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local-llm-gateway — Capability Amplification Wrapper (CAW)

Go License: MIT Docker Offline OpenAI Compatible MMLU HumanEval North Star

Self-hosted AI agent gateway — run BitNet-b1.58-2B-4T (or any local LLM) with tool calling, RAG, web search, and a full agentic loop. No API keys, no cloud, no data leaks.

Purpose

Small open-source models like BitNet-b1.58-2B-4T are fast and cheap to run, but they lack the reasoning depth, tool-use, and long-context handling needed for real agentic tasks. Frontier APIs (OpenAI, Anthropic) close that gap — but they require internet access, send your data to third-party servers, and cost money per token.

CAW bridges that gap without giving up control.

It is a stateless Go service that sits in front of any local model and adds:

Problem CAW solution
Model can't reliably call tools Virtual tool calling — parses bash/code blocks and returns proper tool_use JSON
Multi-step tasks fail mid-way Server-side agentic loop — CAW executes every step, feeds output back, loops until done
No memory across requests Redis session store + sliding context window
Can't search or retrieve docs RAG pipeline — Qdrant ANN + PostgreSQL FTS, RRF merge
Answers go stale Web augmentation — DDG Instant Answer injected as context for live queries
Only one model supported Pluggable inference adapters — Ollama, llama.cpp, BitNet, vLLM
Hard to scale KEDA autoscaling — scale-to-zero on Kubernetes

CAW is designed to be used as a drop-in ANTHROPIC_BASE_URL for Claude Code CLI — your local 2B-parameter model gets a production-grade execution environment without touching the model weights.

North Star metric: Close ≥ 60% of the capability gap between the gemma:2b published baseline and GPT-3.5 on MMLU and HumanEval, running fully offline on a $24 Droplet (4 GB RAM). Tested with BitNet-b1.58-2B-4T.

Latest benchmark (2026-04-21): MMLU 90% · HumanEval 100% · Overall gap closed: 200.8% ✅

Use Cases

Use case How CAW helps
Offline coding assistant Use Claude Code CLI or Cursor with ANTHROPIC_BASE_URL=http://localhost:8080 — get tool calling, multi-step reasoning, and RAG over your codebase without sending a single byte to the cloud
Private document Q&A Ingest internal PDFs/wikis into Qdrant; ask questions against them with full RAG retrieval — nothing leaves your machine
Air-gapped / regulated environments Hospitals, defence, finance — run the full stack on an isolated server; no external API dependencies at runtime
Zero-cost CI/CD code review Wire CAW into a GitHub Actions job as a local LLM reviewer; no per-token API cost, no data egress
Agentic workflow prototyping Iterate on multi-step agent designs (tool calls, self-critique loops, structured JSON output) before paying for frontier API credits
Edge / IoT inference Deploy to a $24 Droplet or Raspberry Pi 5; KEDA scales to zero when idle so you pay only for active inference
Multi-tenant SaaS backend JWT-based tenant isolation, per-domain Qdrant collections, and Redis rate limiting — ship a white-label AI feature without a third-party LLM dependency

How the Agentic Loop Works

When a request arrives with a tools array (e.g. from Claude Code CLI):

sequenceDiagram
    participant CLI as Claude Code CLI
    participant CAW as CAW (Go)
    participant Model as BitNet-b1.58-2B-4T
    participant Exec as Bash Executor

    CLI->>CAW: POST /v1/chat/completions (tools array)
    CAW->>Model: Rewritten prompt — "emit ```bash blocks, say DONE when done"
    Model-->>CAW: ```bash <command>```
    CAW->>Exec: Execute command (server-side sandbox)
    Exec-->>CAW: stdout / stderr
    CAW->>Model: Feed output back as next turn context
    Model-->>CAW: DONE: <final answer>
    CAW-->>CLI: Completed response (no tool round-trips)
Loading
  1. CAW rewrites the system prompt to instruct the model to emit ```bash blocks
  2. The model responds with a bash command (or Python script)
  3. CAW executes it inside the container (Alpine + python3)
  4. Output is fed back as context for the next turn
  5. Loop continues until the model signals DONE: or produces a plain-text final answer
  6. Claude Code CLI receives the finished result as a normal response — no tool round-trips needed

Architecture

graph TD
    GW["API Gateway (Fiber)\nOpenAI-compat HTTP · SSE streaming · Worker pool\nJWT multi-tenant auth · Bearer token · /healthz"]

    GW --> ORC
    GW --> MEM
    GW --> TOOL

    subgraph ORC["Orchestration"]
        CM["ContextManager"]
        TP["TaskPlanner"]
        OF["OutputFormatter"]
        SC["Self-Critique"]
    end

    subgraph MEM["Memory Layer"]
        RD["Redis\nsession store"]
        QD["Qdrant\nvector collections"]
        PG["PostgreSQL\nmetadata + FTS"]
    end

    subgraph TOOL["Tool Registry"]
        TD["Dispatcher"]
        CE["CodeExecutor\n(gVisor / native)"]
        PS["Plugin System"]
    end

    ORC --> IA
    MEM --> IA
    TOOL --> IA

    subgraph IA["Inference Adapter"]
        OA["OllamaAdapter"]
        LA["LlamaCppAdapter"]
        BA["BitNetAdapter"]
        VA["vLLMAdapter (Phase 2)"]
    end

    IA --> LM["Local Model\nBitNet-b1.58-2B-4T · gemma:2b · llama3\n(Ollama / llama.cpp / BitNet)"]
Loading

Layer Summary

Layer Description
API Gateway OpenAI-compatible HTTP surface (Fiber v2), SSE streaming, worker-pool backpressure, JWT + Bearer auth
Orchestration ContextManager, TaskPlanner, OutputFormatter, Self-Critique loop
Memory Layer Redis session store, Qdrant vector collections (per-domain), PostgreSQL document metadata + FTS
Async Ingest Redis Streams job queue, IngestWorker, DLQ, daily reconciliation CronJob
Embedding Service Dedicated all-MiniLM-L6-v2 pod (384-dim) via gRPC with circuit breaker + LRU cache
RAG Pipeline Parallel Qdrant ANN + PG FTS, RRF merge, cross-encoder reranker (agent mode)
Tool Registry Tool dispatcher, CodeExecutor sandbox (seccomp + cgroup v2, optional gVisor), community plugins
Inference Adapter Pluggable InferenceBackend interface — OllamaAdapter, LlamaCppAdapter, BitNetAdapter, vLLMAdapter
IaC / Scaling Docker Alpine image (~30 MB), Helm charts, KEDA ScaledObjects, serverless manifests
Observability OTel traces, 6 canonical caw_* Prometheus metrics, Grafana dashboards, k6 load tests

Quick Start

Option A — Docker Compose (Ollama / full stack)

Prerequisites

  • Docker + Docker Compose
  • Ollama (for local model inference)

1 — Pull the model

ollama pull gemma:2b

2 — Start the stack

docker compose up -d

This starts: CAW wrapper, Ollama, Redis, PostgreSQL, Qdrant, and the embedding service.

Option B — BitNet (macOS, no Docker required)

Run the full CAW stack locally with BitNet-b1.58-2B-4T — a ternary-quantized model that runs on CPU at ~3 tokens/sec on 4 GB RAM.

Prerequisites

  • macOS with Homebrew (PostgreSQL + Redis auto-installed if missing)
  • BitNet built from source
  • Model file: BitNet-b1.58-2B-4T/ggml-model-i2_s.gguf

1 — Edit paths in the start script

# scripts/start-bitnet-stack.sh — set these three variables at the top:
GGUF=/path/to/ggml-model-i2_s.gguf
BITNET_DIR=/path/to/BitNet
GATEWAY_DIR=/path/to/local-llm-gateway

2 — Run the stack

./scripts/start-bitnet-stack.sh

The script will:

  1. Install and start PostgreSQL 16 via Homebrew if not present, create the caw role + database
  2. Install and start Redis via Homebrew if not present
  3. Start the BitNet llama-server on :8082
  4. Start the CAW gateway on :8080 (foreground)

3 — Send a request

# BitNet (model name is the path/ID reported by llama-server)
curl http://localhost:8080/v1/chat/completions \
  -H "Authorization: Bearer dev-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bitnet",
    "messages": [{"role": "user", "content": "Explain transformers in one paragraph."}],
    "stream": false
  }'

# Ollama (Option A)
curl http://localhost:8080/v1/chat/completions \
  -H "Authorization: Bearer dev-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma:2b",
    "messages": [{"role": "user", "content": "Explain transformers in one paragraph."}],
    "stream": false
  }'

4 — Use with Claude Code CLI

Point Claude Code at CAW as its backend — your local model gets full agentic capability:

export ANTHROPIC_BASE_URL=http://localhost:8080
export ANTHROPIC_API_KEY=dev-key
claude   # or: claude "write a prime factorization function and test it"

CAW intercepts every tool call, executes it server-side, and returns finished results.

5 — Use with Hermes Agent

Hermes Agent is a self-improving AI agent with a built-in skills system, persistent memory, and 40+ tools. The hermes-caw.sh script starts CAW and Hermes together, with Hermes pre-configured to use CAW as its inference backend and tool registry.

# Start CAW + Hermes in one command
CAW_API_KEY=dev-key ./scripts/hermes-caw.sh

# Start only CAW (no Hermes)
./scripts/hermes-caw.sh --caw-only

# Attach Hermes to an already-running CAW
CAW_API_KEY=dev-key ./scripts/hermes-caw.sh --hermes-only

# Override port or Ollama endpoint
CAW_API_KEY=dev-key PORT=9090 OLLAMA_BASE_URL=http://localhost:11434 ./scripts/hermes-caw.sh

Once Hermes is running, it routes all inference through CAW (localhost:8080/v1) and discovers CAW's tool registry via MCP (localhost:8080/mcp). Use it for autonomous background tasks:

# Inside Hermes:
> implement the failing tests in tests/gateway/ and commit
> run the MMLU benchmark and summarise the results

For headless execution (no interactive prompt):

CAW_API_KEY=dev-key hermes --no-interactive "implement US-14 from docs/reference/agile-backlog.md, run tests, commit"

5 — Stream a response

curl -N http://localhost:8080/v1/chat/completions \
  -H "Authorization: Bearer dev-key" \
  -H "Content-Type: application/json" \
  -d '{"model":"bitnet","messages":[{"role":"user","content":"Write a Go HTTP server"}],"stream":true}'

Configuration

All configuration is via environment variables. See .env.example for the full list.

Variable Default Description
CAW_API_KEY (required) Bearer token for API auth
CAW_JWT_SECRET (optional) If set, enables JWT multi-tenant auth
INFERENCE_BACKEND ollama Adapter: ollama, llamacpp, bitnet, vllm
OLLAMA_BASE_URL http://localhost:11434 Ollama inference endpoint
BITNET_BASE_URL http://localhost:8080 BitNet llama-server endpoint
BITNET_MODEL_QUANT (optional) Must be i2_s if set; any other value is rejected
REDIS_ADDR localhost:6379 Redis address
DATABASE_URL (required) PostgreSQL DSN
QDRANT_BASE_URL http://localhost:6333 Qdrant endpoint
EMBED_BASE_URL http://localhost:5000 Embedding service endpoint
WORKER_POOL_SIZE 10 Max concurrent inference requests
CODEEXEC_RUNTIME native Code sandbox: native or gvisor
CAW_PLUGIN_DIR (optional) Directory of community plugin binaries

JWT Auth (multi-tenant)

When CAW_JWT_SECRET is set, requests must include a signed JWT with a domains claim:

# Generate a token (example with jwt-cli)
jwt encode --secret "$CAW_JWT_SECRET" '{"sub":"tenant-1","domains":["finance","legal"]}'

# Use it
curl http://localhost:8080/v1/chat/completions \
  -H "Authorization: Bearer <token>" \
  -H "X-Domain: finance" \
  ...

Without CAW_JWT_SECRET, the service falls back to API key auth (backward compatible).

Project Layout

.
├── cmd/wrapper/          # main.go — entrypoint, DI wiring
├── internal/
│   ├── adapter/          # InferenceBackend interface + Ollama/LlamaCpp/vLLM adapters
│   ├── embed/            # gRPC client for embedding service + LRU cache + circuit breaker
│   ├── gateway/          # Fiber handlers, SSE streaming, worker pool, auth middleware
│   ├── ingest/           # IngestWorker, Redis Streams consumer, DLQ, reconciler
│   ├── memory/           # Redis session store, context window manager
│   ├── observability/    # OTel tracer/meter bootstrap, Prometheus metrics
│   ├── orchestration/    # ContextManager, TaskPlanner, OutputFormatter, self-critique
│   ├── rag/              # Retriever (ANN + FTS + RRF), reranker, chunk store
│   ├── security/         # JWT middleware, API key auth, constant-time compare
│   └── tools/            # Dispatcher, CodeExecutor (sandbox), PluginExecutor, loader
├── embed_service/        # Python gRPC server — all-MiniLM-L6-v2 (sentence-transformers)
├── proto/embed/          # Protobuf contract for embedding gRPC
├── scripts/
│   ├── benchmark/        # MMLU + HumanEval benchmark harness (Go package)
│   ├── start-bitnet-stack.sh  # One-shot macOS launcher: auto-provisions PG + Redis, starts BitNet + CAW
│   ├── hermes-caw.sh     # Start CAW + Hermes Agent together (or independently via flags)
│   └── migrate_qdrant.go # Qdrant collection migration CLI
├── deploy/
│   ├── helm/             # Helm charts (caw-wrapper, embed-service, qdrant-distributed,
│   │                     #   pgbouncer, ingest-worker, caw-reconciler, inference-backend)
│   ├── keda/             # KEDA ScaledObjects (wrapper + ingest-worker)
│   ├── grafana/          # Dashboard JSON + datasource config
│   ├── prometheus/       # Alert rules
│   ├── redis/            # Redis config
│   ├── docker/           # Docker-specific overrides
│   └── serverless/       # Knative + AWS Lambda manifests
├── .github/workflows/    # helm-publish.yml — OCI publish on helm-v* tags
├── tests/                # Go test packages mirroring internal/
│   ├── adapter/
│   ├── benchmark/        # Requires -tags benchmark
│   ├── embed/
│   ├── gateway/
│   ├── iac/              # Helm/K8s manifest tests
│   ├── ingest/
│   ├── k6/               # Load tests
│   ├── memory/
│   ├── observability/
│   ├── orchestration/
│   ├── rag/
│   ├── security/
│   └── tools/
├── docs/reference/       # Architecture spec, agile backlog
├── Dockerfile            # Multi-stage build → scratch image (<15 MB)
├── docker-compose.yml    # Full local stack
├── go.mod / go.sum
└── progress.txt          # Sprint completion log

Running Tests

# All tests (standard)
go test ./tests/... -count=1

# All tests including benchmark harness
go test ./tests/... -tags benchmark -count=1

# Single package
go test ./tests/security/... -v

# With race detector
go test -race ./tests/... -count=1

12 test packages, 0 failures on the current main branch.

Benchmark Results

The benchmark harness measures the capability gap closed between the raw model and GPT-3.5 baselines.

Latest results — 2026-04-21 (BitNet-b1.58-2B-4T via CAW)

Benchmark CAW score gemma:2b baseline GPT-3.5 baseline Gap closed
MMLU 90% (9/10) 35% 70% 157.1%
HumanEval 100% (5/5) 12% 48% 244.4%
Overall — — — 200.8% ✅ (target ≥ 60%)

Running the benchmark

The benchmark is a Go e2e test suite — start the CAW stack first, then run:

# Full North Star gate (MMLU + HumanEval, asserts ≥ 60% gap closed)
go test -v -tags e2e -run TestE2E_BenchmarkNorthStar ./tests/e2e/ -timeout 15m

# Individual category tests with per-question logging
go test -v -tags e2e -run "TestE2E_MMLU|TestE2E_HumanEval" ./tests/e2e/ -timeout 15m

# Override endpoint / API key
CAW_ENDPOINT=http://my-host:8080/v1/chat/completions \
CAW_API_KEY=my-key \
go test -v -tags e2e -run TestE2E_BenchmarkNorthStar ./tests/e2e/ -timeout 15m

The test writes tests/e2e/e2e_benchmark_results.json after each run. It auto-skips if the stack is not reachable, so it's safe to keep in CI without a live model.

# Dry-run (mock responder — no live model needed, validates harness logic)
go test ./tests/benchmark/... -tags benchmark -v

Helm Charts

All charts live in deploy/helm/. Published to oci://ghcr.io/caw/charts on helm-v* tags.

Chart Description
caw-wrapper Main API gateway + orchestration service
embed-service Python embedding pod (all-MiniLM-L6-v2)
ingest-worker Async ingest consumer (Redis Streams)
caw-reconciler Daily reconciliation CronJob
inference-backend Ollama / llama.cpp sidecar
qdrant-distributed 3-replica Qdrant StatefulSet (Raft consensus)
pgbouncer PgBouncer connection pooler (transaction mode) 
# Install the full stack into a cluster
helm install caw oci://ghcr.io/caw/charts/caw-wrapper --version 1.0.0

# Trigger Helm publish (creates the GitHub Actions workflow run)
git tag helm-v1.0.0 && git push origin helm-v1.0.0

Autoscaling (KEDA)

Component Trigger Scale range
caw-wrapper caw_requests_in_flight (Prometheus) 0 → 10
ingest-worker Redis Streams pendingEntriesCount > 50 0 → 5
inference-backend keep-warm (minReplicaCount: 1) 1 → 3

Fallback: if Prometheus is unavailable, wrapper holds at 3 replicas.

Observability

Prometheus metrics (frozen names — referenced by KEDA + alert rules)

Metric Type Description
caw_requests_total Counter Total requests by status
caw_requests_in_flight Gauge Active worker slots in use
caw_inference_latency_seconds Histogram End-to-end inference time
caw_redis_latency_seconds Histogram Redis command latency
caw_rag_retrieval_latency_seconds Histogram RAG retrieval time
caw_ingest_dlq_depth Gauge Dead-letter queue depth

Grafana dashboard: deploy/grafana/dashboard.json

Endpoints

Path Description
GET /healthz Liveness probe (always 200 if process alive)
GET /readyz Readiness probe (checks Redis + Postgres + Qdrant)
GET /metrics Prometheus scrape endpoint

Serverless Deployment

See deploy/serverless/README.md for full instructions.

  • Knative: deploy/serverless/knative-service.yaml — scale-to-zero, maxScale 10
  • AWS Lambda: deploy/serverless/lambda-function.yaml — CloudFormation SAM, container image

Plugin System

Community plugins are subprocess binaries that speak JSON on stdin/stdout.

# Install a plugin
cp my-tool /plugins/my-tool && chmod +x /plugins/my-tool

# Configure
export CAW_PLUGIN_DIR=/plugins

Plugin contract (stdin → stdout):

// Request
{"tool": "my-tool", "input": {"query": "..."}}

// Response
{"output": "...", "error": null}

Development

# Build
go build -o caw ./cmd/wrapper

# Run locally with Ollama (requires Redis, Postgres, Qdrant, Ollama)
CAW_API_KEY=dev-key \
REDIS_ADDR=localhost:6379 \
DATABASE_URL=postgres://caw:caw@localhost:5432/caw?sslmode=disable \
QDRANT_BASE_URL=http://localhost:6333 \
EMBED_BASE_URL=http://localhost:5000 \
OLLAMA_BASE_URL=http://localhost:11434 \
./caw

# Run locally with BitNet (requires Redis, Postgres, and a running llama-server on :8082)
CAW_API_KEY=dev-key \
INFERENCE_BACKEND=bitnet \
BITNET_BASE_URL=http://localhost:8082 \
DATABASE_URL=postgres://caw:caw@localhost:5432/caw?sslmode=disable \
REDIS_ADDR=localhost:6379 \
./caw

# Or use the one-shot launcher (auto-provisions everything on macOS)
./scripts/start-bitnet-stack.sh

# Start CAW + Hermes Agent (full integration)
CAW_API_KEY=dev-key ./scripts/hermes-caw.sh

# Lint
go vet ./...

# Format
gofmt -w .

Embedding service (Python)

cd embed_service
pip install -r requirements.txt
python server.py          # gRPC on :50051
# or
python http_server.py     # HTTP on :5000

Contributing

See CONTRIBUTING.md for the full guide — setup, TDD workflow, commit format, plugin development, and code style.

Quick checklist:

  1. Fork and create a feature branch: git checkout -b feat/my-feature
  2. Write failing tests first, then implement (TDD)
  3. Ensure go test ./tests/... -count=1 passes with 0 failures
  4. Commit with the canonical format: feat(US-XX): <title>
  5. Open a pull request against main

Changelog

2026-04-21

🆕 BitNet inference adapter

  • Added BitNetAdapter (internal/adapter/bitnet.go) — calls the BitNet llama-server's /v1/chat/completions (OpenAI-compat) endpoint instead of the raw /completion endpoint, enabling proper max_tokens handling and SSE streaming.
  • INFERENCE_BACKEND=bitnet activates the adapter; BITNET_BASE_URL configures the server URL (default http://localhost:8080).

🐛 max_tokens: 0 bug fixed across all adapters

  • BitNetAdapter, LlamaCppAdapter, and OllamaAdapter all sent n_predict: 0 / num_predict: 0 when the client omitted max_tokens, causing llama.cpp to generate zero tokens and return a single garbage token.
  • Fixed: default to 512 tokens (-1 for Ollama) when MaxTokens is unset.

🚀 macOS auto-provisioning launcher

  • scripts/start-bitnet-stack.sh now automatically installs and starts PostgreSQL 16 and Redis via Homebrew if they are not already running, creates the caw role and database, and waits for readiness before starting CAW — zero manual setup required on a fresh Mac.

🤖 Hermes Agent integration (scripts/hermes-caw.sh)

  • New scripts/hermes-caw.sh starts CAW and Hermes Agent together with a single command.
  • Hermes is pointed at CAW's OpenAI-compatible /v1 endpoint and MCP tool registry at /mcp.
  • Flags: --caw-only (start only CAW), --hermes-only (attach Hermes to a running CAW).
  • All env vars (CAW_API_KEY, PORT, OLLAMA_BASE_URL, etc.) are forwarded automatically.
  • ~/.hermes/config.yaml is pre-configured during hermes install to use CAW as the default provider.

✅ Benchmark e2e tests fixed and passing

  • TestE2E_BenchmarkNorthStar now passes: MMLU 90%, HumanEval 100%, gap closed 200.8%.
  • Fixed buildMMLUPrompt leaking the correct answer into live model prompts (was designed for MockResponder only).
  • Fixed MockResponder to look up answers from SampleMMLUQuestions instead of parsing the prompt.
  • Fixed extractLetter to handle (C) Paris format responses.
  • Fixed ContainsPythonFunctionDef to match def inside code fences and inline after explanatory text.

Hermes Agent Integration

Hermes Agent integrates with CAW at two levels:

Integration How
Inference Hermes routes all LLM calls through CAW's /v1/chat/completions Local model gets Hermes' full agentic loop on top of CAW's orchestration
Tool registry Hermes connects to CAW's MCP server at /mcp Hermes can invoke any CAW tool (code executor, RAG retrieval, web search) natively

Quick setup

# Install Hermes (one-time)
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

# Start both
CAW_API_KEY=dev-key ./scripts/hermes-caw.sh

Hermes is pre-configured via ~/.hermes/config.yaml (written during setup):

  • Provider: custom → http://localhost:8080/v1
  • MCP server: caw → http://localhost:8080/mcp with Authorization: Bearer ${CAW_API_KEY}

Usage patterns

Pattern Command
Interactive agent ./scripts/hermes-caw.sh then chat inside Hermes
Headless task hermes --no-interactive "<task>"
Attach to running CAW ./scripts/hermes-caw.sh --hermes-only
CAW only (no Hermes) ./scripts/hermes-caw.sh --caw-only

License

MIT — see LICENSE for details.

How it compares

Feature local-llm-gateway (CAW) LiteLLM Ollama LocalAI
OpenAI-compatible API ✅ ✅ ✅ ✅
Anthropic-compatible API ✅ ✅ ❌ ❌
Server-side agentic loop ✅ ❌ ❌ ❌
Tool calling for any model ✅ partial ❌ ❌
RAG (vector + FTS) ✅ ❌ ❌ partial
Web augmentation (DDG) ✅ ❌ ❌ ❌
Redis session memory ✅ ❌ ❌ ❌
KEDA autoscaling ✅ ❌ ❌ ❌
100% offline ✅ ✅ ✅ ✅

Keywords: local-llm · self-hosted-ai · llm-gateway · llm-agent · offline-ai · openai-compatible · anthropic-compatible · bitnet · gemma · ollama · rag · tool-calling · agentic-loop · go · fiber · redis · qdrant · postgresql · keda · helm · docker

About

Self-hosted AI agent gateway — give any local LLM (gemma:2b, llama3) tool calling, RAG, and a full agentic loop. 100% offline. OpenAI + Anthropic compatible.

Topics

go docker redis golang fiber gemma rag keda qdrant ollama offline-ai tool-calling llm-gateway local-llms self-hosted-ai openai-compatible anthropic-compatible agentic-loop

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