freeaiagent 1.0.0

Local AI agent service — HTTP endpoints, persistent context, multi-model LLM backend

freeaiagent

A local AI agent service you pip install once and call from anywhere.

Runs as a persistent HTTP server on localhost:7731. Stores conversation history in SQLite. Any app — script, CLI tool, personal project — can delegate tasks to it with a single HTTP call, no LLM code required on the caller's side.

Built on free LLM backends: Ollama (local, no API key) and Groq (free-tier cloud).

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Why

Embedding LLM logic directly into every app that needs it means duplicating prompt management, context handling, model configuration, and install detection across projects. When something changes — a new model, a different provider, a context bug — you fix it in every app separately.

freeaiagent is the single place that owns all of that. Your apps just call an endpoint.

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Install

pip install freeaiagent          # Ollama only
pip install "freeaiagent[groq]"  # + Groq support

Requires Python 3.10+.

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Free API Keys

No budget needed. Every option below is free.

Ollama — local, no key, no internet

Runs entirely on your machine. Best for privacy.

1. Download from ollama.com
2. Pull a model: ollama pull llama3.2:3b
3. freeaiagent start — done.

Groq — fastest free cloud inference

No credit card. ~1000 requests/day free.

1. Sign up at console.groq.com → API Keys → Create
2. Wire it in:

freeaiagent config set backends.groq.api_key gsk_...
freeaiagent config set default_backend groq
freeaiagent config set default_model openai/gpt-oss-20b

Google Gemini — 1500 free requests/day

Works via the existing OpenAI-compatible backend (Google provides an OpenAI-compatible endpoint).

1. Get a key at aistudio.google.com/apikey — free, no billing setup
2. Wire it in:

freeaiagent config set backends.gemini.type openai_compat
freeaiagent config set backends.gemini.base_url https://generativelanguage.googleapis.com/v1beta/openai
freeaiagent config set backends.gemini.api_key AIza...
freeaiagent config set default_backend gemini
freeaiagent config set default_model gemini-2.0-flash

Free Gemini models: gemini-2.0-flash, gemini-2.0-flash-lite, gemini-1.5-flash, gemini-1.5-flash-8b

OpenRouter — 50+ providers, free models available

Free credits on signup. Many models have a :free variant.

1. Sign up at openrouter.ai → Keys → Create
2. Wire it in:

freeaiagent config set backends.openrouter.type openai_compat
freeaiagent config set backends.openrouter.base_url https://openrouter.ai/api
freeaiagent config set backends.openrouter.api_key sk-or-...
freeaiagent config set default_backend openrouter
freeaiagent config set default_model meta-llama/llama-3.1-8b-instruct:free

Some free OpenRouter models: meta-llama/llama-3.1-8b-instruct:free, google/gemma-2-9b-it:free, mistralai/mistral-7b-instruct:free

LM Studio / Jan / LocalAI — local GUI apps, no key

These run models locally and expose an OpenAI-compatible server.

App	Download	Default port
LM Studio	lmstudio.ai	1234
Jan	jan.ai	1337
LocalAI	localai.io	8080

# Example: LM Studio running on default port
freeaiagent config set backends.lmstudio.type openai_compat
freeaiagent config set backends.lmstudio.base_url http://localhost:1234
freeaiagent config set default_backend lmstudio

No keys? Run freeaiagent keys anytime to see this setup guide.

---

Quick start

1. Start the server

freeaiagent start
# Running at http://localhost:7731
# API docs at http://localhost:7731/docs

2. Chat with it

freeaiagent chat
# You: what is the capital of France?
# Agent [llama3.2:3b]: Paris.

3. Call it from any app

import urllib.request, json

req = urllib.request.Request(
    "http://localhost:7731/chat",
    data=json.dumps({"message": "summarize this for me: ..."}).encode(),
    headers={"Content-Type": "application/json"},
)
response = json.loads(urllib.request.urlopen(req).read())["response"]

No pip dependencies needed in the calling app. Pure stdlib.

---

Chat UI

A browser-based chat interface — similar to ChatGPT, no signup, runs entirely on your machine.

Open it:

freeaiagent start   # then visit http://localhost:7731/ui

┌──────────────┬──────────────────────────────────────────────┐
│ + New Chat   │  Model: [openai/gpt-oss-20b ▾]               │
├──────────────┤──────────────────────────────────────────────┤
│ Chat 1       │                                              │
│ Chat 2       │    [assistant response]                      │
│ Chat 3  ···  │             [your message]                   │
│              │    [assistant response]                      │
│              ├──────────────────────────────────────────────┤
│              │  [ Type a message…                  ] [↑]   │
└──────────────┴──────────────────────────────────────────────┘

• Sessions sidebar — all your chats, ordered by most recent. Click to switch.
• New Chat — starts a fresh session instantly, no naming required.
• Model picker — dropdown populated live from your configured backends. Change mid-chat.
• Rename — double-click any session title to edit it inline.
• Delete — hover a session and click ×.
• Keyboard — Enter to send, Shift+Enter for a newline.

Sessions and history are stored in ~/.freeaiagent/context.db and persist across restarts.

---

CLI

freeaiagent start                          # start server (default port 7731)
freeaiagent start --port 8080              # custom port
freeaiagent start --reload                 # dev mode: auto-reload on code change

freeaiagent chat                           # interactive chat (default session)
freeaiagent chat "quick one-liner"         # single message, then exit
freeaiagent chat --session work            # chat in a named session
freeaiagent chat --session work "hi"       # single message to named session

freeaiagent sessions                       # list all sessions

freeaiagent task "explain this" \
  --input "$(cat file.py)"                 # one-shot task, no context read/written
freeaiagent task "translate to French" \
  --input "Hello world" \
  --model mistral:7b                       # override model for this task

freeaiagent status                         # health check + active backend/model
freeaiagent models                         # list models on active backend
freeaiagent keys                           # show where to get free API keys

freeaiagent context show                   # print conversation history (default session)
freeaiagent context show --session work    # print history for a named session
freeaiagent context clear                  # wipe default session
freeaiagent context clear --session work   # wipe a named session

freeaiagent config show                    # print current config
freeaiagent config set default_model mistral:7b
freeaiagent config set default_backend groq
freeaiagent config set backends.groq.api_key gsk_...

---

HTTP API

All endpoints accept and return JSON.

POST /chat

Send a message. Conversation history is read and updated automatically per session.

curl -X POST http://localhost:7731/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "what did I just ask you?", "session_id": "work"}'

{
  "response": "You asked me what you just asked me.",
  "model": "llama3.2:3b",
  "session_id": "work",
  "context_length": 4
}

Field	Type	Description
message	string	required
session_id	string	session to read/write context for (default: "default")
system	string	optional system prompt override for this message
model	string	optional model override for this message
backend	string	optional backend override for this message

---

POST /task

One-shot task. No context is read or written — clean slate every time.

curl -X POST http://localhost:7731/task \
  -H "Content-Type: application/json" \
  -d '{"task": "list all TODO comments", "input": "..."}'

{
  "result": "Line 42: TODO fix this\nLine 87: TODO add tests",
  "model": "llama3.2:3b"
}

Field	Type	Description
task	string	required — the instruction
input	string	optional — content to work on
model	string	optional — override model for this call
system	string	optional — override system prompt

---

GET /context?session=default

Returns the conversation history for a session.

{
  "messages": [
    {"role": "user", "content": "hello", "timestamp": "2026-06-21T10:00:00+00:00"},
    {"role": "assistant", "content": "hi there", "timestamp": "2026-06-21T10:00:01+00:00"}
  ],
  "total": 2,
  "session_id": "default"
}

DELETE /context?session=default

Clears conversation history for a session.

{"cleared": 4, "message": "Cleared 4 messages.", "session_id": "default"}

GET /sessions

Lists all sessions ordered by most recent activity.

{
  "sessions": [
    {
      "id": "work",
      "title": "Explain the difference between...",
      "model": "openai/gpt-oss-20b",
      "message_count": 14,
      "last_updated": "2026-06-21T10:45:00Z"
    }
  ]
}

POST /sessions

Create a session explicitly (also auto-created on first /chat message).

{"id": "my-project", "title": "My Project"}

PATCH /sessions/{id}

Rename a session: {"title": "New name"}

DELETE /sessions/{id}

Delete a session and all its messages.

GET /health

{"status": "ok", "active_backend": "ollama", "default_model": "llama3.2:3b"}

Returns "status": "degraded" with an "error" field if no backend is reachable.

GET /models

{"models": ["llama3.2:3b", "mistral:7b", "phi3:mini"]}

---

Configuration

Config lives at ~/.freeaiagent/config.json and is created on first run.

{
  "default_backend": "ollama",
  "default_model": "llama3.2:3b",
  "port": 7731,
  "backends": {
    "ollama": {
      "base_url": "http://localhost:11434"
    },
    "groq": {
      "api_key": ""
    }
  },
  "fallback_order": ["ollama", "groq"]
}

Edit directly or use freeaiagent config set <key> <value> with dotted keys:

freeaiagent config set port 8080
freeaiagent config set backends.ollama.base_url http://192.168.1.10:11434
freeaiagent config set backends.groq.api_key gsk_abc123
freeaiagent config set default_backend groq
freeaiagent config set default_model llama-3.1-8b-instant

---

Backends

Ollama (default)

Runs locally. No API key. No data leaves your machine.

# Install Ollama from https://ollama.com
ollama pull llama3.2:3b
freeaiagent start

Any model available on your Ollama instance works. Switch with:

freeaiagent config set default_model mistral:7b

Groq (free-tier cloud)

Fast inference. Free API key at console.groq.com.

freeaiagent config set backends.groq.api_key gsk_...
freeaiagent config set default_backend groq
freeaiagent config set default_model llama-3.1-8b-instant

Models are fetched live from the Groq API when your key is set — the list below is the fallback.

Model	Context	Notes
openai/gpt-oss-20b	131k	Current production — replaces llama-3.1-8b
openai/gpt-oss-120b	131k	Current production — most capable
groq/compound	131k	Agentic system with web search + code execution
groq/compound-mini	131k	Lighter compound system
llama-3.1-8b-instant	131k	Deprecated Aug 16 2026
llama-3.3-70b-versatile	131k	Deprecated Aug 16 2026
qwen/qwen3.6-27b	131k	Preview
qwen/qwen3-32b	131k	Preview — deprecated Jul 17 2026
meta-llama/llama-4-scout-17b-16e-instruct	131k	Preview — deprecated Jul 17 2026

Automatic fallback

If the default backend is unreachable, freeaiagent tries the next one in fallback_order automatically. No configuration needed for this to work — just have both set up.

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Using from another app

The agent runs as a separate process. Your app calls it over HTTP — no LLM dependencies, no model management, no context handling in your code.

Python (stdlib only):

import urllib.request, json

def ask(message):
    body = json.dumps({"message": message}).encode()
    req = urllib.request.Request(
        "http://localhost:7731/chat",
        data=body,
        headers={"Content-Type": "application/json"},
    )
    return json.loads(urllib.request.urlopen(req).read())["response"]

def run_task(task, input_text=None):
    body = json.dumps({"task": task, "input": input_text}).encode()
    req = urllib.request.Request(
        "http://localhost:7731/task",
        data=body,
        headers={"Content-Type": "application/json"},
    )
    return json.loads(urllib.request.urlopen(req).read())["result"]

Shell:

curl -s -X POST http://localhost:7731/task \
  -H "Content-Type: application/json" \
  -d "{\"task\": \"summarize\", \"input\": \"$(cat notes.txt)\"}" \
  | python -c "import sys,json; print(json.load(sys.stdin)['result'])"

JavaScript / Node:

const res = await fetch("http://localhost:7731/chat", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ message: "hello" }),
});
const { response } = await res.json();

---

Context storage

Conversation history is stored in ~/.freeaiagent/context.db (SQLite). It persists across server restarts.

Each session has its own history. Clear a session any time:

freeaiagent context clear                  # clear the default session
freeaiagent context clear --session work   # clear a named session
# or via HTTP:
curl -X DELETE "http://localhost:7731/context?session=work"
curl -X DELETE "http://localhost:7731/sessions/work"  # also deletes session record

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Roadmap

Done

• Ollama, Groq, OpenAI-compatible backends (LM Studio, Gemini, OpenRouter, Jan, LocalAI, llamafile)
• Per-call model and backend overrides
• Sliding window context (max_messages config)
• Session-based context windows (session_id on all endpoints)
• Chat web UI at localhost:7731/ui

Phase 3 — Auto caller detection Sessions created automatically per caller using X-Caller-ID header or caller port. Zero config for multi-app setups.

Phase 4 — More features

• llamafile dedicated backend (auto-start the .exe, no Ollama install)
• Streaming responses (/chat/stream SSE)
• Ensemble inference (fan out same query to multiple models, pick the best output)
• Tool use / function calling

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Development

git clone <repo>
cd freeaiagent
pip install -e ".[dev,groq]"

pytest                              # unit + integration (no LLM needed)
pytest tests/smoke/ -m smoke -v    # smoke tests (requires Ollama running)

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License

MIT