freeaiagent — free local AI agent, zero setup

Run powerful AI models fully on-device. No cloud, no cost, no compromise on privacy.

Run a local AI agent with one pip install. No OpenAI key, no cloud required — it downloads and runs a local LLM for you, or connects to free cloud tiers (Groq, Gemini, OpenRouter). Any app calls it over HTTP with no LLM code on its side.

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:

• Local, zero-install — downloads and runs a local GGUF model for you via llamafile (no Ollama, no keys). Supports 1B–14B models.
• SDX — text + vision in one bundle — attach images in any chat; a local vision model reads them and the conversation continues seamlessly. Five tiers from ~3 GB to ~14 GB. See SDX.
• In-process (optional) — load a GGUF straight into Python via llama-cpp-python, no subprocess or local server.
• Ollama — local, no key, if you already use it.
• Free cloud tiers — Groq, Google Gemini, OpenRouter, Together, Cerebras (bring a free API key).

Plus streaming, tool/function calling, per-app sessions, summarizing & per-backend context windows, hybrid RAG retrieval over chat history (SDX), and ensemble inference out of the box.

Call it from Python with the built-in Client SDK (one import, live download progress, no HTTP boilerplate), or point any OpenAI SDK / LangChain / LlamaIndex client at its drop-in /v1 endpoint. Install it as an auto-start service so it's always available.

Architecture

Your apps talk to one local HTTP service — over plain HTTP, the Python Client SDK, or the OpenAI-compatible /v1 API. freeaiagent owns model routing, persistent context, the automatic fallback chain, and tool calls — so no app needs any LLM code, keys, or model management of its own.

flowchart TD
    subgraph apps["YOUR APPS"]
        A1["Web app<br/>Flask / Django / FastAPI"]
        A2["Python app<br/>freeaiagent.Client SDK"]
        A3["OpenAI SDK / LangChain<br/>→ /v1 endpoint"]
        A4["Browser<br/>built-in Chat UI /ui"]
    end

    apps -->|"HTTP · SDK · OpenAI /v1"| API

    API["HTTP API · localhost:7731<br/>/chat · /task · /chat/stream · /tools · /sessions<br/>/pull/stream · /models · /config · /v1/chat/completions"]

    API --> CORE

    subgraph CORE["freeaiagent server · FastAPI"]
        R["Router<br/>backend + model per call"]
        S["Sessions + Context<br/>SQLite, per-app (X-Caller-ID)"]
        F["Fallback chain<br/>local → ollama → cloud"]
        T["Tool loop<br/>calls your HTTP tools"]
    end

    CORE -->|"whichever backend is available"| B1 & B2 & B3

    B1["Local model (default)<br/>llamafile + GGUF · 1B–14B<br/>zero install · offline · no key"]
    B2["Ollama (optional)<br/>if you already run it"]
    B3["Free cloud tiers<br/>Groq · Gemini · OpenRouter<br/>Together · Cerebras"]

    DB[("Local storage ~/.freeaiagent/<br/>config.json · context.db · models/ · engine/")]
    CORE -.-> DB

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.

Install

pip install freeaiagent

Requires Python 3.10+. That's everything — local models and all cloud presets work with no extra packages. (For the optional in-process backend: pip install "freeaiagent[llama-cpp]".) Then either pull a local model or add a free key (below).

freeaiagent pull          # one-time local model download (~2.3 GB), then fully offline
freeaiagent start --open  # starts the server and opens the Chat UI in your browser

Backends & free keys

No budget needed. Every option below is free.

Local model — zero install, no key, fully offline (default)

A self-contained model the agent downloads and runs for you. No Ollama, no service.

freeaiagent pull            # one-time ~2.3 GB download (Llama-3.2-3B), with a progress bar
freeaiagent start --open    # starts the server and opens the Chat UI in your browser

Pick a different size or browse the catalog:

freeaiagent models --available     # see all local models (1B → 14B)
freeaiagent pull qwen2.5-7b        # a stronger model (also fetches a one-time engine)
freeaiagent config set default_model qwen2.5-7b

Ollama — local, no key, no internet

Runs entirely on your machine. Best if you already use Ollama.

1. Download from ollama.com
2. Pull a model: ollama pull llama3.2:3b
3. freeaiagent config set default_backend ollama && freeaiagent start

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

Cloud presets — Gemini, OpenRouter, Together, Cerebras

These are built in as presets. Just add a key, pick the backend, pick a model — no base_url/type wiring needed.

Google Gemini — 1500 free requests/day. Key: aistudio.google.com/apikey

freeaiagent config set backends.gemini.api_key AIza...
freeaiagent config set default_backend gemini
freeaiagent config set default_model gemini-2.0-flash

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

OpenRouter — many :free models. Key: openrouter.ai

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

Together AI — free tier. Key: api.together.xyz

freeaiagent config set backends.together.api_key ...
freeaiagent config set default_backend together
freeaiagent config set default_model meta-llama/Llama-3.3-70B-Instruct-Turbo-Free

Cerebras — free tier, very fast. Key: cloud.cerebras.ai

freeaiagent config set backends.cerebras.api_key csk-...
freeaiagent config set default_backend cerebras
freeaiagent config set default_model llama-3.3-70b

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

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

# 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.

Local models

The local backend is self-contained — no Ollama, no Python ML deps. It downloads a model and runs it as a local OpenAI-compatible server.

freeaiagent models --available    # browse the catalog

 * llama-3.2-1b    1.3 GB  RAM>= 2GB  [low ] fused   Fastest. Classify / extract / tag.
   gemma-2-2b      2.0 GB  RAM>= 4GB  [mid ] fused   Concise; strong at short summaries.
   llama-3.2-3b    2.3 GB  RAM>= 4GB  [mid ] fused   Balanced default. Light reasoning / Q&A.
   phi-3-mini      2.4 GB  RAM>= 4GB  [mid ] fused   Strong reasoning per byte.
   qwen2.5-7b      4.7 GB  RAM>= 8GB  [high] engine  Strong reasoning, Q&A, summaries.
   llama-3.1-8b    4.9 GB  RAM>= 8GB  [high] engine  Broad capability, 128k context.
   qwen2.5-14b     9.0 GB  RAM>=16GB  [max ] engine  Strongest local option.

• fused models are a single self-contained file (kept under 4 GB so they run on Windows).
• engine models are external GGUF weights run via a one-time ~305 MB llamafile engine — this is how 7B–14B models run anywhere, including Windows.

freeaiagent pull llama-3.2-3b          # a catalog model by name
freeaiagent pull qwen2.5-7b            # also fetches the shared engine, once
freeaiagent config set default_model qwen2.5-7b
freeaiagent rm qwen2.5-7b              # delete a downloaded model to free disk

Interrupted downloads resume automatically — re-running pull continues a partial .part file via an HTTP range request instead of starting over. Catalog entries may pin a SHA256 checksum, which is verified after download.

Any model from HuggingFace

Search the whole Hub for GGUF models and pull any of them — no key for public repos.

freeaiagent search qwen2.5                         # find GGUF repos (most-downloaded first)
freeaiagent search bartowski/Qwen2.5-7B-Instruct-GGUF   # list that repo's GGUF files + sizes
freeaiagent pull hf:bartowski/Qwen2.5-7B-Instruct-GGUF/Qwen2.5-7B-Instruct-Q4_K_M.gguf

Models download to ~/.freeaiagent/models/, the engine to ~/.freeaiagent/engine/.

SDX — chat with images, fully local

SDX (Smart Decision eXecution) is a compound engine that bundles a text model + vision model behind one model name. Attach an image in the Chat UI (or pass image on /chat) and SDX reads it locally; text-only turns skip the vision model entirely. One pull fetches the whole bundle — nothing ever leaves your machine.

freeaiagent pull sdx-standard              # one bundle: text + vision + embedder
freeaiagent config set default_backend sdx
freeaiagent start --open                   # attach images right in the Chat UI

Pick a tier with freeaiagent config set backends.sdx.model sdx-plus. Each tier uses its model's native chat template and real tokenizer-based context budgeting. Switching tiers automatically frees the previous one's RAM; the vision model unloads after 5 idle minutes.

How images work: the vision model extracts a description that is stored in the session as text ([SDX-Image]: ...). That means image content stays part of the conversation permanently — later turns (and retrieval, below) can refer back to a chart or screenshot long after the image itself is gone.

Hybrid RAG over your chat history (SDX-only)

Long sessions forget their beginnings under any sliding window. With context_strategy: rag, SDX embeds every exchange locally (shared ~84 MB nomic embedder, cached in SQLite) and blends the most relevant earlier exchanges with the recent window — clearly fenced as memory, so the model never sees a scrambled dialogue. Because images become text, pictures are retrievable too ("what was that error screenshot from earlier?").

freeaiagent config set backends.sdx.context_strategy rag

Every /chat response reports what actually ran — "sdx": {"strategy_used": "rag"} — and RAG silently falls back to the recency window if anything is missing, so it can never break a chat. Tune with backends.sdx.rag.top_k, .min_similarity, and .recent_pairs.

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 [llama-3.2-3b]: 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.
• Images — on an SDX model, attach an image to any message; you'll see an "Analyzing image…" step, then the reply (see SDX).

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 the active backend
freeaiagent models --available             # browse the local model catalog
freeaiagent keys                           # show where to get free API keys

freeaiagent pull                           # download the default local model
freeaiagent pull qwen2.5-7b                # download a catalog model by name
freeaiagent pull sdx-standard              # download an SDX text+vision bundle
freeaiagent pull hf:owner/repo/file.gguf   # download any GGUF from HuggingFace
freeaiagent rm qwen2.5-7b                   # delete a downloaded model (frees disk)
freeaiagent search qwen2.5                  # find GGUF models on HuggingFace
freeaiagent search owner/repo              # list a repo's GGUF files

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_...

freeaiagent install                        # auto-start at login (no admin)
freeaiagent service status                 # is the auto-start service installed?
freeaiagent uninstall                      # remove the auto-start service

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
}

On an SDX backend the response also includes "sdx": {"strategy_used": "rag" | "recency"} — which context strategy actually built this turn.

Auto sessions: if you don't pass session_id, the session is taken from the X-Caller-ID request header — so an app can set one header and get its own context thread automatically. Resolution order: body session_id → X-Caller-ID → "default".

POST /chat/stream

Same as /chat, but streams the reply token-by-token as Server-Sent Events. The full response is persisted to the session when the stream finishes.

curl -N -X POST http://localhost:7731/chat/stream \
  -H "Content-Type: application/json" \
  -d '{"message": "write a haiku about the sea"}'

data: {"token": "Waves"}
data: {"token": " roll"}
data: {"token": " in"}
data: [DONE]

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"
}

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"]}

Tools / function calling

Register an HTTP tool once; the model may call it during a /chat with tools: true. When the model calls a tool, freeaiagent POSTs the arguments to your tool's endpoint and feeds the result back, looping until the model produces an answer.

# Register a tool
curl -X POST http://localhost:7731/tools/register \
  -H "Content-Type: application/json" \
  -d '{
        "name": "get_weather",
        "description": "Get the current weather for a city",
        "endpoint": "http://localhost:9000/weather",
        "parameters": {"type": "object",
                       "properties": {"city": {"type": "string"}},
                       "required": ["city"]}
      }'

# Use it
curl -X POST http://localhost:7731/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "what is the weather in Paris?", "tools": true}'

Tool calling needs a backend/model that supports the OpenAI tool protocol (Groq, most hosted providers, some local models). Backends without support answer normally instead.

Models & config management

POST /pull/stream

Server-side model download, streamed as SSE so a UI can show a real progress bar. GGUF models emit an engine phase (the one-time shared runtime) before model. One download at a time (409 otherwise); an unknown model is a 400.

data: {"type": "start",    "phase": "model", "label": "qwen2.5-7b", "total_mb": 4700}
data: {"type": "progress", "phase": "model", "pct": 12, "downloaded_mb": 564, "speed_mbps": 8.1}
data: {"type": "done",     "path": "~/.freeaiagent/models/Qwen2.5-7B-Instruct-Q4_K_M.gguf"}
data: [DONE]

OpenAI-compatible (/v1)

Point any OpenAI SDK / LangChain / LlamaIndex client at http://localhost:7731/v1.

Configuration

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

{
  "default_backend": "llamafile",
  "default_model": "llama-3.2-3b",
  "port": 7731,
  "max_messages": 0,
  "context_strategy": "sliding",
  "summarize_threshold": 40,
  "summarize_batch": 30,
  "summarize_model": null,
  "backends": {
    "llamafile": {"type": "llamafile", "port": 8080, "auto_download": false},
    "llama_cpp": {"type": "llama_cpp", "n_ctx": 4096, "n_gpu_layers": 0},
    "sdx":       {"type": "sdx", "model": "sdx-standard", "n_gpu_layers": 0,
                  "context_strategy": "recency",
                  "rag": {"top_k": 6, "min_similarity": 0.30, "recent_pairs": 4},
                  "keep_loaded": false, "vision_idle_unload_s": 300},
    "ollama":    {"base_url": "http://localhost:11434"},
    "groq":      {"api_key": ""},
    "together":   {"type": "openai_compat", "base_url": "https://api.together.xyz", "api_key": ""},
    "openrouter": {"type": "openai_compat", "base_url": "https://openrouter.ai/api", "api_key": ""},
    "cerebras":   {"type": "openai_compat", "base_url": "https://api.cerebras.ai", "api_key": ""},
    "gemini":     {"type": "openai_compat",
                   "base_url": "https://generativelanguage.googleapis.com/v1beta/openai",
                   "api_prefix": "", "api_key": ""}
  },
  "ensemble": {"enabled": false, "models": [], "judge": null, "strategy": "llm_judge"},
  "fallback_order": ["llamafile", "ollama", "groq"]
}

Each backend may also set its own max_messages (overriding the global one), e.g. a short window for an 8k model and a long one for a 128k model. See Context strategies & ensemble.

Cloud presets are inert until you set their api_key. default_model for the local backend is a catalog name (freeaiagent models --available); legacy values are migrated automatically.

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

Context strategies & ensemble

Context window

By default the agent uses a sliding window: only the last max_messages messages are sent to the model (0 = unlimited). The effective window resolves in order: per-call max_messages (on /chat) → backends.<name>.max_messages → global max_messages.

freeaiagent config set max_messages 20                     # global window
freeaiagent config set backends.groq.max_messages 100      # per-backend override
# or per request:
curl -X POST http://localhost:7731/chat -H "Content-Type: application/json" \
  -d '{"message": "hi", "max_messages": 10}'

Summarization

Instead of dropping old messages, summarize them: once a session grows past summarize_threshold, the oldest summarize_batch messages are folded into a single system "memory" block — so long sessions keep their early context (decisions, constraints, names) at the cost of one extra LLM call per fold.

freeaiagent config set context_strategy summarize
freeaiagent config set summarize_threshold 40    # start summarizing past 40 messages
freeaiagent config set summarize_batch 30        # fold the oldest 30 into one summary
freeaiagent config set summarize_model llama-3.2-3b   # optional; defaults to the chat model

Ensemble inference

Send the same prompt to several models in parallel and return the best answer — catches per-model blind spots for high-stakes one-shot questions (costs N× tokens, ~max-of-N latency). All ensemble models run on the active backend, so they must be models it can serve.

# Per request — pass an explicit model list:
curl -X POST http://localhost:7731/chat -H "Content-Type: application/json" \
  -d '{"message": "explain X", "ensemble": ["llama-3.1-8b-instant", "llama-3.3-70b-versatile"]}'

# Or configure it once, then send "ensemble": true (or set enabled):
freeaiagent config set ensemble.models '["llama-3.1-8b-instant", "llama-3.3-70b-versatile"]'
freeaiagent config set ensemble.strategy llm_judge    # llm_judge | longest | majority
freeaiagent config set ensemble.enabled true

The response includes an ensemble_votes array (each model's answer, or its error) alongside the winning response. Judge strategies: llm_judge (a small model picks the best; falls back to longest on failure), longest (longest answer, penalised for repetition), majority (most common answer). Failed models are dropped; only an all-failed ensemble errors.

Backend reference

Local (llamafile) — default

Self-contained. No Ollama, no key, no data leaves your machine. Downloads a model once and runs it locally. See Local models for the catalog, GGUF/engine models, and HuggingFace search.

freeaiagent pull          # download the default local model
freeaiagent start

In-process (llama-cpp-python) — optional

Loads a GGUF model directly into the Python process — no llamafile subprocess, no local HTTP hop. Useful if you want a pure-Python path or finer control over n_ctx / GPU offload. Reuses the same GGUF weights freeaiagent pull downloads.

pip install "freeaiagent[llama-cpp]"     # optional extra (compiles llama-cpp-python)
freeaiagent pull qwen2.5-7b              # any GGUF (engine-mode) catalog model
freeaiagent config set default_backend llama_cpp
freeaiagent config set backends.llama_cpp.n_gpu_layers 35   # offload to GPU (0 = CPU only)

Inert until both the package and a GGUF model are present, so it never interferes with the default setup.

Ollama

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.

Cloud presets

together, openrouter, cerebras, and gemini are built-in OpenAI-compatible presets — set an api_key and select the backend (see Cloud presets). Add any other OpenAI-compatible server (LM Studio, Jan, LocalAI) with type: openai_compat and a base_url.

Automatic fallback

If the default backend is unreachable, freeaiagent tries the next one in fallback_order automatically (default: llamafile → ollama → groq). So if a cloud key is exhausted or you're offline, the local model still answers.

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 SDK (recommended)

pip install freeaiagent ships a synchronous Client with full server parity and zero HTTP boilerplate. Streaming and downloads are plain iterators — no async.

from freeaiagent import Client

# name → X-Caller-ID, so this app gets its own context thread.
# auto_start=True launches the server if it isn't already running.
agent = Client(name="my-app", auto_start=True)

# Chat (context preserved per session)
print(agent.chat("hello"))
print(agent.chat("write a tagline", session="marketing", model="qwen2.5-7b"))

# Stream tokens
for token in agent.stream("write a haiku about caching"):
    print(token, end="", flush=True)

# One-shot task (no shared context)
print(agent.task("summarize concisely", input=long_text))

# Download a model with live progress (for a real progress bar in your UI)
for p in agent.pull("qwen2.5-7b"):
    if p.type == "progress":
        print(f"[{p.phase}] {p.pct:.0f}%  {p.downloaded_mb:.0f}/{p.total_mb:.0f} MB")
    elif p.type == "done":
        print("Saved to", p.path)

# Namespaced management — mirrors the CLI
agent.models.catalog()        # downloadable models, each flagged installed
agent.models.installed()      # local files on disk
agent.sessions.list()
agent.context.get(session="marketing")
agent.config.set("default_backend", "groq")
agent.tools.register("get_weather", description="Weather for a city",
                     endpoint="http://localhost:9000/weather",
                     parameters={"type": "object", "properties": {"city": {"type": "string"}}})

agent.is_running()            # fast health check

The client auto-discovers the running port via ~/.freeaiagent/server.json, so apps survive port changes with no config. Errors are typed: ServerNotRunning, BackendUnavailable, DownloadInProgress.

Install once so it's always up and use Client(auto_start=False):

freeaiagent install          # auto-start at login (no admin); Linux/macOS/Windows
freeaiagent service status
freeaiagent uninstall

Drop-in OpenAI API

freeaiagent speaks the OpenAI wire protocol, so anything built on the OpenAI SDK, LangChain, or LlamaIndex works unchanged — just point base_url at it:

from openai import OpenAI
llm = OpenAI(base_url="http://localhost:7731/v1", api_key="none")
llm.chat.completions.create(model="qwen2.5-7b", messages=[{"role": "user", "content": "hi"}])

Raw HTTP

If you'd rather not add a dependency, call the endpoints directly.

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",
            "X-Caller-ID": "my-app",   # this app gets its own context thread
        },
    )
    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

Roadmap

Done

• Zero-install local backend (llamafile) with a model catalog, engine mode for 7B–14B GGUF models, and HuggingFace search/pull
• Ollama, Groq, and OpenAI-compatible cloud presets (Gemini, OpenRouter, Together, Cerebras; plus LM Studio, Jan, LocalAI)
• Per-call model and backend overrides
• Sliding window context (max_messages) + per-backend & per-call context windows
• Summarization context strategy (fold old messages into a memory block)
• Auto caller detection (X-Caller-ID header → per-app session)
• Streaming responses (/chat/stream SSE)
• Tool use / function calling (/tools, tools=true)
• Chat web UI at localhost:7731/ui
• Python SDK (freeaiagent.Client) with live pull progress and port auto-discovery
• Server-side streaming downloads (/pull/stream), model/config management endpoints
• OpenAI-compatible /v1/chat/completions + /v1/models proxy
• Auto-start service install (freeaiagent install) on Linux/macOS/Windows
• Ensemble inference (fan out the same query to multiple models, pick the best output)
• Optional in-process engine (llama-cpp-python)
• Resumable downloads + SHA256 checksum verification, freeaiagent rm
• SDX compound engine — text + vision in one local bundle, 5 tiers (Nano → Max), image chat in the UI and API
• SDX v2 — native chat templates per model, real tokenizer-based context budgets, engine manager with tier eviction and vision idle-unload
• Hybrid RAG over chat history (SDX) — local embeddings, semantic retrieval blended with a recency window, retrievable image content, strategy_used response metadata
• Concurrency hardening — serialized generation per model, safe engine eviction under load, per-call stop

Planned

• Hardware auto-detect (freeaiagent doctor) — set GPU offload automatically, recommend a model tier
• Structured output — JSON-schema-constrained generation (GBNF grammars)
• /v1/embeddings endpoint (OpenAI-compatible, backed by the local embedder)
• Document RAG — attach a folder, get indexed + cited answers
• Quality evals per catalog model (text + vision), published honestly
• Custom catalog entries

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)

License

MIT