llmai-agent 0.2.3

Local AI coding agent — Ollama + any model

LLMai

A local-first AI coding agent with three layers of awareness.
Runs on your own Ollama. Remembers across sessions. Searches your org's knowledge before writing code. Observable end-to-end.

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What LLMai is

A privacy-first AI coding agent that runs the model locally and gives it three distinct kinds of awareness — not just one giant chat window:

Layer	Backed by	What it gives the agent
Operational	Dynatrace (OpenTelemetry → Bindplane)	Every tool call traced: latency, token count, permission outcome, success/error
Personal	MongoDB Atlas (per-workspace)	Recalls your past sessions, decisions, and extracted facts. Boots warm
Organizational	Elastic (per-org)	Hybrid search over GitLab issues, CI failure logs, docs — checks "have we seen this before?" before writing code

All three are opt-in. The core agent runs 100% locally with no external dependencies. Each integration is one config flip away.

Live demo: ll-mai.vercel.app

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Stack at a glance

                                        ┌─ search_knowledge ──► Elastic (issues + docs)
                                        │
   Browser / CLI ──► Agent Loop ────────┼─ recall_memory ─────► MongoDB Atlas
                          │             │                       (per-workspace sessions
                          │             │                        + summaries + knowledge)
                          │             │
                          │             └─ query_logs ────────► Elastic ES|QL
                          │                                     (pipeline + agent self-logs)
                          │
                          ▼
                     OTel spans
                          │
                          ▼
                  Bindplane ──┬──► Dynatrace (traces + metrics)
                              │
                              └──► Elastic llmai-agent-logs
                                   (so the agent can query itself)

LLM backend: Ollama localhost:11434 — Gemini / Groq optional

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Two interfaces

	How to run	Best for
CLI REPL	llmai	Terminal power users
Web UI	llmai-server → http://localhost:7777	Browser, streaming, permission cards

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Quick start (core only, no partner integrations)

Install from PyPI (the distribution is named llmai-agent because bare llmai was already taken; the import path is still import llmai):

pip install llmai-agent
ollama serve
ollama pull qwen2.5-coder
llmai-server   # opens http://localhost:7777

Or install from source for development:

git clone https://github.com/sechan9999/LLMai.git
cd LLMai
pip install -e .

ollama serve
ollama pull qwen2.5-coder

llmai-server   # opens http://localhost:7777

That's it. The agent has 8 core tools (read/write/edit files, run shell, search code, list files, fetch URL, mkdir) plus 11 GitLab tools when GITLAB_TOKEN is set. Read-only ops auto-approve; writes and shell commands prompt for permission.

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Adding the three layers (10-15 min each)

1. Dynatrace — observe every tool call

make install-telemetry          # adds opentelemetry packages

# in .env (copy from .env.example):
DT_ENDPOINT=https://<your>.live.dynatrace.com
DT_API_TOKEN=dt0c01.YOUR_TOKEN

make bindplane-up               # starts the OTel collector locally

export LLMAI_OTEL_ENABLED=true
export LLMAI_OTEL_ENDPOINT=http://localhost:4318
llmai-server

Spans: agent.turn → agent.iteration → llm.chat + tool.invocation. Metrics: tool invocation counts, LLM latency, token histograms.

Full guide: docs/dynatrace-setup.md

2. MongoDB Atlas — remember across sessions

make install-memory             # adds pymongo
ollama pull nomic-embed-text    # embedding model

# in config.json or env:
LLMAI_MEMORY_ENABLED=true
LLMAI_MEMORY_URI=mongodb+srv://USER:[e-mail]/

python scripts/setup_atlas_indexes.py   # one-time vector index bootstrap
llmai-server

Three collections: sessions, summaries (vector-embedded), knowledge (extracted facts, vector-embedded). New tool: recall_memory for the agent. On each new session, the 3 most recent prior summaries are auto-injected as a system message.

Full guide: docs/atlas-setup.md

3. Elastic — search org knowledge before writing code

make install-elastic            # adds elasticsearch client
make elastic-up                 # local ES + Kibana via docker
make elastic-setup              # creates llmai-* indices

# optional: pull org knowledge in
export GITLAB_TOKEN=glpat-...
export GITLAB_PROJECT=group/project
make elastic-ingest             # pulls last 500 issues + 50 failed pipelines

export LLMAI_ELASTIC_ENABLED=true
export LLMAI_ELASTIC_URL=http://localhost:9200
llmai-server

Two tools: search_knowledge (hybrid keyword + dense vector, RRF-fused, auto-approved) and query_logs (raw ES|QL, permission-gated). System prompt nudges the model to call search_knowledge before writing code that touches error paths or external APIs.

Full guide: docs/elastic-setup.md

One-command demo bootstrap

make demo-up           # starts Elastic + Kibana + Bindplane
make demo-bootstrap    # pulls embed model + creates ES indices
make demo-status       # health check across the whole stack

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Key features

• 100% local by default — no API keys, no cloud, nothing leaves your machine
• Agentic loop — observe → judge → act, up to 20 iterations per turn
• Permission gates — read-only auto-approves; writes and shell prompt
• Three-layer awareness — operational (Dynatrace), personal (Atlas), org (Elastic) — all opt-in
• MCP-compatible tool shapes — recall_memory, search_knowledge, query_logs mirror the official MCP server contracts
• Dual tool-calling modes — native OpenAI function calling for capable models; XML fallback for gemma3, phi3, etc.
• Context compression — auto-summarizes when conversation exceeds ~50k tokens
• Workspace sandboxing — file ops restricted to WORKSPACE_ROOT; dangerous command patterns blocked
• Provider-agnostic — Ollama, Gemini, Groq (cloud fallback for the hosted demo)
• Dual interface — CLI REPL + WebSocket-streaming Web UI

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Tools (when their layer is enabled)

Category	Tools	Default permission
Core (8)	read_file, write_file, edit_file, run_command, list_files, search_code, fetch_url, create_directory	reads allow, writes/shell ask
GitLab (11)	gitlab_list_issues, gitlab_get_mr, gitlab_get_job_log, …	reads allow, mutations ask
Memory (1)	recall_memory	allow
Elastic (2)	search_knowledge, query_logs	allow / ask

Tools are registered conditionally — the model never sees a tool whose backend isn't connected.

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Configuration

config.json (or env vars — env always wins):

{
  "ollama_url": "http://localhost:11434",
  "model": "qwen2.5-coder",
  "permissions": { ... },
  "telemetry": { "enabled": false, "endpoint": "http://localhost:4318", ... },
  "memory":    { "enabled": false, "uri": "mongodb+srv://...", ... },
  "elastic":   { "enabled": false, "url": "http://localhost:9200", ... }
}

Full example: config.example.json.

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CLI commands

Command	Description
/reset	Clear conversation context (and finalize session memory)
/model <name>	Switch model
/models	List Ollama models locally
/tokens	Show estimated token count
/perms	Show current permission settings
/compress	Force context compression now
/exit	Quit

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Supported models

Native tool calling (recommended): Qwen 2.5 / 2.5-Coder / 3, Llama 3.1+, Mistral NeMo, FireFunction, Command-R(+).

XML fallback: gemma3, phi3, mistral — anything OpenAI-incompatible. Slightly lower fidelity but functional.

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Cloud backends (optional)

The agent speaks the OpenAI /v1/chat/completions contract.

# Gemini (via AI Studio key — also works for Vertex AI compat endpoint)
export GEMINI_API_KEY=...

# Any OpenAI-compat endpoint (LM Studio, vLLM, custom):
# set "ollama_url" in config.json

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Project structure

LLMai/
├── llmai/                # Python package (formerly vixcode)
│   ├── agent.py          # Sync CLI agent loop
│   ├── main.py           # CLI REPL entry point
│   ├── llm.py            # OpenAI-compat HTTP client
│   ├── tools.py          # 8 core tools + conditional registration
│   ├── gitlab_tools.py   # 11 GitLab tools
│   ├── permissions.py    # allow / ask / deny system
│   ├── telemetry.py      # OpenTelemetry init + span context managers
│   ├── memory/           # MongoDB Atlas persistent memory
│   │   ├── store.py
│   │   ├── embeddings.py
│   │   └── recall_tool.py
│   └── elastic/          # Elasticsearch knowledge search + log analytics
│       ├── client.py
│       ├── search_tool.py
│       └── query_tool.py
├── server/               # FastAPI + WebSocket Web UI
│   ├── app.py
│   ├── agent_ws.py       # Async agent loop (native + XML modes)
│   └── static/index.html
├── website/              # Landing page (Vercel)
│   └── index.html
├── api/chat.js           # Vercel serverless cloud fallback (Groq)
├── scripts/              # Bootstrap scripts for partner integrations
│   ├── setup_atlas_indexes.py
│   ├── elastic_setup_indexes.py
│   ├── elastic_ingest_gitlab.py
│   └── elastic_ingest_logs.py
├── bindplane/config.yaml             # OTel collector → Dynatrace + Elastic
├── docker-compose.bindplane.yml      # Bindplane container
├── docker-compose.elastic.yml        # Elasticsearch + Kibana
├── Makefile                          # Common dev / demo tasks
├── docs/
│   ├── dynatrace-setup.md
│   ├── atlas-setup.md
│   └── elastic-setup.md
└── tests/                # 103 passing

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Documentation

• Dynatrace observability — OpenTelemetry spans, metrics, Bindplane pipeline
• MongoDB Atlas memory — cross-session continuity, semantic recall
• Elastic knowledge search — hybrid search, ES|QL, agent self-logs
• Local LLM Setup — best practices for Ollama
• Permission System — allow/ask/deny configuration
• Tool System — tool definitions and sandboxing
• OSS Comparison — vs. Claude Code, Aider, Open Interpreter, Goose

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License

MIT

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Acknowledgments

• Ollama — Local LLM runtime
• FastAPI — Web framework
• Rich — Terminal formatting
• OpenTelemetry — Observability standard
• Bindplane — OTel collector
• Dynatrace, MongoDB Atlas, Elastic — Hackathon partner backends