is the issue ID. Then it says you're going to be working on a specific branch. Again, all of this is just a setup that I cooked up, really. This is not Sandcastle giving you any kind of prescription on how you want to run it. This is just a really cool workflow that I tend to use in my repos, so I figured it belonged in a template. If we zoom back to main.mts, we can see that the result here is captured in a variable, and if there are more than one commits here, we then run a reviewer. This pattern has been incredibly powerful because the implementer can make mistakes, but the reviewer generally picks it up. And of course, if you want to do an adversarial review where you have one agent run another or review another agent's code, then you can just do sun castle.codex. If you want to have multiple different agents spawn at the same time, come up with an implementation, and then some other reviewer takes all of those branches, chooses the best one, or makes a like a mix of them, you can. That's the power of having a totally agnostic setup to what agent you're running. That's the power of using your owning your own process. Anyway, let's take a look at the review prompt here. It's worth noting this little syntax here because this is really nice. This is something I copied from Claude skills, where if you specify an exclamation mark before a bunch of backticks here, it will run this when it's resolving the prompt. And so it will actually execute git diff source branch branch here. This review prompt just uses a very basic process, understands the change, analyze it for improvements, check correctness, maintain balance, and crucially it's a great step for like adding your own project standards. So for instance, I've added this coding standards in here that you can fill in with any project standards that you want to be added. Let's look back at main.mts, can see what happens after all of these branches get created. We can see that they then get passed into a merger agent down the bottom. And this one takes all of the branches, takes all of the resulting issues, so it understands the changes that were made, and then merges them back to the main branch. The reason we use an agent for this is that there might be merge conflicts between them. And I usually like to have a really powerful agent handling those merge conflicts for me because they can sometimes be pretty gnarly. And so at the end of this, we have had multiple agents running at the same time, all committing to their branches, and then we get a like a senior merger developer to pull them back into main. Just this setup has massively increased my velocity, and it works super duper well. And again, Sandcastle is not opinionated here. If you wanted to make these into PR branches, you totally could. Okay, let's go and check in with our running process, and let's see what happened. All right, we can see that we had an implementer kick off here, then a reviewer. Let's check the logs for the reviewer. We can see that it found that the code was already clean and well structured. Minimal scaffold template naming is clear. Then, let's see what happened in the merge. So, we can just pull up the merger here, and the merger ran the type checks. It merged in the branch, and it also closed the issue with a comment. Beautiful. We can see too that if we go and have a look at the rest of our code base here, whoa, we now have a bit more code going on. We have a tsconfig.json, we have a vitest.config.ts, and we have a few files knocking about inside the CLI here. So, you can start to see how Sandcastle is working here. You can build these relatively complicated flows using really nice ergonomic markdown prompts. You can get it to run on different branches, and just merge that back into main, or you can get it to do really nice PR flows as well. You know, it's just code. It is a programmatic way to run Claude code, to run Codex, and to build these workflows that turn into these mini software factories. I've been incredibly happy with it, and I'm really excited to see what you build with it, too. If you're thinking about these hard problems, too, then you should check out my newsletter for AI skills for real engineers. These follow the skills repo that went absolutely viral a few days ago, and I also post tips and tricks there for getting the most out of agents using good old software fundamentals. So, thanks for watching, folks. I'm really excited about this tool. I think it's going to be a really nice contribution to the ecosystem, and I've been loving using it. So, nice work, and I'll see you in the next one.


**Video: "Sandcastle — Run AI Coding Agents AFK in Isolated Sandboxes"**

**Who made it:** Matt Pocock (TypeScript educator, AI Hero creator)

**What Sandcastle is:**

A TypeScript library for orchestrating AI coding agents (Claude Code, Codex, etc.) in isolated Docker sandboxes. The key insight: to run agents AFK (away from keyboard) without them destroying your system, you need sandboxing. Docker provides the isolation, Sandcastle provides the orchestration.

**The problem it solves:**
- Running coding agents unattended requires handling permissions
- YOLO mode = agent deletes your home directory or exfiltrates data
- Docker sandboxes were clunky to set up for AFK agents
- No simple "run this prompt in this sandbox with this agent" function existed

**How it works:**
- `npm install ai-hero-sandcastle`
- `npx sandcastle init` → pick agent (Claude Code, Codex), pick sandbox (Docker), pick backlog manager (GitHub Issues)
- Generates a `.sandcastle/` directory with Dockerfile, prompts, and main.mts
- One function: `sandcastle.run({ agent, sandbox, prompt })`

**The workflow he demos (parallel planner + review):**
1. **Planner agent** — reads GitHub issues with "sandcastle" label, creates a plan (which issues to work on)
2. **Implementer agents** — one per issue, all running in parallel in separate Docker containers on separate branches
3. **Reviewer agent** — reviews each branch's code for quality
4. **Merger agent** — merges all branches back to main, handles conflicts

All running AFK. Multiple agents in parallel. Each in its own sandbox.

**Key features:**
- Agent-agnostic (Claude Code, Codex, any CLI agent)
- Parallel execution (multiple sandboxes at once)
- GitHub Issues as backlog (label-based task pickup)
- Branch-per-task workflow
- Markdown prompts with executable snippets (git diff, etc.)
- Review step catches implementer mistakes
- Merger handles conflicts between parallel branches
- All configurable, not opinionated on workflow

**Relevance to us:**

This is interesting for a different reason than FLUX. This is about **automated code contribution** — agents picking up tasks and implementing them. Could be used for:
- FLUX development itself (agents implementing features from GitHub issues)
- Campdesk development
- Any repo where you want AFK agents working on backlog

It's NOT relevant to trading/inference routing. It's a developer productivity tool, not an AI gateway or guardrails system.

The concept of parallel sandboxed agents with a reviewer is powerful though — similar pattern to our FLUX intelligence pipeline (multiple analysis paths → reviewer picks the best strategy).


lastly, this one https://www.youtube.com/watch?v=Bg-IPiql7x8


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Sender (untrusted metadata):
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yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt -o "/tmp/yt-%(id)s" "https://www.youtube.com/watch?v=Bg-IPiql7x8" 2>&1 | tail -5


[info] Bg-IPiql7x8: Downloading 1 format(s): 401+251
[info] Writing video subtitles to: /tmp/yt-Bg-IPiql7x8.en.vtt
WARNING: The extractor specified to use impersonation for this download, but no impersonate target is available. If you encounter errors, then see  https://github.com/yt-dlp/yt-dlp#impersonation  for information on installing the required dependencies
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python3 -c "
import re

with open('/tmp/yt-Bg-IPiql7x8.en.vtt') as f:
    content = f.read()

lines = content.split('\n')
text_lines = []
for line in lines:
    if line.startswith('WEBVTT') or line.startswith('Kind:') or line.startswith('Language:'):
        continue
    if re.match(r'^\d{2}:\d{2}', line):
        continue
    if line.strip() == '':
        continue
    clean = re.sub(r'<[^>]+>', '', line).strip()
    if clean and (not text_lines or text_lines[-1] != clean):
        text_lines.append(clean)

transcript = ' '.join(text_lines)
transcript = re.sub(r'\s+', ' ', transcript)
print(transcript[:15000])
" 2>&1


So, let's be honest about something. In its current state, Hermes Agent is a better, more reliable AI agent than OpenCloth. They've added a ton of functionality over the last month that has not only made it a super powerful 247 AI employee, but more importantly has made it incredibly reliable. In this video, I'll go over all seven new changes that I absolutely love, show you how to use them, and make you a master of Hermes agent. Let's lock in and get into it. So, this is the brand new Hermes dashboard I'll be walking you through. I'll be taking you through a bunch of really incredible features I've been using that I love, including what you see here, which is the brand new cananband board. I'll show you some amazing workflows so you can be doing a ton of things at the same time. Here's the deal with OpenClaw real quick. Let's address the elephant in the room. OpenClaw has had two major issues over the last month that has led to a tremendous amount of people switching from OpenClaw to Hermes. You check Google Trends, anything right now, the chart for Open Claw is going like this. The chart for Hermes agents going like that. The two major issues are that one is, and this is the part that pisses me off the most, every single update breaks OpenClaw. They release an update every day. Every they're shipping a ton. I'm not going to hate on them for that. They're shipping a ton, but everything they ship breaks OpenClaw. Every time I update OpenClaw, it breaks and I have to go back in and spend half an hour fixing it. That is not an experience people want. The second thing, and I'm seeing this probably more often on the timeline, is bloat. The app is just getting huge. It's getting too many features and functionality. And the more people use it, the more it slows down over time. Performance has just crumbled a ton for people. For me, I experienced this once a few months ago. Then I spent hours and hours and hours trying to figure out why it was happening and what I can do to prevent it from happening again. I found the culprit to mostly be the way it manages sessions. I have another video from a couple months ago when I solved this on managing your sessions. So performance for me in OpenClaw hasn't been as much of an issue, but for a lot of people it is. But the broken updates have been an issue. And because that I can't really enjoy any of their new features because I'd rather stay on an old update than going on a new update and have to spend a half hour every single day trying to fix it. So with that being said, we are using Hermes agent now. I'm going to go through it all here. The Hermes agent team seems to be very very focused. They are doing fewer updates, but the updates are much more reliable and seem to be all fitting into specific narrative. So it's not just a hundred things crammed into one update. It's several very specific updates that make sense. You can see here every update, which again, by the way, their aesthetics, their marketing team, incredible. Props to them. All have very specific themes are all focused. So when you update, you know what you're getting. You're not getting just a hundred things thrown at you and they all break. You're getting very specific updates that fit into a narrative so you understand what's happening with your agent, which is very important. Shout out to the Hermes team just for being focused like this. It's great. Anyway, let's get into it. Let's talk about Hermes. So this is the Hermes dashboard. I never use the Open Claw dashboard. I don't find it too helpful. But there are some amazing things in this Hermes dashboard. All you need to do to get this running is you open up your CLI and you type in Hermes dashboard. We're in here. Hermes dashboard. You hit enter. It gives you a URL where your dashboard lives. Now once you're in here, there are many things you can be doing. I'm going to start off with one of the biggest features they released over the last month, which is the Cananband board. This is absolutely glorious for multitasking with Hermes agent. When you are talking to your Hermes through Telegram or iMessage or Discord or whatever it is, you are talking in a single thread. You send a message, it receives that message and does work. If you try to send 10 messages at one time, they all get cued. So, it's a very single file way to do work. On the alternative is the Canananband board. Now, you can do 10, 20, 30 strands at the same time. And if you're anything like me and you work on 20 different things a day, you're building a SAS, you're writing content, you're managing community, you're doing a whole bunch of different things, you can't just have a single thread. You need to have multiple things going on at any given time. This is where the cananban board comes in. And this is great because it's deeply integrated with everything. Hermes, let me show you how this works. So, what I like to do is when I come in at the beginning of the day, I go into triage over here. So, that is the leftmost column. There's a bunch of columns here. triage, todo, ready, in progress, block, done. It's all in order. It's all how the tasks flow through. We start off in triage. I go in, I have my to-do list for the day. You can see it right here. And I go in and I type in everything I need to do. So, I'll say discuss him hiring with AI. Create. I'll do another one. Message live stream clippers. Create that. And then plan second channel. By the way, link for the second channel down below. Alex Finlabs. Check that out. Create that. And so this puts all my tasks in here. Now, what's going to happen is in the next 60 seconds, a cron job will fire and I have a Hermes agent. I'll show you how to do all this in a second. I'll show you how to set up everything I'm doing here. I have a very custom workflow I'm showing you right here. Within 60 seconds, my agent that's dedicated to managing this canban board will go in, see what's in triage, look at all my memories, fill out these tasks with details based on my memory so it's relevant for what I want to accomplish, and move it into ready. Allow me to then take that fully fleshed out task and assign it to an agent so I can get that work done. So, you look in here, I created all these tasks a few minutes ago. They've all been taken from my manager agent, fleshed out, and put into ready. So, let's pop open one of these here. You can see here the agent went in, looked at all my memories from my Obsidian memory system. I had a video on that like a month ago if you want information on that, and took all the context around it, and put all these details in here. So instead of me having to go in and sit here for an hour, type in all the details of what I want the agent to do and add all this context, my agent is automatically doing that for me. Now all I need to do is go in here, assign it to one of my agents, my main worker agents just named Hermes. I just like the name Hermes. I didn't give it a creative name. It is what it is. I hit enter. Now this is assigned to my main Hermes agent who will then in a second do the work for me. Look at the task name. Look at the information. Take it from ready to in progress and manage it all the way to done for me. This is the best way I've seen multitasking with agents ever. And it is native and directly inside Hermes agent. All that's required out of me is two things. Creating the tasks and triage and then assigning it when they're in ready. That's it. All the work is done for me by my agent team. You can see here, boom. I as it was live doing this video that task was automatically picked up by Hermes agent and it's gone and moved over to in progress. And what's amazing is as the agents do different things as they fill out the details do the work they add comments down here. So you can see the librarian who's my manager agent spec written by the librarian output will land in the Obsidian vault. I have an entire folder in the Obsidian vault which I'll show you in a second that all this output goes into. So how do you set this up? Pretty simple. By default, there's no automation here. By default, there's no agents picking things up, adding details. I created this workflow completely custom by myself. I'll show you how to do that in a second. But by default, you're just putting in tasks and assigning them, and eventually they get picked up by the agent. Here's what you want to do to enable all of this amazing automation that will save you tons of time. Here's the prompts, and I'll put this down below. I want to work on the canban board inside the Hermes dashboard. Please create a cron job that checks the cananban board every 10 minutes. I decide 10 minutes is the sweet spot. You can go like every 30 seconds if you want. If you create a agent that manages this that's on a super cheap model. I mean even if you're on an expensive model it's not really a big deal. It's not using a ton of tokens. So you can do it quicker if you want. 10 minutes is fine for me to see if there are any tasks in triage. Then take the tasks look through all the memories and fill out the details for the task. Then move it into ready. Then check to see if there are any task in ready that I assigned an agent to. If I did, take care of that task. This prompt, if you take it from down below, put it into your agent right now, will get you all set up. You have two different options here. You can have your main agent, your Hermes agent managing this. I'd actually recommend against this. I would recommend setting up a second Hermes agent that will manage this. That's why I have a librarian. This is my librarian. It's running on chat GBT. my oath. So, I'm not spending any extra money on it and I have it do a lot of my administrative tasks. If you just go to your main Hermes agent and say, "Set up another Hermes agent profile for a librarian or whatever you want to name your kind of administrative assistant," it will set that up for you. And then you just tell your main agent, "Hey, have the librarian check every 10 minutes." The reason why you want to do that is if your main agent's doing something every 10 minutes, checking the board, having that crown job, it can block tasks, right? It could slow down your workflow. So, you want a second agent that's doing that every 10-minute work. And that's what the librarian does. If you set this up and you have this going all day and you put your tasks in and you manage it, you're going to automate your workflows and you'll get more done you ever have before. The next major feature I want to talk about and I think this is one of the most underrated developments in AI this entire year is slashgo. There are two major platforms right now that utilize this new slash goal functionality. That's codeex from open AAI and now Hermes. Hermes actually beat open clawus. Let's talk about slash goal and how it works. So let's go into our terminal. We're going to say Hermes. That is going to open up our Hermes CLI. So, we can interact directly with our Hermes agent. From in here, I can now use slashgoal. Slashgo is very different than just sending a prompt. When you send a prompt, it has very specific actions and the agent takes those actions. Slashgo instead gives your agent a mission. It is a highlevel mission that the agent can take many steps to do that can go over very long time periods. I've seen some people do slash goals that have literally run for three plus days straight. Obviously, you don't want to be using Opus for this if you're doing a multi- aent mission. I'd probably recommend using the chat GPT OOTH or even better, a local model video on local models coming very soon. But SLG goal allows you to do very highlevel big missions that your agent will go after, figure out how to accomplish, do multi-step approaches, test itself the entire way. It's basically like a Ralph loop but for your AI agent. So you got to open up the CLI, you got to do slashgoal, and then you type in your goal. One thing I need to say here is the quality of your prompt for the slashgoal is very, very critical. If you just do say slashgoal and say, "Hey, build me a game." You're not going to get very good results. I would highly recommend metaprompting here. And basically what I mean by that is go to your favorite LLM, Chad, GBT, Claude, whatever. Say, "Hey, I'm trying to build a slashgoal prompt for my agent. This is what I'm trying to accomplish. Please write me a prompt that gives me a superdetailed SLGO prompt." It will give you a prompt that will be probably extremely detailed and allow your agent do a lot more work. I did that already. I have a prompt right here where it's going to audit my entire Obsidian vault, all the memories I have inside it. Find the dead memories we haven't updated in a while. See which ones are kind of orphans that are not in folders and give me a cleanup plan that I can then approve. And it goes and cleans it up. So, I'm going to hit enter on this and my Hermes agent will go and build an entire plan and then clean up my Obsidian vault. and it'll go on this mission and it can run for minutes, hours, days. It will just figure out the best way to accomplish this highlevel mission. I highly recommend doing this. If you don't off on the top of your head know a goal you want to give your agent, I'd recommend doing this. Go to your agent, say, "Hey, I want to give you a slashgoal, but I don't know what goal I want to give you." Take a look at our projects, things we've done lately, and come up with three options for goals we can execute on. Then you choose one of those goals and then it goes and it tackles that goal. So I'd highly recommend doing a little bit of reverse prompting here if you're not sure what you want to do with slash goals. But it is a great feature that goes and sends off your agent. Does amazing work. If you learned anything at all so far, we have like four more features I want to go through. This is going to be a wildly helpful video. Learn anything so far, leave a like down below. Make sure to subscribe, turn notifications. Also join the Vibe Coding Academy. I have full video series on Hermes and Open Claw and a ton of people in the can. You're experts on these tools. You'll learn a ton. I promise be the best thing you ever do. Join the Vibe Coding Academy link down below. Next, I want to talk about profiles, aka multi- aents. Hermes put a focus on making it so you can create multiple agents very easily. Create teams of these agents that you can then customize any way you want and start attacking tasks on your canban board. much easier to do than OpenClaw. The way you do this is you can either ask your agent to create new agents or you go in the dashboard here, click profiles, multi- aents, go in there, give it a name, name your new profile, and click create. And boom, you have a new Hermes agent. This is its own Hermes agent with its own memories, its own skills, its own environment variables, everyth


**Video: "Hermes Agent vs OpenClaw — 7 New Features That Make It Better"**

**Creator:** Alex (AI/vibe coding YouTuber)

**The Core Claim:** Hermes Agent has become more reliable than OpenClaw due to two major OpenClaw problems:
1. **Every update breaks it** — daily shipping but each update causes 30 min of fixing
2. **Bloat/performance** — too many features, slows down over time, session management issues

**Hermes Agent's 7 Features Covered:**

**1. Kanban Board (native task management)**
- Built into the Hermes dashboard
- Columns: Triage → Todo → Ready → In Progress → Blocked → Done
- He creates tasks in Triage, a "librarian" agent (cheap model, cron every 10 min) auto-fills details from memory and moves them to Ready
- He assigns tasks to worker agents, they execute and move to Done
- Best multitasking he's seen with agents — parallel task execution

**2. /goal (long-running missions)**
- Different from prompts — gives agent a high-level mission that can run for hours/days
- Agent figures out multi-step approach on its own
- Like a REPL loop for agents
- Example: audit entire Obsidian vault, find dead memories, create cleanup plan
- Recommends meta-prompting (use an LLM to write the goal prompt)

**3. Profiles / Multi-Agent**
- Create multiple agents easily (each with own memory, skills, env vars)
- Team of specialized agents attacking different tasks
- Easier to set up than OpenClaw multi-agent

**4. (More features mentioned but transcript cuts off)**

**Key Workflow He Uses:**
- Manager agent ("Librarian") on cheap model (ChatGPT o-auth) — handles admin
- Worker agent ("Hermes") on powerful model — does actual work
- Kanban cron every 10 min: check triage → fill details → move to ready → pick up assigned tasks
- Output goes to Obsidian vault

**Relevance to Us:**

This is a direct competitor analysis to OpenClaw. The criticisms are valid — OpenClaw does ship fast and sometimes breaks. However:

- We're running OpenClaw and it works fine for our setup (we control updates)
- The Kanban + multi-agent pattern is similar to what we have with Ledger, Shepherd, etc.
- The /goal concept is like our TASKS.md + cron system
- His "librarian on cheap model" pattern = what we're doing with Ledger crons

The video doesn't change what we're building. It's a user preference thing (Hermes vs OpenClaw). Our infrastructure is already more sophisticated than what he's showing — FLUX, Bifrost, TensorZero, etc. go way beyond what either tool provides out of the box.


ok, now based on everything we've learned. what actions should we take?


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message

**Actions based on everything we learned today:**

**IMMEDIATE (tonight/tomorrow):**

1. Fix TensorZero on QNAP (config file path issue — 2 min fix, already started)
2. Wire FLUX pipeline through TensorZero instead of direct Anthropic calls — define classify_news, research, strategize as TensorZero functions with opus-4-7
3. Seed the starter playbooks into the PostgreSQL database so the execution engine has something to match against Monday morning
4. Verify LLM Guard is running on QNAP (was pulling when we got sidetracked)

**THIS WEEK (before market opens Monday):**

5. TensorZero becomes the primary inference gateway for FLUX (replaces the direct API call in the pipeline). Bifrost stays as backup/comparison.
6. Add feedback loop: after each trade executes, score the playbook (profit/loss) and feed that back to TensorZero as a metric. This is how it learns.
7. Define TensorZero "episodes" — each news event → classify → research → strategize → execute is one episode. Track end-to-end quality.
8. LLM Guard sits in front of TensorZero — scan prompts before they go out, scan responses before they become playbooks. Catches hallucinated tickers, PII in logs, injection from news content.

**THIS MONTH:**

9. Kill LiteLLM (TensorZero replaces it with better performance + learning). Keep Bifrost as the simple/fast fallback.
10. Dashboard redesign — properly. Not rushing it. Study the Market Insight reference, take time, ship something actually good.
11. Add a second model variant to TensorZero functions (maybe sonnet for classification, opus for strategy). Let TensorZero A/B test and find optimal routing.
12. Backtest framework — replay archived market events through the pipeline, score outcomes, validate strategies before deploying live.
13. Kalshi adapter — add prediction markets as a signal source (not execution venue). Kalshi market prices as leading indicators for traditional market moves.

**BEFORE GOING LIVE (6-8 weeks):**

14. 30+ days of paper trading data showing positive Sharpe
15. LLM Guard fully configured (especially for live trading — no hallucinated orders)
16. TensorZero metrics prove which model/prompt combos produce profitable playbooks
17. Circuit breakers tested (kill switch, daily loss limit, position limits all verified)
18. Dashboard showing real performance metrics (win rate, drawdown, profit factor)
19. Swap Alpaca paper keys for live keys. Start with half the paper position sizes.

**INFRASTRUCTURE DECISIONS MADE:**

- TensorZero = primary AI gateway (routing + learning + metrics)
- Bifrost = backup/fast gateway (keep running, use if TensorZero has issues)
- LiteLLM = evaluate then likely kill (redundant once TensorZero is proven)
- LLM Guard = guardrails layer (independent service, all projects use it)
- FLUX = trading platform (engine + pipeline + dashboard + adapters)
- OpenClaw = agent orchestration (Ledger, Link, other agents)
- QNAP = all infrastructure (Docker + Traefik + PostgreSQL)

**KEY LEARNING FROM THE VIDEOS:**

- The $60K routing trap confirms: self-hosting is the right call
- TensorZero is the missing piece that makes the whole system get smarter over time
- Sandcastle pattern (parallel agents + reviewer) could apply to FLUX strategy generation — spawn 3 strategists, have a reviewer pick the best playbook
- Keep OpenClaw updates conservative (don't update daily — the Hermes video confirms the instability complaints)


great. do all 4 of those things. update any flux docs and diagrams that need to be updated to reflect the changes


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778374621337",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:57 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```


exec

# 1. Fix TensorZero config path
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker logs tensorzero --tail 3" 2>&1


[2m2026-05-10T00:56:48.877861Z[0m [31mERROR[0m [2mtensorzero_error[0m[2m:[0m functions.classify_news: model: unknown field `model`, expected one of `variants`, `system_schema`, `user_schema`, `assistant_schema`, `schemas`, `tools`, `tool_choice`, `parallel_tool_calls`, `description`, `experimentation`, `evaluators`
[2m2026-05-10T00:56:48.877980Z[0m [31mERROR[0m [2mgateway[0m[2m:[0m Failed to load config. Config file glob `/app/config/tensorzero.toml` resolved to the following files:
/app/config/tensorzero.toml


# Config format is wrong. TensorZero uses a different schema than what I wrote.
# Let me fix it based on what the video showed.

ANTHROPIC_KEY=$(op read "op://OpenClaw/Anthropic - SMS Agent/credential" 2>/dev/null)

cat > /tmp/tensorzero-config.toml << 'EOF'
[gateway]
bind_address = "0.0.0.0:3000"

[models.claude-opus-4-7]
routing = ["anthropic_opus"]

[models.claude-opus-4-7.providers.anthropic_opus]
type = "anthropic"
model_name = "claude-opus-4-7"

[functions.classify_news]
type = "chat"

  [functions.classify_news.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1

[functions.research]
type = "chat"

  [functions.research.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1

[functions.strategize]
type = "chat"

  [functions.strategize.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1

[functions.review]
type = "chat"

  [functions.review.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1

[metrics.playbook_profit]
type = "float"
level = "inference"
optimize = "max"

[metrics.playbook_triggered]
type = "boolean"
level = "inference"
optimize = "max"
EOF

rsync -avz -e "sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no" \
  /tmp/tensorzero-config.toml admin@192.168.7.5:/share/appconfigs/tensorzero/config.toml 2>&1 | tail -2

sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "cd /share/appconfigs/tensorzero && /share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker compose restart tensorzero" 2>&1


sent 411 bytes  received 54 bytes  422727 bytes/sec
total size is 937  speedup is 2.02
 Container tensorzero  Restarting
 Container tensorzero  Started


Conversation info (untrusted metadata):
```json
{
  "chat_id": "+12817509483",
  "message_id": "1778374635152",
  "sender_id": "+12817509483",
  "sender": "Brandon",
  "timestamp": "Sat 2026-05-09 18:57 MDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Brandon (+12817509483)",
  "id": "+12817509483",
  "name": "Brandon"
}
```

i meant 6, do all 6 of those things. just do all the things.


# Check TensorZero status first
sleep 5
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker logs tensorzero --tail 5" 2>&1


[2m2026-05-10T00:57:31.682486Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m ├ Valkey: disabled
[2m2026-05-10T00:57:31.682495Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m ├ Relay mode: disabled
[2m2026-05-10T00:57:31.682505Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m ├ Autopilot Worker: disabled
[2m2026-05-10T00:57:31.682515Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m ├ Autopilot Tool Whitelist Approver: disabled
[2m2026-05-10T00:57:31.682518Z[0m [32m INFO[0m [2mgateway[0m[2m:[0m └ OpenTelemetry: disabled


# Good - TensorZero started without errors. Verify it responds.
curl -s https://tensorzero.disorganized.net/health 2>&1 | head -5
echo "==="
# Test inference
ANTHROPIC_KEY=$(op read "op://OpenClaw/Anthropic - SMS Agent/credential" 2>/dev/null)

curl -s -X POST https://tensorzero.disorganized.net/inference \
  -H "Content-Type: application/json" \
  -d '{
    "function_name": "classify_news",
    "input": {
      "messages": [{"role": "user", "content": "Say hi in 3 words"}]
    }
  }' 2>&1 | python3 -c "
import json, sys
data = json.load(sys.stdin)
if 'content' in str(data):
    print(f'✅ TensorZero working')
    print(f'   {json.dumps(data)[:300]}')
else:
    print(f'Result: {json.dumps(data)[:500]}')
"


Command still running (session marine-tidepool, pid 53337). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


(no new output)

Process exited with signal SIGKILL.


# Timed out - might be DNS not ready or TZ still booting. Try direct.
curl -s -o /dev/null -w "%{http_code}" https://tensorzero.disorganized.net/ 2>&1


# 404 = Traefik is routing to it but TZ doesn't have a root endpoint. Try the inference endpoint.
curl -s -X POST "https://tensorzero.disorganized.net/inference" \
  -H "Content-Type: application/json" \
  -d '{"function_name":"classify_news","input":{"messages":[{"role":"user","content":"Say hi"}]}}' 2>&1 | python3 -c "
import json,sys
try:
    data = json.load(sys.stdin)
    print(json.dumps(data, indent=2)[:500])
except:
    print(sys.stdin.read()[:500])
" 2>&1


All variants failed with errors: opus: All model providers failed to infer with errors: anthropic_opus: Error from anthropic client: The TensorZero Gateway doesn't know the output token limit for `claude-opus-4-7` and Anthropic requires you to provide a `max_tokens` value. Please set `max_tokens` in your configuration or inference request.


# Need to add max_tokens to the variant configs
cat > /tmp/tensorzero-config.toml << 'EOF'
[gateway]
bind_address = "0.0.0.0:3000"

[models.claude-opus-4-7]
routing = ["anthropic_opus"]

[models.claude-opus-4-7.providers.anthropic_opus]
type = "anthropic"
model_name = "claude-opus-4-7"

[functions.classify_news]
type = "chat"

  [functions.classify_news.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1
  max_tokens = 4096

[functions.research]
type = "chat"

  [functions.research.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1
  max_tokens = 8192

[functions.strategize]
type = "chat"

  [functions.strategize.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1
  max_tokens = 8192

[functions.review]
type = "chat"

  [functions.review.variants.opus]
  type = "chat_completion"
  model = "claude-opus-4-7"
  weight = 1
  max_tokens = 4096

[metrics.playbook_profit]
type = "float"
level = "inference"
optimize = "max"

[metrics.playbook_triggered]
type = "boolean"
level = "inference"
optimize = "max"
EOF

rsync -avz -e "sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no" \
  /tmp/tensorzero-config.toml admin@192.168.7.5:/share/appconfigs/tensorzero/config.toml 2>&1 | tail -2

sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "cd /share/appconfigs/tensorzero && /share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker compose restart tensorzero" 2>&1

sleep 5

# Test again
curl -s -X POST "https://tensorzero.disorganized.net/inference" \
  -H "Content-Type: application/json" \
  -d '{"function_name":"classify_news","input":{"messages":[{"role":"user","content":"Say hello in 5 words"}]}}' 2>&1 | python3 -c "
import json,sys
data = json.load(sys.stdin)
if 'output' in str(data) or 'content' in str(data):
    print('✅ TensorZero working!')
    print(json.dumps(data)[:400])
else:
    print(json.dumps(data)[:500])
"


Command still running (session calm-coral, pid 53406). Use process (list/poll/log/write/kill/clear/remove) for follow-up.

sent 432 bytes  received 54 bytes  25445 bytes/sec
total size is 1017  speedup is 2.09
 Container tensorzero  Restarting
 Container tensorzero  Started


sent 432 bytes  received 54 bytes  25445 bytes/sec
total size is 1017  speedup is 2.09
 Container tensorzero  Restarting
 Container tensorzero  Started

Process exited with signal SIGKILL.


exec

# Wait for it to be ready and test
sleep 8
curl -s -X POST "https://tensorzero.disorganized.net/inference" \
  -H "Content-Type: application/json" \
  -d '{"function_name":"classify_news","input":{"messages":[{"role":"user","content":"Say hello in 5 words"}]}}' 2>&1 | python3 -c "
import json,sys
raw = sys.stdin.read()
try:
    data = json.loads(raw)
    if 'content' in json.dumps(data):
        print('✅ TensorZero: working!')
        # Extract the response text
        content = data.get('output', data.get('content', data))
        print(f'   {json.dumps(content)[:300]}')
    else:
        print(json.dumps(data, indent=2)[:500])
except:
    print(raw[:500])
"


Command still running (session rapid-bison, pid 53472). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


# The inference call is taking too long (opus-4-7 can be slow). Let me check if it actually works
# by looking at logs
sshpass -p 'Cy@n314159' ssh -o StrictHostKeyChecking=no admin@192.168.7.5 \
  "/share/ZFS530_DATA/.qpkg/container-station/usr/bin/docker logs tensorzero --tail 8" 2>&1