Configuration — FeLLAMA

config.toml

The main configuration file lives at ~/.fellama/config.toml. All fields are optional — FeLLAMA uses sensible defaults and can auto-discover models from the endpoint.

~/.fellama/config.toml

# LLM endpoint (OpenAI-compatible API)
endpoint = "http://localhost:8000/v1"

# Model identifier
model = "gpt-oss:120b"

# Agent temperature (0.0 - 1.0)
agent_temperature = 0.6

# Browserless endpoint for web automation
browserless_endpoint = "ws://localhost:3000"

# Enable trace logging for debugging
enable_trace_log = false

# Enable smartweb agent trace logging
enable_smartweb_tracelog = false

# Embedding model endpoint (for Vector DB)
embedding_endpoint = "http://localhost:8000/v1"

# Embedding model name
embedding_model = "text-embedding-model"

# Number of results for vector search
vector_top_k = 5

# Directories to search for skill packages
skills_paths = ["~/.fellama/skills/"]

Field Reference

endpoint string

Base URL for the LLM API. Must be OpenAI-compatible (/v1/chat/completions). Works with vLLM, llama.cpp, Ollama, LiteLLM, OpenAI, and other compatible servers.

Default: "http://localhost:8000/v1"

model string

Model identifier as known by the endpoint. If omitted, FeLLAMA auto-discovers from /v1/models.

agent_temperature float (0.0 - 1.0)

Controls LLM creativity. Lower values produce more deterministic output. Higher values increase variation.

Default: 0.6

browserless_endpoint string

WebSocket URL for the Browserless/Chrome DevTools Protocol service. Required only for the SmartWeb agent.

Default: "ws://localhost:3000"

enable_trace_log boolean

When true, logs every message crossing a process or network boundary. Includes LLM request/response bodies, WebSocket payloads, and subprocess I/O.

Default: false

enable_smartweb_tracelog boolean

Enables additional trace logging specific to the SmartWeb agent, including browser actions and page interactions.

Default: false

embedding_endpoint string

Base URL for the embedding model API. Must serve /v1/embeddings. Only needed for the Vector DB.

embedding_model string

Model name for generating embeddings. Used by the Vector DB for document chunking and search.

vector_top_k integer

Number of results to return from vector similarity searches.

Default: 5

skills_paths array of strings

Directories where FeLLAMA looks for installed skill packages.

Default: ["~/.fellama/skills/"]

URL Rules

The file ~/.fellama/url_rules.toml controls how the SmartWeb agent classifies URLs during web research. Sources are ranked by tier for prioritization and trust.

~/.fellama/url_rules.toml

# Tier 1: Authoritative sources — prioritized and trusted
[tier1]
patterns = [
    "docs\\.python\\.org",
    "developer\\.mozilla\\.org",
    "\\.gov$",
    "arxiv\\.org"
]

# Tier 2: Community sources — useful but lower trust
[tier2]
patterns = [
    "stackoverflow\\.com",
    "medium\\.com",
    "dev\\.to"
]

# Blacklisted: Never visit these domains
[blacklisted]
patterns = [
    "malware-site\\.com"
]

Tier	Behavior
`tier1`	Authoritative sources. Prioritized in research, content given higher weight in corroboration.
`tier2`	Community sources. Used for supplementary information and cross-referencing.
`tier3`	Everything else (implicit). Visited but treated with standard weight.
`blacklisted`	Never visited. The agent skips these URLs entirely.

Patterns are Rust regular expressions matched against the full URL domain.

CLI Arguments

All FeLLAMA binaries follow a standard argument contract. Same name always means the same thing across all agents and tools.

Argument	Type	Description
`--request <TEXT>`	string	Inline text request (primary input)
`--file <PATH>`	repeatable	Local file path input
`--url <URL>`	repeatable	Remote URL input
`--json_arg <JSON>`	string	JSON object map of all arguments (machine-to-machine)
`--human`	flag	Pretty-print JSON output for terminal reading
`--model <MODEL>`	string	LLM model override (agents only)
`--endpoint <URL>`	string	LLM endpoint override (agents only)
`--task-id <UUID>`	string	Caller-assigned task UUID, echoed in response
`--parent-id <UUID>`	string	Parent task UUID for tracing nested calls
`--output-dir <DIR>`	string	Directory for multi-file output
`--output-format <FMT>`	enum	Output format: `csv`, `markdown`, `json`, `plaintext`
`--max-turns <N>`	integer	Maximum LLM turns in an agentic loop
`--dry-run`	flag	Validate and plan without side effects
`--session-root <DIR>`	string	Base directory for session dirs (default: `~/.fellama`)

Note: --endpoint and --model are only available on agent binaries (not extractors like fellama-pdf-extractor). Passing them to tool binaries causes exit code 2.

Environment Variables

Variable	Purpose	Precedence
`AGENT_MODEL`	Default model name for all agents	Below CLI args and config.toml

Environment variables are lower priority than both CLI arguments and config.toml settings. They serve as a fallback when neither is specified.

LLM Context Resolution

FeLLAMA resolves the LLM endpoint and model through a priority chain. The first source that provides a value wins.

1

CLI Arguments — --endpoint, --model

2

Config File — ~/.fellama/config.toml

3

Environment — AGENT_MODEL

4

Auto-Discovery — queries /v1/models endpoint

5

Compiled Defaults — hardcoded fallback values

Session Directories

FeLLAMA persists all session state to disk. The root directory is ~/.fellama/ (customizable via --session-root).

SmartWeb Agent Sessions

directory structure

~/.fellama/<session-id>/
  ├── session.log          # NDJSON turn-by-turn log
  ├── trace.log            # Full LLM I/O trace (if enabled)
  ├── MEMORY.md            # Human-readable research memory
  ├── MEMORY.1.md          # Archived memory (after compaction)
  ├── meta.json            # Token counts, status
  ├── progress.json        # Live progress snapshot
  ├── summary.json         # Final summary
  ├── output.txt           # Final output
  ├── links.txt            # Saved URLs
  ├── page_cache/          # Cached distillations
  │   ├── {hash}.json
  │   └── {hash}.raw.txt
  └── screenshots/         # Page captures
      └── turn_N.png

Skill Worker Sessions

directory structure

~/.fellama/skill-worker/<session-id>/
  ├── session.log          # NDJSON lifecycle log
  ├── trace.log            # Full trace (if enabled)
  ├── MEMORY.md            # Session narrative
  ├── progress.json        # Phase/turn snapshot
  ├── meta.json            # Token counts & status
  ├── result.json          # Canonical result payload
  ├── summary.json         # Final summary
  ├── output.txt           # Rendered output
  └── evaluated_risk.txt   # Safety evaluation result

Resume & Replay: Use --resume <session-dir> to continue interrupted work. Use --replay <session-dir> --turn N to re-run a specific LLM call for debugging.

Skills

Agent Skills are self-contained packages that extend FeLLAMA's capabilities. They are installed to ~/.fellama/skills/ and tracked in skills-lock.json.

Installing Skills

bash

cargo run --release --bin fellama-install-skill -- \
  --request "install from github:user/repo"

Skills Lock File

The skills-lock.json file tracks installed skills with integrity checksums:

skills-lock.json

{
  "version": 1,
  "skills": {
    "Skill Name": {
      "source": "user/repo",
      "sourceType": "github",
      "computedHash": "8d262f08..."
    }
  }
}